557892
research-article2014
MCRXXX10.1177/1077558714557892Medical Care Research and ReviewAbraham et al.
Empirical Research
What Is the Cost of Quality for Diabetes Care?
Medical Care Research and Review 2014, Vol. 71(6) 580–598 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077558714557892 mcr.sagepub.com
Jean M. Abraham1, Daniel J. Crespin1, Jeffrey S. McCullough1, and Jon B. Christianson1
Abstract Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector performance improvement initiatives. Using a unique panel data set for a commercially insured population and multivariate regression analysis, this study examines the relationship between medical care spending and diabetesrelated quality measures, including provider-initiated processes of care and patientdependent quality activities. Empirical evidence generated from this analysis of the relationship between a comprehensive set of diabetes quality measures and diabetesrelated spending does not lend support for the assumption that high-quality preventive and primary care combined with effective patient self-management can lead to lower costs in the near term. Finally, we find no relationship between adjusted spending and intermediate clinical outcomes (e.g., HbA1c level) measured at the clinic level. Keywords diabetes, cost, quality, disease management, employer-based population
Introduction Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector initiatives focused on U.S. health system performance improvement. Over the past decade, purchasers have pursued multiple strategies to improve value and efficiency with respect to care delivery for their enrollee populations. These approaches have included provider-focused pay-for-performance This article, submitted to Medical Care Research and Review on December 20, 2013, was revised and accepted for publication on September 17, 2014. 1University
of Minnesota, Minneapolis, MN, USA
Corresponding Author: Jean M. Abraham, Division of Health Policy and Management, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN 55455, USA. Email: [email protected]
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programs, public reporting of providers’ performances on clinical quality and/or resource use, and new benefit designs that structure provider networks based on performance (e.g., tiered or narrow networks; Fronstin, 2003; Trude, Au, & Christianson, 2006). Purchasers also have employed patient-focused strategies, such as coaching programs, to educate enrollees with chronic illnesses. These programs may help patients modify their lifestyles and become better informed consumers of medical care (Terry, Grossmeier, Mangen, & Gingerich, 2013). Underlying many performance improvement initiatives is the assumption that high-quality preventive and primary care combined with effective patient self-management leads to better clinical outcomes and lower costs. However, the empirical evidence on this relationship is weak. We employ a unique panel data set of a large employer-based population to examine the relationship between the cost of diabetes care and medical quality. In particular, we use a comprehensive set of provider-focused (e.g., testing and prescribing) and patient-focused (e.g., medication adherence and disease management participation) indicators to investigate costs or savings associated with specific quality measures.
Background Prior Research Hussey, Wertheimer, and Mehrotra (2013) reviewed the empirical literature from 1990 to 2012 regarding the relationship between the cost and quality of medical care. The authors considered 61 studies that examined cost and quality associations across geographic markets, organizations (e.g., hospitals and nursing homes), and individuals with specific attributes (e.g., presence of chronic conditions). They assessed how these associations vary across different metrics of quality (e.g., outcome vs. process measures) and cost (e.g., charges, plan payments, organizational costs, or resource use). Their review suggests that the evidence is quite mixed as to whether higher quality is associated with higher cost, no difference, or lower cost. The findings of Hussey et al. (2013) identify a combination of estimation and measurement issues encountered by researchers in attempts to establish a cost–quality relationship. Notably, the authors report a strong potential for omitted variables bias, whereby factors such as health status or preferences of patients and/or providers may be unobserved by the researcher, but exert influence on both the quality and cost of care received. This issue is particularly problematic for studies that have relied on cross-sectional data because they may be unable to entirely account for preexisting differences in health status, quality of care, and spending, thus leading to biased estimates. Also, the cost–quality relationship may differ depending on whether researchers take a short-run versus long-run perspective. This distinction is essential because providers may initially increase their intensity of care, which would lead to higher costs, in order to achieve long-run quality improvements. These initial increases in intensity may result in better management and less need for future use of acute care, thus contributing to lower long-run costs. Finally, the cost–quality relationship may
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vary according to whose perspective is taken (e.g., patient or health plan) or the specific medical condition(s) being analyzed. A substantial portion of the empirical work on the cost–quality relationship to date has focused on diabetes. Diabetes currently affects 18.8 million individuals in the United States, with an aggregate estimated annual cost of $176 billion in 2012 (American Diabetes Association, 2013). The prevalence of diabetes and its associated costs are expected to grow as the overall population in the United States ages. Consequently, understanding how changes in diabetes care quality influence costs not only is important for purchasers now but also will continue to remain so in the future (Huang, Basu, O’Grady, & Capretta, 2009). Within the diabetes cost–quality literature, several different study designs have been employed, including observational and simulation approaches. Of observational studies, some have focused on the relationship between costs- and provider-initiated processes of care (e.g., annual testing for nephropathy and cholesterol) and found mixed evidence of an association (Kralewski, Dowd, & Xu, 2012; Solberg, Lyles, Shore, Lemke, & Weiner, 2002). Other observational studies have used measures of clinical outcomes (e.g., glycemic control; Gilmer et al., 2005; Wagner et al., 2001) or activities that are “patient-dependent,” such as glycemic control medication adherence (Jha, Aubert, Yao, Teagarden, & Epstein, 2012) and participation in a disease management program (Sidorov et al., 2002). These studies have found that better performance on these clinical outcomes or quality-related activities is associated with lower costs or utilization (e.g., hospitalization or emergency department visits). Simulation also has been employed to examine the cost–quality relationship. For example, Nuckols et al. (2011) used medical record data and simulation methods to estimate the incremental per-patient cost and cost-effectiveness of consistently providing basic glucose management to U.S. adults with type 2 diabetes using a 1 year time horizon and payer perspective. Simulating an “improved care” scenario of 100% adherence to care processes, the authors found spending would increase by $327 per diabetic, mostly due to increased use of antihyperglycemic medications.
New Contribution This study advances the literature on the cost–quality relationship for diabetes care in several ways. First, we consider a broad range of quality measures and activities, allowing us to infer a more complete cost–quality relationship for diabetes relative to studies that have focused on a single or more limited set of factors. In particular, our measures encompass both provider-initiated processes of care and patient-dependent quality activities (e.g., outpatient visits, medication adherence, and disease management participation). Second, the inclusion of disease management participation allows us to test for any moderating effects of self-care management on activities that may affect medical care consumption. Third, we investigate how changes in quality improvement affect spending on both ambulatory services (e.g., outpatient visits and prescription drugs) and hospital-based services, including emergency department (ED) visits and inpatient stays. This separation permits examination of whether
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changes in quality affect spending differently for one type of care versus another. Last, we explore the association between spending, adjusted for receipt of processes of care and quality-related activities, on a composite measure of clinic-level quality that documents improvements in intermediate clinical outcomes for diabetic patients.
Conceptual Framework Figure 1 provides a conceptual framework for relating how provider and patient attributes and actions influence patient behaviors, clinical outcomes, utilization, and spending. Provider characteristics (e.g., physician training, clinic size) are expected to influence adherence to diabetes treatment guidelines, which may influence intermediate clinical outcomes (e.g., HbA1c levels as well as cholesterol and blood pressure). Providers have considerable oversight regarding the intensity of treatment (e.g., office visit frequency and prescribed medicines) they recommend to diabetic patients. Optimally, providers follow evidence-based guidelines for routine care that recommend annual tests to evaluate HbA1c and low-density lipoprotein (LDL) cholesterol levels in addition to eye examinations to check for retinopathy and nephropathy examinations to screen for and treat kidney damage caused by high blood sugar. At the clinic level, practices may invest in disease registries or other processes to increase rates of recommended testing among their patient population. These investments are likely to be more prevalent among larger clinics, which can spread fixed costs over a larger number of patients. These efforts may increase ambulatory and pharmaceutical costs but are intended to prevent emergency and inpatient costs. Clearly, patients also play an important role in managing their diabetes. Each year, for example, it is recommended that patients initiate a provider visit to evaluate how well their condition is managed through lifestyle changes and/or medication use (National Committee for Quality Assurance, 2008). Thus, compliance with recommendations may be associated with increased use of outpatient care. While providers may prescribe medicines for patients to manage their condition, patients are ultimately responsible for taking them. We hypothesize that patients’ ability to follow recommendations may be related to both sociodemographic characteristics as well as illness severity. Both provider actions and patient attributes are hypothesized to affect intermediate clinical outcomes, such as HbA1c levels and cholesterol. Poor control of these intermediate outcomes may increase individuals’ risk for diabetes complications, including blindness, leg amputation, renal disease, and heart attack. In turn, poor control may lead to increased utilization of health care services (e.g., ED and inpatient hospitalization) and spending.
