Patient Medical Group Continuity and Healthcare Utilization
Published Online: August 23, 2012
Louise H. Anderson, PhD; Thomas J. Flottemesch, PhD; Patricia Fontaine, MD, MS; Leif I. Solberg, MD; and Stephen E. Asche, MA
Yearly total cost of care was defined as the total amount paid for medical services to medical providers during the year. To avoid variation from differences in contracted reimbursement rates across medical groups, all costs were based on a standardized measure, the HealthPartners relative resource value unit. These units are based on Centers for Medicare & Medicaid Services relative value units, inpatient diagnosisrelated groups, and ambulatory payment classification weights. Use of standardized costs for each Current Procedural Terminology code, diagnosis-related group, and pharmacy claim made patient costs independent of the provider contract or type of insurance coverage. All costs are expressed as 2005 dollars. Costs and utilization counts were annualized for members with fewer than 12 months of enrollment in a given year.
Multilevel multiple regression models were used to estimate the association of annualized medical cost and utilization with attribution and continuity categories (NotAtt, InfAtt, HiCont, MedCont, LowCont). To account for within-subject correlation across observation years, a generalized estimating equations approach was used. For continuous outcomes of total and inpatient costs, a 2-part Heckman estimator was used.12 First, the association between patient categories and positive expenditure was determined by logistic regression. Second, the association between categories and expenditures was estimated for those with expenditures. Because both continuous outcomes were heavily skewed, a log transformation with Duan’s smearing estimator was used.13,14 For the count outcome of ED utilization, a zero-inflated Poisson model was used, first with logistic regression to estimate the association between patient categories and positive ED utilization, and second, with Poisson regression to estimate the association between categories and number of ED visits among patients with ED use.
The multivariate models adjusted for patient demographics, complexity, comorbidities, and study year. Number of medications was our measure of patient complexity because it was reliably available from pharmacy claims data, and it is a validated, transparent, and easily reproducible measure.15 We included interaction and polynomial terms that improved model performance.
To more easily interpret model results, we estimated predicted outcomes (probabilities, costs, and utilization) for each patient category, with covariate values set at the mean value within each category. We also estimated the marginal effect of changing category assignment. The predicted outcomes for the average MedCont and LowCont patients were estimated as if they were HiCont patients. These estimates quantified the impact of patient continuity.
We identified 121,780 patients who were covered by HealthPartners insurance and met the inclusion criteria. Among patients enrolled in 2005, 28% were excluded because of age, and 37% were excluded because they were not enrolled with HealthPartners in all years. An additional 16% were excluded because of noncontinuous pharmacy coverage or fewer than 10 months of enrollment each year.
In 2007, the study midpoint, the average patient age was 50.5 years, 52.4% were female, and most patients had commercial insurance (88.2%). Roughly 7% (7.3%) were identified as having asthma, 3.3% coronary artery disease, 0.8% congestive heart failure, 2.6% chronic obstructive pulmonary disease, 19.2% depression, and 7.5% diabetes. Total costs averaged $8547 per patient, and there were an average of 2.2 primary care visits per patient.
Characteristics and utilization of the study population by attribution and continuity categories are presented in Table 1. Of the 121