Method Data Sources Our primary data source was the University of Minnesota’s “UPlan” self-insured medical program for calendar years 2006 to 2009. These data include information on eligibility, medical claims, and disease management program participation for approximately
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Provider Characteriscs Training and Experience Structural Aributes Available Financial Resources
Managementrelated ulizaon
Paent Characteriscs Socio-demographic Illness Severity
Adherence to diabetes treatment guidelines
Parcipaon in diabetes management program
Intermediate Outcomes • HbA1c • Blood Pressure • Cholesterol
Complicaons • Blindness • Leg Amputaon • Heart Aack • Renal Disease Complicaons-Related Healthcare Ulizaon • ED visits, Hospitalizaon
Economic Outcomes Cost of guideline adherence and diabetes management parcipaon minus savings from health improvements
Figure 1. Conceptual framework.
41,000 covered lives annually. While the university offers eligible employees a choice of plans and cost-sharing provisions, covered services are standardized. Additionally, we used publicly reported clinic-level measures of quality from Minnesota Community Measurement (MNCM, http://mncm.org/).
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Study Population We identified UPlan enrollees ages 18 to 75 years with a diagnosis of diabetes from our administrative data using Healthcare Effectiveness Data and Information Set (HEDIS) criteria.1 We included enrollees if they met any of the following criteria: (a) Two visits with a diagnosis of diabetes on different dates of services in an outpatient or nonacute inpatient setting, (b) one visit with a diagnosis of diabetes in an acute inpatient or ED setting, or (c) one prescription drug claim for insulin or oral hypoglycemic/antihyperglycemic medication. We identified diabetes diagnoses using IDC9-CM diagnosis codes in the principal or secondary diagnosis field. Using these criteria, we identified 1,679 unique enrollees with diabetes over the 2006 to 2009 period. Within this sample, 384 enrollees were observed for 1 year, 349 for 2 years, 317 for 3 years, and 629 for all 4 years.
Measure Definitions Annual Diabetes-Related Spending. We constructed our spending measures by first identifying all claims for each enrollee with a primary or secondary diagnosis of IDC9-CM diagnosis code or a prescription for insulin or oral hypoglycemic/antihyperglycemic medication. We then separately aggregated plan-paid spending amounts for ambulatory care and prescription drugs (AMB/RX) and emergency department visits and inpatient hospitalizations (ED/IP). All plan-paid spending measures were inflation-adjusted to 2009 dollars. Provider-Initiated Quality. Evidence-based guidelines for routine diabetes care included annual tests to evaluate HbA1c and LDL cholesterol levels in addition to eye examinations to check for retinopathy and nephropathy examinations to screen for and treat kidney damage caused by high blood sugar. We chose measures that are HEDIS-specific, endorsed by the National Quality Forum, and continued to be used for public reporting activities. From the claims data, we constructed binary indicators corresponding to whether an enrollee had the following tests on an annual basis: an HbA1c test, an LDL screening, and a medical attention for nephropathy. Because almost every diabetic enrollee in the sample had a retinal eye exam, we excluded this process measure. For many patients, providers prescribe oral medications or insulin to control blood sugar and/or cholesterol-lowering medications. To account for this providerinitiated activity, we included three indicators for whether an enrollee had filled a prescription for insulin, antidiabetic, or cholesterol-reducing medications. This could, of course, also be considered a patient-initiated activity because the patient has to decide to fill the prescription. Patient-Dependent Quality Activities. Since it is recommended that patients initiate a provider visit to evaluate how well their condition is managed through lifestyle changes and/or medication use, we included a measure of enrollees’ annual number of outpatient visits for diabetes. Additionally, medication adherence is inversely related to an
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individual’s likelihood of inpatient hospitalization or emergency room use (Jha et al., 2012; Salas, Hughes, Zuluaga, Vardeva, & Lebmeier, 2009). We constructed medication adherence measures that interact with the prescription indicators described above. For enrollees with a filled prescription, we calculated a medication possession ratio equal to the number of days supplied of that medication in a year divided by the total days of the year. We then defined binary indicators of adherence if an enrollee received a fill for the medication type and the enrollee’s MPR was greater than 0.80—a standard cutoff described in the adherence literature (Benner et al., 2002; Bramley, Gerbino, Nightengale, & Frech-Tamas, 2006; Granger et al., 2005; Karaca-Mandic, Swenson, Abraham, & Kane, 2013). Patients in our study had the option of participating in a diabetes management program administered by a wellness program vendor for the UPlan. We defined a binary indicator for whether or not a diabetic enrollee participated in the UPlan disease management program in a given year and 0 if not. Through education and telephonic coaching, enrollees may learn to better manage their condition and to engage in quality-related activities, such as adhering to their medications. Because we expect the benefits of coaching to persist over time, we maintained the binary indicator as being “turned on” for years subsequent to the initial year of participation. In the empirical analysis, we considered both a “main” effect of participation and then estimated a specification to test for moderating effects of participation on the relationship between patient-dependent quality activities and spending. Other Patient, Provider, and Clinic Attributes. We also included an enrollee’s age, sex, diabetes type (e.g., type 1), and health status. Because an individual’s health status is likely to be associated with both the quantity and intensity of medical care consumed as well as spending, we use a publicly available, risk-adjustment algorithm designed by the University of California-San Diego for the Chronic Illness and Disability Payment System (http://cdps.ucsd.edu/) to control for health status. This software uses a patient’s diagnoses and prescription drug utilization to identify specific health conditions from 20 major disease categories in order to create a risk score. We normalized risk scores so that the mean equals one. We controlled for several clinic-level characteristics that may affect providers’ investments in diabetes-related quality improvement initiatives. To code these variables, we attributed each patient in our data to a clinic on an annual basis. First, we attributed patients to their assigned primary care clinic from their health insurance plan in our administrative data when available. This specifically affects enrollees in the HealthPartners health plan offered during open enrollment. If no primary care clinic was specified, we assigned the enrollee to the clinic with the plurality of diabetes outpatient visits. In the case of ties (12% of enrollees), we assign an enrollee to the clinic with the most plan payments for diabetes outpatient visits. First, we included in our models the number of UPlan diabetic enrollees seen at the clinic during the year. Second, we included the total number of diabetics within the age group 18 to 75 years old across all purchasers, who were seen by the clinic
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as reported by MNCM. In our data, 1,338 person-years (29% of all person-years) were assigned to a nonreporting clinic. Because the number of all-payer diabetics was not available in our data for these patients, we instead used the annual average number of diabetics seen at clinics reporting to MNCM. Third, we included an indicator variable for whether an enrollee’s assigned clinic was affiliated with HealthPartners, since this organization has a national reputation for developing system-wide initiatives to improve chronic disease management (Beaulieu, Cutler, Ho, Horrigan, & Isham, 2003). Finally, because spending is a function of both the quantity and intensity of services and the unit prices of those services, it is important to account for differences across providers in unit prices resulting from contract negotiations. To achieve this aim, we constructed a “price index” for each clinic that equals the average payment for prevalent outpatient visits and included it as a control in the model.2
Empirical Specification To examine the effect of patient-dependent quality activities (Patient) and providerinitiated quality (Provider) on the ith enrollee’s annual diabetes spending (S) in time t, we specified the following regression model: Sit = α + Patient it β + Providerit γ + X it δ + θ j + τt + o` it , where {β,γ,δ} are parameters to be estimated, and ò is an error term. Additionally, the model includes a set of controls (X), as well as clinic- and time-specific fixed effects, θ j and τt , respectively. The clinic fixed effect captures unobserved clinic-specific quality, while the time effect captures across-time changes in quality or measures that are common across clinics. These fixed effects mitigate bias from omitted variables to identify the true relationship between quality and costs. In this context, the β and γ parameters may be interpreted as the change in spending with the change in a quality metric, while δ describes the contribution of control variables. We use generalized linear models assuming a log link and Poisson family distribution to estimate the relationship between our quality measures and activities with AMB/RX spending. The assumption of a Poisson family distribution was based on results from the Park test, as specified by Manning and Mullahy (2001). We separately estimate ED/IP spending using a two-part model. The first part models the probability of any ED/IP spending using a binary probit, while the second part models the level of spending conditional on a positive value. We estimated the second equation via generalized linear model with a log link and gamma family distribution. For the continuous explanatory variables in all models (e.g., number of outpatient visits), we take a log transformation to address skewness and improve fit.3 Since we observe multiple observations of enrollees over time, we use Huber-White robust standard errors clustered by enrollee to correct for autocorrelation.
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Results Descriptive Statistics Table 1 reports descriptive statistics. The average age of diabetic enrollees within the UPlan population is 54 years and 45% are female. Compared with national estimates from the 2008-2010 Medical Expenditure Panel Survey of individuals 18 to 75 years old with diabetes, the UPlan population is younger and less likely to be female.4 Twenty percent has type 1 diabetes. Across the study period, average total diabetesrelated spending was $3,408. AMB/RX spending constitutes two thirds of diabetesrelated spending on average ($2,389). Approximately 14% of enrollees had at least one ED visit or inpatient stay over the time period. Unconditional average spending on ED/IP care was $1,018. For the HEDIS measures, 80% of enrollees received an annual HbA1c test, 72% a cholesterol screening, and 86% medical attention for nephropathy. Twenty-seven percent of diabetic enrollees participated in the UPlan’s disease management program.
Multivariate Analysis Results Table 2 reports marginal effects and standard errors for models corresponding to AMB/RX spending. Column 1 reports our baseline model, and Column 2 includes a set of interaction terms between disease management participation and patientdependent quality activities to test for moderating effects of self-care management that may influence medical care consumption. Among the set of provider-initiated quality measures, we find positive and significant effects on two process-of-care measures: receipt of an HbA1c test and medical attention for nephropathy. The marginal effect on medical attention for nephropathy suggests that its receipt is associated with $303 higher annual spending. Enrollees on a prescribed medicine regime for diabetes management had higher annual spending. For example, an enrollee who filled an insulin prescription and adhered to it throughout the year had $2,031 (= $1,733 + $298) higher AMB/RX spending. Spending increased by $840 for enrollees on hypoglycemia medications. Unsurprisingly, AMB/ RX spending is related to an enrollee’s diabetes outpatient visits. These results suggest that each additional visit is associated with a $270 increase in diabetes-related spending. Participants in the UPlan’s disease management program have $174 higher average AMB/RX spending than nonparticipants, although the effect is only marginally significant (p = .052). Column 2 tests for moderating effects of disease management participation on the cost–quality relationship. None of the interaction terms are statistically significant, and we conclude that disease management program participation in this population does not appear to moderate the cost–quality relationship. We observe modest differences in AMB/RX spending by age. Each additional year of age is associated with −$11 in AMB/RX spending. There was no difference by
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Table 1. Mean Patient-Level Descriptive Statistics for UPlan Enrollees With Diabetes, 20062009. Diabetes-Related Provider Plan Payments (2009 $) Total Ambulatory and prescription drug Inpatient and emergency department Provider-Initiated Quality Measures Process measures Received HbA1c test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Optimal diabetes care composite measure at assigned clinic Patient-Initiated Quality Measures Medical utilization # Diabetes-related outpatient visits Filled a prescription for insulin Filled a prescription for hypoglycemia Filled a prescription for cholesterol Medication adherence (conditional on having a prescription) Insulin Hypoglycemic Rx Cholesterol Rx Disease management (DM) program participation Other Patient and Provider Attributes Age Female Risk score Type 1 diabetes Not assigned to a MNCM reporting clinic # UPlan diabetics at assigned clinic # All-payer diabetics at assigned clinic Attended HealthPartners clinic Price index at assigned clinic Year 2006 2007 2008 2009
3,408 (9,804) 2,389 (2,995) 1,018 (8,771)
0.80 0.72 0.86 0.21 (0.09)
3.76 (3.05) 0.34 0.42 0.66 0.42 0.67 0.70 0.27 54.03 (10.61) 0.45 1.00 (1.00) 0.20 0.29 52.53 (75.02) 765.09 (451.84) 0.32 99.94 (20.74) 0.22 0.24 0.26 0.27
Note. MNCM = Minnesota Community Measurement. Data includes 4,849 person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinic-specific variables. Standard Deviations for continuous variable in parentheses.
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Table 2. Marginal Effects for Ambulatory and Rx Diabetes-Related Plan Payments for UPlan Enrollees With Diabetes, 2006-2009.
Provider-Initiated Quality Measures Process measures Received HbA1c Test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Filled a prescription for insulin Filled a prescription for hypoglycemia Rx Filled a prescription for cholesterol Rx Patient-Initiated Quality Measures Medical utilization ln(# Diabetes-related outpatient visits) Medication adherence Insulin Hypoglycemia Rx Cholesterol Rx Disease management (DM) program participation DM program participation interactions DM * ln(# Diabetes-related outpatient visits) DM * Filled a prescription for insulin DM * Filled a prescription for hypoglycemia Rx DM * Filled a prescription for cholesterol RX DM * Adherence to insulin DM * Adherence to hypoglycemia Rx DM * Adherence to cholesterol RX Other Patient and Provider Attributes ln(Age) Female ln(Risk scores) Type 1 diabetes Not assigned to MNCM reporting clinic ln(# UPlan diabetics at assigned clinic) ln(# All-payer diabetics as assigned clinic) Assigned to HealthPartners clinic ln(Price index at assigned clinic) Time Trend 2007 2008 2009 Clinic fixed effects Observations
Base
Moderating effects
(1)
(2)
284* (143) −110 (125) 303* (123) 1,733** (106) 402** (108) 176 (133)
284* (144) −112 (125) 291* (123) 1,727** (122) 356** (134) 212 (174)
1,016** (71)
1,028** (90)
298** (103) 438** (112) 259* (106) 174 (89)
236 (131) 473** (131) 343** (129) 381 (258) −52 (155) 23 (194) 136 (221) −128 (252) 187 (212) −120 (233) −255 (219)
−594** (214) −9 (85) 456** (101) 699** (102) −204 (125) −9 (122) −18 (87) −318 (1541) 404 (230)
−621** (215) −12 (85) 456** (103) 694** (103) −200 (126) −8 (123) −10 (87) −522 (1554) 395 (230)
198* (88) 347** (114) 559** (120) Y 4,849
200* (89) 344** (115) 555** (121) Y 4,849
Note. MNCM = Minnesota Community Measurement. Data includes person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinic-specific variables. All Ambulatory and Rx diabetes-related plan payments models are generalized linear models using a log link function and a Poisson family function. Huber-White robust standard errors clustered by patient in parentheses. **p < .01. *p < .05.
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gender. We find that people with type 1 diabetes have $699 higher spending relative to type 2 diabetics after controlling for differences in treatment patterns. Table 3 provides marginal effects and standard errors for our baseline and moderating effects ED/IP diabetes spending models. Part 1 corresponds to the probability of positive ED/IP spending, while Part 2 reports results for the level of ED/IP spending conditional on use. Very few quality measures predict an enrollee’s probability of having any ED/IP spending. Exceptions include filling a prescription for hypoglycemia (0.034) and the number of diabetes-related outpatient visits. For ED/IP spending conditional on use (Part 2), the only significant quality measure is disease management participation. Enrollees who participate have $6,745 higher average ED/IP spending.
Sensitivity Checks We conducted five sensitivity checks. First, we investigated whether confounding factors affect both the decision to participate in disease management as well as ED/IP spending. For example, if an enrollee has an unobserved health shock that leads to an ED visit and a diagnosis that triggers an invitation to participate in the program, then the association between disease management and ED/IP spending in our base model is likely biased upward. To check for this occurrence, we reestimated the baseline model excluding enrollees’ first observation if their initial year of data included positive ED/ IP spending. This exclusion criterion affects 183 enrollee-year observations. We found no significant difference in the relation between disease management and ED/IP spending between our base model and our model excluding enrollees with first year ED/IP spending. Second, we tested the sensitivity of our results to the presence of outliers. We constructed a trimmed sample by removing observations in the bottom 1% and top 1% of the diabetes spending distribution. In the AMB/RX spending models, we observe slightly attenuated magnitudes for our quality measures for medical attention for nephropathy, outpatient visits, and prescribed medication use. For the ED/IP models, trimming was applied only to the subsample with positive spending (e.g., observations from the upper 1% of the spending distribution were removed). With the trimmed sample, we found the marginal effect of disease management participation on ED/IP spending is smaller, suggesting the potential influence of outliers Third, we reestimated the models to account for the possibility of a lag between the quality of care received and spending. We used a one-period lag, whereby quality in time t − 1 is hypothesized to affect spending in time t. For AMB/RX spending, most of the statistically significant relationships between specific quality measures and spending persist, although the effect of HbA1c testing is no longer significant. In the ED/IP models, we continue to find few explanatory variables that predict either an enrollee’s likelihood of having any ED/IP spending or level of spending. Although we find no difference between disease management participants and nonparticipants in the probability of having any ED/IP spending, participants continue to have higher levels of ED/IP spending conditional on use.
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−1,422 (2,872) 1,357 (1,677) 1,646 (2,201) 1,068 (1,764) −1,265 (1,710) −1,267 (1,807)
38 (1,122) −1,772 (1,668) −1,162 (2,028) 49 (2,238) 6,745** (1,957)
−0.006 (0.017) 0.021 (0.014) 0.034* (0.014) −0.013 (0.014)
0.031** (0.008) −0.006 (0.015) −0.023 (0.016) −0.016 (0.013) 0.012 (0.012)
(2)
Part 2
0.028 (0.018) −0.024 (0.015)
(1)
Provider-Initiated Quality Measures Process measures Received HbA1c test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Filled a prescription for insulin Filled a prescription for hypoglycemia Rx Filled a prescription for cholesterol Rx Patient-Initiated Quality Measures Medical utilization ln(# Diabetes-related outpatient visits) Medication adherence Insulin Hypoglycemia Rx Cholesterol Rx Disease management (DM) program participation DM program participation interactions DM * ln(# Diabetes-related outpatient visits) DM * Filled a prescription for insulin DM * Filled a prescription for hypoglycemia Rx DM * Filled a prescription for cholesterol RX DM * Adherence to insulin
Part 1
Base model
−1,341 (2,139) 5,190 (3,592) 9,741* (4,540) −3,794 (3,814) −4,969 (4,045)
−0.002 (0.016) 0.006 (0.028) −0.003 (0.031) 0.053 (0.031) −0.01 (0.032)
(continued)
315 (2,055) 2,272 (2,570) −2,846 (2,770) 3,588 (3,361)
610 (1,314)
2,054 (2,366) −1,144 (2,104) −5,078* (2,525) −239 (2,257)
−1,285 (2,957) 832 (1,727)
(4)
Part 2
−0.003 (0.017) −0.032 (0.019) −0.012 (0.016) −0.023 (0.031)
0.032** (0.009)
−0.005 (0.017) 0.019 (0.016) 0.035* (0.017) −0.028 (0.017)
0.028 (0.018) −0.024 (0.015)
(3)
Part 1
Moderating effects
Table 3. Marginal Effects for Inpatient and ED Diabetes-Related Plan Payments for UPlan Enrollees With Diabetes, 2006-2009.
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−0.006 (0.014) 0.01 (0.016) 0.002 (0.016) N 4,849
−0.084** (0.026) −0.01 (0.010) 0.151** (0.007) −0.012 (0.016) −0.002 (0.013) −0.008* (0.004) 0.002 (0.009) −0.006 (0.014) −0.011 (0.027)
0.026 (0.035) −0.012 (0.029)
(3)
Part 1
−4,027 (2,367) −983 (2,687) −894 (2,709) Y 655
−2,864 (3,715) 1,514 (1,362) 6,716** (1,712) 1,939 (2,144) −3,382 (2,956) −1,728 (2,401) −314 (4,088) −2,737 (8,997) 5,209 (5,009)
−8,535 (4,967) 9,563* (4,468)
(4)
Part 2
Moderating effects
Note. ED = emergency department. MNCM = Minnesota Community Measurement. Data includes person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinicspecific variables. The probability of having Inpatient or ED plan payments was modeled using probits. Inpatient and ED diabetes-related plan payments models are generalized linear models using a log link function and a gamma family function. Huber-White robust standard errors clustered by patient are in parentheses. **p < .01. *p < .05.
−4,314 (2,206) −1,878 (2,527) −1,625 (2,647) Y 655
−0.005 (0.014) 0.01 (0.016) 0.002 (0.016) N 4,849
(2)
Part 2
−2,993 (3,661) 1,104 (1,314) 6,547** (1,503) 1,620 (1,994) −2,918 (2,969) −1,258 (2,251) 16 (4,187) −2,614 (8,688) 1,882 (4,538)
Base model
−0.088** (0.026) −0.01 (0.010) 0.151** (0.007) −0.012 (0.016) −0.002 (0.013) −0.008* (0.004) 0.002 (0.009) −0.006 (0.014) −0.012 (0.027)
(1)
DM * Adherence to hypoglycemia Rx DM * Adherence to cholesterol RX Other Patient and Provider Attributes ln(Age) Female ln(Risk scores) Type 1 diabetes Not assigned to MNCM reporting clinic ln(# UPlan diabetics at assigned clinic) ln(# All-payer diabetics as assigned clinic) Assigned to HealthPartners clinic ln(Price index at assigned clinic) Time Trend 2007 2008 2009 Clinic fixed effects Observations
Part 1
Table 3. (continued)
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Fourth, we estimated the models including an interaction term for type 2 diabetes and filling a prescription for insulin to capture diabetes-specific severity in addition to the risk score. The inclusion of this term did not alter the estimated relationships reported within the baseline spending model results. Fifth, we reestimated the models using total spending rather than diabetes-specific spending. Although the general pattern of results is maintained, there is greater variation in total spending across individuals, leading to a lower signal-to-noise ratio and less precise estimates.
Are Quality Investments Related to Better Clinical Outcomes? We examined whether spending related to improvements in provider-initiated care processes and patient-related quality activities is associated with intermediate clinical outcomes measured at the clinic level. Specifically, we used the Optimal Diabetes Care (ODC) composite score publicly reported on an annual basis by MNCM to capture clinics’ overall record of diabetes-related intermediate clinical and behavioral outcomes. The ODC score measures the proportion of patients within a clinic with diabetes and of ages 18 to 75 years that attain all of the following treatment goals: (1) HbA1c level of
research-article2014
MCRXXX10.1177/1077558714557892Medical Care Research and ReviewAbraham et al.
Empirical Research
What Is the Cost of Quality for Diabetes Care?
Medical Care Research and Review 2014, Vol. 71(6) 580–598 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077558714557892 mcr.sagepub.com
Jean M. Abraham1, Daniel J. Crespin1, Jeffrey S. McCullough1, and Jon B. Christianson1
Abstract Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector performance improvement initiatives. Using a unique panel data set for a commercially insured population and multivariate regression analysis, this study examines the relationship between medical care spending and diabetesrelated quality measures, including provider-initiated processes of care and patientdependent quality activities. Empirical evidence generated from this analysis of the relationship between a comprehensive set of diabetes quality measures and diabetesrelated spending does not lend support for the assumption that high-quality preventive and primary care combined with effective patient self-management can lead to lower costs in the near term. Finally, we find no relationship between adjusted spending and intermediate clinical outcomes (e.g., HbA1c level) measured at the clinic level. Keywords diabetes, cost, quality, disease management, employer-based population
Introduction Increasing the quality of care and reducing cost growth are core objectives of numerous private- and public-sector initiatives focused on U.S. health system performance improvement. Over the past decade, purchasers have pursued multiple strategies to improve value and efficiency with respect to care delivery for their enrollee populations. These approaches have included provider-focused pay-for-performance This article, submitted to Medical Care Research and Review on December 20, 2013, was revised and accepted for publication on September 17, 2014. 1University
of Minnesota, Minneapolis, MN, USA
Corresponding Author: Jean M. Abraham, Division of Health Policy and Management, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN 55455, USA. Email: [email protected]
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programs, public reporting of providers’ performances on clinical quality and/or resource use, and new benefit designs that structure provider networks based on performance (e.g., tiered or narrow networks; Fronstin, 2003; Trude, Au, & Christianson, 2006). Purchasers also have employed patient-focused strategies, such as coaching programs, to educate enrollees with chronic illnesses. These programs may help patients modify their lifestyles and become better informed consumers of medical care (Terry, Grossmeier, Mangen, & Gingerich, 2013). Underlying many performance improvement initiatives is the assumption that high-quality preventive and primary care combined with effective patient self-management leads to better clinical outcomes and lower costs. However, the empirical evidence on this relationship is weak. We employ a unique panel data set of a large employer-based population to examine the relationship between the cost of diabetes care and medical quality. In particular, we use a comprehensive set of provider-focused (e.g., testing and prescribing) and patient-focused (e.g., medication adherence and disease management participation) indicators to investigate costs or savings associated with specific quality measures.
Background Prior Research Hussey, Wertheimer, and Mehrotra (2013) reviewed the empirical literature from 1990 to 2012 regarding the relationship between the cost and quality of medical care. The authors considered 61 studies that examined cost and quality associations across geographic markets, organizations (e.g., hospitals and nursing homes), and individuals with specific attributes (e.g., presence of chronic conditions). They assessed how these associations vary across different metrics of quality (e.g., outcome vs. process measures) and cost (e.g., charges, plan payments, organizational costs, or resource use). Their review suggests that the evidence is quite mixed as to whether higher quality is associated with higher cost, no difference, or lower cost. The findings of Hussey et al. (2013) identify a combination of estimation and measurement issues encountered by researchers in attempts to establish a cost–quality relationship. Notably, the authors report a strong potential for omitted variables bias, whereby factors such as health status or preferences of patients and/or providers may be unobserved by the researcher, but exert influence on both the quality and cost of care received. This issue is particularly problematic for studies that have relied on cross-sectional data because they may be unable to entirely account for preexisting differences in health status, quality of care, and spending, thus leading to biased estimates. Also, the cost–quality relationship may differ depending on whether researchers take a short-run versus long-run perspective. This distinction is essential because providers may initially increase their intensity of care, which would lead to higher costs, in order to achieve long-run quality improvements. These initial increases in intensity may result in better management and less need for future use of acute care, thus contributing to lower long-run costs. Finally, the cost–quality relationship may
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vary according to whose perspective is taken (e.g., patient or health plan) or the specific medical condition(s) being analyzed. A substantial portion of the empirical work on the cost–quality relationship to date has focused on diabetes. Diabetes currently affects 18.8 million individuals in the United States, with an aggregate estimated annual cost of $176 billion in 2012 (American Diabetes Association, 2013). The prevalence of diabetes and its associated costs are expected to grow as the overall population in the United States ages. Consequently, understanding how changes in diabetes care quality influence costs not only is important for purchasers now but also will continue to remain so in the future (Huang, Basu, O’Grady, & Capretta, 2009). Within the diabetes cost–quality literature, several different study designs have been employed, including observational and simulation approaches. Of observational studies, some have focused on the relationship between costs- and provider-initiated processes of care (e.g., annual testing for nephropathy and cholesterol) and found mixed evidence of an association (Kralewski, Dowd, & Xu, 2012; Solberg, Lyles, Shore, Lemke, & Weiner, 2002). Other observational studies have used measures of clinical outcomes (e.g., glycemic control; Gilmer et al., 2005; Wagner et al., 2001) or activities that are “patient-dependent,” such as glycemic control medication adherence (Jha, Aubert, Yao, Teagarden, & Epstein, 2012) and participation in a disease management program (Sidorov et al., 2002). These studies have found that better performance on these clinical outcomes or quality-related activities is associated with lower costs or utilization (e.g., hospitalization or emergency department visits). Simulation also has been employed to examine the cost–quality relationship. For example, Nuckols et al. (2011) used medical record data and simulation methods to estimate the incremental per-patient cost and cost-effectiveness of consistently providing basic glucose management to U.S. adults with type 2 diabetes using a 1 year time horizon and payer perspective. Simulating an “improved care” scenario of 100% adherence to care processes, the authors found spending would increase by $327 per diabetic, mostly due to increased use of antihyperglycemic medications.
New Contribution This study advances the literature on the cost–quality relationship for diabetes care in several ways. First, we consider a broad range of quality measures and activities, allowing us to infer a more complete cost–quality relationship for diabetes relative to studies that have focused on a single or more limited set of factors. In particular, our measures encompass both provider-initiated processes of care and patient-dependent quality activities (e.g., outpatient visits, medication adherence, and disease management participation). Second, the inclusion of disease management participation allows us to test for any moderating effects of self-care management on activities that may affect medical care consumption. Third, we investigate how changes in quality improvement affect spending on both ambulatory services (e.g., outpatient visits and prescription drugs) and hospital-based services, including emergency department (ED) visits and inpatient stays. This separation permits examination of whether
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changes in quality affect spending differently for one type of care versus another. Last, we explore the association between spending, adjusted for receipt of processes of care and quality-related activities, on a composite measure of clinic-level quality that documents improvements in intermediate clinical outcomes for diabetic patients.
Conceptual Framework Figure 1 provides a conceptual framework for relating how provider and patient attributes and actions influence patient behaviors, clinical outcomes, utilization, and spending. Provider characteristics (e.g., physician training, clinic size) are expected to influence adherence to diabetes treatment guidelines, which may influence intermediate clinical outcomes (e.g., HbA1c levels as well as cholesterol and blood pressure). Providers have considerable oversight regarding the intensity of treatment (e.g., office visit frequency and prescribed medicines) they recommend to diabetic patients. Optimally, providers follow evidence-based guidelines for routine care that recommend annual tests to evaluate HbA1c and low-density lipoprotein (LDL) cholesterol levels in addition to eye examinations to check for retinopathy and nephropathy examinations to screen for and treat kidney damage caused by high blood sugar. At the clinic level, practices may invest in disease registries or other processes to increase rates of recommended testing among their patient population. These investments are likely to be more prevalent among larger clinics, which can spread fixed costs over a larger number of patients. These efforts may increase ambulatory and pharmaceutical costs but are intended to prevent emergency and inpatient costs. Clearly, patients also play an important role in managing their diabetes. Each year, for example, it is recommended that patients initiate a provider visit to evaluate how well their condition is managed through lifestyle changes and/or medication use (National Committee for Quality Assurance, 2008). Thus, compliance with recommendations may be associated with increased use of outpatient care. While providers may prescribe medicines for patients to manage their condition, patients are ultimately responsible for taking them. We hypothesize that patients’ ability to follow recommendations may be related to both sociodemographic characteristics as well as illness severity. Both provider actions and patient attributes are hypothesized to affect intermediate clinical outcomes, such as HbA1c levels and cholesterol. Poor control of these intermediate outcomes may increase individuals’ risk for diabetes complications, including blindness, leg amputation, renal disease, and heart attack. In turn, poor control may lead to increased utilization of health care services (e.g., ED and inpatient hospitalization) and spending.
Method Data Sources Our primary data source was the University of Minnesota’s “UPlan” self-insured medical program for calendar years 2006 to 2009. These data include information on eligibility, medical claims, and disease management program participation for approximately
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Provider Characteriscs Training and Experience Structural Aributes Available Financial Resources
Managementrelated ulizaon
Paent Characteriscs Socio-demographic Illness Severity
Adherence to diabetes treatment guidelines
Parcipaon in diabetes management program
Intermediate Outcomes • HbA1c • Blood Pressure • Cholesterol
Complicaons • Blindness • Leg Amputaon • Heart Aack • Renal Disease Complicaons-Related Healthcare Ulizaon • ED visits, Hospitalizaon
Economic Outcomes Cost of guideline adherence and diabetes management parcipaon minus savings from health improvements
Figure 1. Conceptual framework.
41,000 covered lives annually. While the university offers eligible employees a choice of plans and cost-sharing provisions, covered services are standardized. Additionally, we used publicly reported clinic-level measures of quality from Minnesota Community Measurement (MNCM, http://mncm.org/).
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Study Population We identified UPlan enrollees ages 18 to 75 years with a diagnosis of diabetes from our administrative data using Healthcare Effectiveness Data and Information Set (HEDIS) criteria.1 We included enrollees if they met any of the following criteria: (a) Two visits with a diagnosis of diabetes on different dates of services in an outpatient or nonacute inpatient setting, (b) one visit with a diagnosis of diabetes in an acute inpatient or ED setting, or (c) one prescription drug claim for insulin or oral hypoglycemic/antihyperglycemic medication. We identified diabetes diagnoses using IDC9-CM diagnosis codes in the principal or secondary diagnosis field. Using these criteria, we identified 1,679 unique enrollees with diabetes over the 2006 to 2009 period. Within this sample, 384 enrollees were observed for 1 year, 349 for 2 years, 317 for 3 years, and 629 for all 4 years.
Measure Definitions Annual Diabetes-Related Spending. We constructed our spending measures by first identifying all claims for each enrollee with a primary or secondary diagnosis of IDC9-CM diagnosis code or a prescription for insulin or oral hypoglycemic/antihyperglycemic medication. We then separately aggregated plan-paid spending amounts for ambulatory care and prescription drugs (AMB/RX) and emergency department visits and inpatient hospitalizations (ED/IP). All plan-paid spending measures were inflation-adjusted to 2009 dollars. Provider-Initiated Quality. Evidence-based guidelines for routine diabetes care included annual tests to evaluate HbA1c and LDL cholesterol levels in addition to eye examinations to check for retinopathy and nephropathy examinations to screen for and treat kidney damage caused by high blood sugar. We chose measures that are HEDIS-specific, endorsed by the National Quality Forum, and continued to be used for public reporting activities. From the claims data, we constructed binary indicators corresponding to whether an enrollee had the following tests on an annual basis: an HbA1c test, an LDL screening, and a medical attention for nephropathy. Because almost every diabetic enrollee in the sample had a retinal eye exam, we excluded this process measure. For many patients, providers prescribe oral medications or insulin to control blood sugar and/or cholesterol-lowering medications. To account for this providerinitiated activity, we included three indicators for whether an enrollee had filled a prescription for insulin, antidiabetic, or cholesterol-reducing medications. This could, of course, also be considered a patient-initiated activity because the patient has to decide to fill the prescription. Patient-Dependent Quality Activities. Since it is recommended that patients initiate a provider visit to evaluate how well their condition is managed through lifestyle changes and/or medication use, we included a measure of enrollees’ annual number of outpatient visits for diabetes. Additionally, medication adherence is inversely related to an
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individual’s likelihood of inpatient hospitalization or emergency room use (Jha et al., 2012; Salas, Hughes, Zuluaga, Vardeva, & Lebmeier, 2009). We constructed medication adherence measures that interact with the prescription indicators described above. For enrollees with a filled prescription, we calculated a medication possession ratio equal to the number of days supplied of that medication in a year divided by the total days of the year. We then defined binary indicators of adherence if an enrollee received a fill for the medication type and the enrollee’s MPR was greater than 0.80—a standard cutoff described in the adherence literature (Benner et al., 2002; Bramley, Gerbino, Nightengale, & Frech-Tamas, 2006; Granger et al., 2005; Karaca-Mandic, Swenson, Abraham, & Kane, 2013). Patients in our study had the option of participating in a diabetes management program administered by a wellness program vendor for the UPlan. We defined a binary indicator for whether or not a diabetic enrollee participated in the UPlan disease management program in a given year and 0 if not. Through education and telephonic coaching, enrollees may learn to better manage their condition and to engage in quality-related activities, such as adhering to their medications. Because we expect the benefits of coaching to persist over time, we maintained the binary indicator as being “turned on” for years subsequent to the initial year of participation. In the empirical analysis, we considered both a “main” effect of participation and then estimated a specification to test for moderating effects of participation on the relationship between patient-dependent quality activities and spending. Other Patient, Provider, and Clinic Attributes. We also included an enrollee’s age, sex, diabetes type (e.g., type 1), and health status. Because an individual’s health status is likely to be associated with both the quantity and intensity of medical care consumed as well as spending, we use a publicly available, risk-adjustment algorithm designed by the University of California-San Diego for the Chronic Illness and Disability Payment System (http://cdps.ucsd.edu/) to control for health status. This software uses a patient’s diagnoses and prescription drug utilization to identify specific health conditions from 20 major disease categories in order to create a risk score. We normalized risk scores so that the mean equals one. We controlled for several clinic-level characteristics that may affect providers’ investments in diabetes-related quality improvement initiatives. To code these variables, we attributed each patient in our data to a clinic on an annual basis. First, we attributed patients to their assigned primary care clinic from their health insurance plan in our administrative data when available. This specifically affects enrollees in the HealthPartners health plan offered during open enrollment. If no primary care clinic was specified, we assigned the enrollee to the clinic with the plurality of diabetes outpatient visits. In the case of ties (12% of enrollees), we assign an enrollee to the clinic with the most plan payments for diabetes outpatient visits. First, we included in our models the number of UPlan diabetic enrollees seen at the clinic during the year. Second, we included the total number of diabetics within the age group 18 to 75 years old across all purchasers, who were seen by the clinic
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as reported by MNCM. In our data, 1,338 person-years (29% of all person-years) were assigned to a nonreporting clinic. Because the number of all-payer diabetics was not available in our data for these patients, we instead used the annual average number of diabetics seen at clinics reporting to MNCM. Third, we included an indicator variable for whether an enrollee’s assigned clinic was affiliated with HealthPartners, since this organization has a national reputation for developing system-wide initiatives to improve chronic disease management (Beaulieu, Cutler, Ho, Horrigan, & Isham, 2003). Finally, because spending is a function of both the quantity and intensity of services and the unit prices of those services, it is important to account for differences across providers in unit prices resulting from contract negotiations. To achieve this aim, we constructed a “price index” for each clinic that equals the average payment for prevalent outpatient visits and included it as a control in the model.2
Empirical Specification To examine the effect of patient-dependent quality activities (Patient) and providerinitiated quality (Provider) on the ith enrollee’s annual diabetes spending (S) in time t, we specified the following regression model: Sit = α + Patient it β + Providerit γ + X it δ + θ j + τt + o` it , where {β,γ,δ} are parameters to be estimated, and ò is an error term. Additionally, the model includes a set of controls (X), as well as clinic- and time-specific fixed effects, θ j and τt , respectively. The clinic fixed effect captures unobserved clinic-specific quality, while the time effect captures across-time changes in quality or measures that are common across clinics. These fixed effects mitigate bias from omitted variables to identify the true relationship between quality and costs. In this context, the β and γ parameters may be interpreted as the change in spending with the change in a quality metric, while δ describes the contribution of control variables. We use generalized linear models assuming a log link and Poisson family distribution to estimate the relationship between our quality measures and activities with AMB/RX spending. The assumption of a Poisson family distribution was based on results from the Park test, as specified by Manning and Mullahy (2001). We separately estimate ED/IP spending using a two-part model. The first part models the probability of any ED/IP spending using a binary probit, while the second part models the level of spending conditional on a positive value. We estimated the second equation via generalized linear model with a log link and gamma family distribution. For the continuous explanatory variables in all models (e.g., number of outpatient visits), we take a log transformation to address skewness and improve fit.3 Since we observe multiple observations of enrollees over time, we use Huber-White robust standard errors clustered by enrollee to correct for autocorrelation.
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Results Descriptive Statistics Table 1 reports descriptive statistics. The average age of diabetic enrollees within the UPlan population is 54 years and 45% are female. Compared with national estimates from the 2008-2010 Medical Expenditure Panel Survey of individuals 18 to 75 years old with diabetes, the UPlan population is younger and less likely to be female.4 Twenty percent has type 1 diabetes. Across the study period, average total diabetesrelated spending was $3,408. AMB/RX spending constitutes two thirds of diabetesrelated spending on average ($2,389). Approximately 14% of enrollees had at least one ED visit or inpatient stay over the time period. Unconditional average spending on ED/IP care was $1,018. For the HEDIS measures, 80% of enrollees received an annual HbA1c test, 72% a cholesterol screening, and 86% medical attention for nephropathy. Twenty-seven percent of diabetic enrollees participated in the UPlan’s disease management program.
Multivariate Analysis Results Table 2 reports marginal effects and standard errors for models corresponding to AMB/RX spending. Column 1 reports our baseline model, and Column 2 includes a set of interaction terms between disease management participation and patientdependent quality activities to test for moderating effects of self-care management that may influence medical care consumption. Among the set of provider-initiated quality measures, we find positive and significant effects on two process-of-care measures: receipt of an HbA1c test and medical attention for nephropathy. The marginal effect on medical attention for nephropathy suggests that its receipt is associated with $303 higher annual spending. Enrollees on a prescribed medicine regime for diabetes management had higher annual spending. For example, an enrollee who filled an insulin prescription and adhered to it throughout the year had $2,031 (= $1,733 + $298) higher AMB/RX spending. Spending increased by $840 for enrollees on hypoglycemia medications. Unsurprisingly, AMB/ RX spending is related to an enrollee’s diabetes outpatient visits. These results suggest that each additional visit is associated with a $270 increase in diabetes-related spending. Participants in the UPlan’s disease management program have $174 higher average AMB/RX spending than nonparticipants, although the effect is only marginally significant (p = .052). Column 2 tests for moderating effects of disease management participation on the cost–quality relationship. None of the interaction terms are statistically significant, and we conclude that disease management program participation in this population does not appear to moderate the cost–quality relationship. We observe modest differences in AMB/RX spending by age. Each additional year of age is associated with −$11 in AMB/RX spending. There was no difference by
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Table 1. Mean Patient-Level Descriptive Statistics for UPlan Enrollees With Diabetes, 20062009. Diabetes-Related Provider Plan Payments (2009 $) Total Ambulatory and prescription drug Inpatient and emergency department Provider-Initiated Quality Measures Process measures Received HbA1c test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Optimal diabetes care composite measure at assigned clinic Patient-Initiated Quality Measures Medical utilization # Diabetes-related outpatient visits Filled a prescription for insulin Filled a prescription for hypoglycemia Filled a prescription for cholesterol Medication adherence (conditional on having a prescription) Insulin Hypoglycemic Rx Cholesterol Rx Disease management (DM) program participation Other Patient and Provider Attributes Age Female Risk score Type 1 diabetes Not assigned to a MNCM reporting clinic # UPlan diabetics at assigned clinic # All-payer diabetics at assigned clinic Attended HealthPartners clinic Price index at assigned clinic Year 2006 2007 2008 2009
3,408 (9,804) 2,389 (2,995) 1,018 (8,771)
0.80 0.72 0.86 0.21 (0.09)
3.76 (3.05) 0.34 0.42 0.66 0.42 0.67 0.70 0.27 54.03 (10.61) 0.45 1.00 (1.00) 0.20 0.29 52.53 (75.02) 765.09 (451.84) 0.32 99.94 (20.74) 0.22 0.24 0.26 0.27
Note. MNCM = Minnesota Community Measurement. Data includes 4,849 person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinic-specific variables. Standard Deviations for continuous variable in parentheses.
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Table 2. Marginal Effects for Ambulatory and Rx Diabetes-Related Plan Payments for UPlan Enrollees With Diabetes, 2006-2009.
Provider-Initiated Quality Measures Process measures Received HbA1c Test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Filled a prescription for insulin Filled a prescription for hypoglycemia Rx Filled a prescription for cholesterol Rx Patient-Initiated Quality Measures Medical utilization ln(# Diabetes-related outpatient visits) Medication adherence Insulin Hypoglycemia Rx Cholesterol Rx Disease management (DM) program participation DM program participation interactions DM * ln(# Diabetes-related outpatient visits) DM * Filled a prescription for insulin DM * Filled a prescription for hypoglycemia Rx DM * Filled a prescription for cholesterol RX DM * Adherence to insulin DM * Adherence to hypoglycemia Rx DM * Adherence to cholesterol RX Other Patient and Provider Attributes ln(Age) Female ln(Risk scores) Type 1 diabetes Not assigned to MNCM reporting clinic ln(# UPlan diabetics at assigned clinic) ln(# All-payer diabetics as assigned clinic) Assigned to HealthPartners clinic ln(Price index at assigned clinic) Time Trend 2007 2008 2009 Clinic fixed effects Observations
Base
Moderating effects
(1)
(2)
284* (143) −110 (125) 303* (123) 1,733** (106) 402** (108) 176 (133)
284* (144) −112 (125) 291* (123) 1,727** (122) 356** (134) 212 (174)
1,016** (71)
1,028** (90)
298** (103) 438** (112) 259* (106) 174 (89)
236 (131) 473** (131) 343** (129) 381 (258) −52 (155) 23 (194) 136 (221) −128 (252) 187 (212) −120 (233) −255 (219)
−594** (214) −9 (85) 456** (101) 699** (102) −204 (125) −9 (122) −18 (87) −318 (1541) 404 (230)
−621** (215) −12 (85) 456** (103) 694** (103) −200 (126) −8 (123) −10 (87) −522 (1554) 395 (230)
198* (88) 347** (114) 559** (120) Y 4,849
200* (89) 344** (115) 555** (121) Y 4,849
Note. MNCM = Minnesota Community Measurement. Data includes person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinic-specific variables. All Ambulatory and Rx diabetes-related plan payments models are generalized linear models using a log link function and a Poisson family function. Huber-White robust standard errors clustered by patient in parentheses. **p < .01. *p < .05.
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gender. We find that people with type 1 diabetes have $699 higher spending relative to type 2 diabetics after controlling for differences in treatment patterns. Table 3 provides marginal effects and standard errors for our baseline and moderating effects ED/IP diabetes spending models. Part 1 corresponds to the probability of positive ED/IP spending, while Part 2 reports results for the level of ED/IP spending conditional on use. Very few quality measures predict an enrollee’s probability of having any ED/IP spending. Exceptions include filling a prescription for hypoglycemia (0.034) and the number of diabetes-related outpatient visits. For ED/IP spending conditional on use (Part 2), the only significant quality measure is disease management participation. Enrollees who participate have $6,745 higher average ED/IP spending.
Sensitivity Checks We conducted five sensitivity checks. First, we investigated whether confounding factors affect both the decision to participate in disease management as well as ED/IP spending. For example, if an enrollee has an unobserved health shock that leads to an ED visit and a diagnosis that triggers an invitation to participate in the program, then the association between disease management and ED/IP spending in our base model is likely biased upward. To check for this occurrence, we reestimated the baseline model excluding enrollees’ first observation if their initial year of data included positive ED/ IP spending. This exclusion criterion affects 183 enrollee-year observations. We found no significant difference in the relation between disease management and ED/IP spending between our base model and our model excluding enrollees with first year ED/IP spending. Second, we tested the sensitivity of our results to the presence of outliers. We constructed a trimmed sample by removing observations in the bottom 1% and top 1% of the diabetes spending distribution. In the AMB/RX spending models, we observe slightly attenuated magnitudes for our quality measures for medical attention for nephropathy, outpatient visits, and prescribed medication use. For the ED/IP models, trimming was applied only to the subsample with positive spending (e.g., observations from the upper 1% of the spending distribution were removed). With the trimmed sample, we found the marginal effect of disease management participation on ED/IP spending is smaller, suggesting the potential influence of outliers Third, we reestimated the models to account for the possibility of a lag between the quality of care received and spending. We used a one-period lag, whereby quality in time t − 1 is hypothesized to affect spending in time t. For AMB/RX spending, most of the statistically significant relationships between specific quality measures and spending persist, although the effect of HbA1c testing is no longer significant. In the ED/IP models, we continue to find few explanatory variables that predict either an enrollee’s likelihood of having any ED/IP spending or level of spending. Although we find no difference between disease management participants and nonparticipants in the probability of having any ED/IP spending, participants continue to have higher levels of ED/IP spending conditional on use.
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−1,422 (2,872) 1,357 (1,677) 1,646 (2,201) 1,068 (1,764) −1,265 (1,710) −1,267 (1,807)
38 (1,122) −1,772 (1,668) −1,162 (2,028) 49 (2,238) 6,745** (1,957)
−0.006 (0.017) 0.021 (0.014) 0.034* (0.014) −0.013 (0.014)
0.031** (0.008) −0.006 (0.015) −0.023 (0.016) −0.016 (0.013) 0.012 (0.012)
(2)
Part 2
0.028 (0.018) −0.024 (0.015)
(1)
Provider-Initiated Quality Measures Process measures Received HbA1c test Received low-density lipoprotein cholesterol screening Received nephropathy medical attention Filled a prescription for insulin Filled a prescription for hypoglycemia Rx Filled a prescription for cholesterol Rx Patient-Initiated Quality Measures Medical utilization ln(# Diabetes-related outpatient visits) Medication adherence Insulin Hypoglycemia Rx Cholesterol Rx Disease management (DM) program participation DM program participation interactions DM * ln(# Diabetes-related outpatient visits) DM * Filled a prescription for insulin DM * Filled a prescription for hypoglycemia Rx DM * Filled a prescription for cholesterol RX DM * Adherence to insulin
Part 1
Base model
−1,341 (2,139) 5,190 (3,592) 9,741* (4,540) −3,794 (3,814) −4,969 (4,045)
−0.002 (0.016) 0.006 (0.028) −0.003 (0.031) 0.053 (0.031) −0.01 (0.032)
(continued)
315 (2,055) 2,272 (2,570) −2,846 (2,770) 3,588 (3,361)
610 (1,314)
2,054 (2,366) −1,144 (2,104) −5,078* (2,525) −239 (2,257)
−1,285 (2,957) 832 (1,727)
(4)
Part 2
−0.003 (0.017) −0.032 (0.019) −0.012 (0.016) −0.023 (0.031)
0.032** (0.009)
−0.005 (0.017) 0.019 (0.016) 0.035* (0.017) −0.028 (0.017)
0.028 (0.018) −0.024 (0.015)
(3)
Part 1
Moderating effects
Table 3. Marginal Effects for Inpatient and ED Diabetes-Related Plan Payments for UPlan Enrollees With Diabetes, 2006-2009.
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−0.006 (0.014) 0.01 (0.016) 0.002 (0.016) N 4,849
−0.084** (0.026) −0.01 (0.010) 0.151** (0.007) −0.012 (0.016) −0.002 (0.013) −0.008* (0.004) 0.002 (0.009) −0.006 (0.014) −0.011 (0.027)
0.026 (0.035) −0.012 (0.029)
(3)
Part 1
−4,027 (2,367) −983 (2,687) −894 (2,709) Y 655
−2,864 (3,715) 1,514 (1,362) 6,716** (1,712) 1,939 (2,144) −3,382 (2,956) −1,728 (2,401) −314 (4,088) −2,737 (8,997) 5,209 (5,009)
−8,535 (4,967) 9,563* (4,468)
(4)
Part 2
Moderating effects
Note. ED = emergency department. MNCM = Minnesota Community Measurement. Data includes person-year observations from 1,679 UPlan enrollees meeting HEDIS diabetes criteria on an annual basis. Mediation adherence is defined as having an annual medication possession ratio for the specific medication greater than or equal to 0.80. Enrollees not assigned to a MNCM reporting clinic given the annual means of enrollees assigned to MNCM reporting clinics for all clinicspecific variables. The probability of having Inpatient or ED plan payments was modeled using probits. Inpatient and ED diabetes-related plan payments models are generalized linear models using a log link function and a gamma family function. Huber-White robust standard errors clustered by patient are in parentheses. **p < .01. *p < .05.
−4,314 (2,206) −1,878 (2,527) −1,625 (2,647) Y 655
−0.005 (0.014) 0.01 (0.016) 0.002 (0.016) N 4,849
(2)
Part 2
−2,993 (3,661) 1,104 (1,314) 6,547** (1,503) 1,620 (1,994) −2,918 (2,969) −1,258 (2,251) 16 (4,187) −2,614 (8,688) 1,882 (4,538)
Base model
−0.088** (0.026) −0.01 (0.010) 0.151** (0.007) −0.012 (0.016) −0.002 (0.013) −0.008* (0.004) 0.002 (0.009) −0.006 (0.014) −0.012 (0.027)
(1)
DM * Adherence to hypoglycemia Rx DM * Adherence to cholesterol RX Other Patient and Provider Attributes ln(Age) Female ln(Risk scores) Type 1 diabetes Not assigned to MNCM reporting clinic ln(# UPlan diabetics at assigned clinic) ln(# All-payer diabetics as assigned clinic) Assigned to HealthPartners clinic ln(Price index at assigned clinic) Time Trend 2007 2008 2009 Clinic fixed effects Observations
Part 1
Table 3. (continued)
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Fourth, we estimated the models including an interaction term for type 2 diabetes and filling a prescription for insulin to capture diabetes-specific severity in addition to the risk score. The inclusion of this term did not alter the estimated relationships reported within the baseline spending model results. Fifth, we reestimated the models using total spending rather than diabetes-specific spending. Although the general pattern of results is maintained, there is greater variation in total spending across individuals, leading to a lower signal-to-noise ratio and less precise estimates.
Are Quality Investments Related to Better Clinical Outcomes? We examined whether spending related to improvements in provider-initiated care processes and patient-related quality activities is associated with intermediate clinical outcomes measured at the clinic level. Specifically, we used the Optimal Diabetes Care (ODC) composite score publicly reported on an annual basis by MNCM to capture clinics’ overall record of diabetes-related intermediate clinical and behavioral outcomes. The ODC score measures the proportion of patients within a clinic with diabetes and of ages 18 to 75 years that attain all of the following treatment goals: (1) HbA1c level of
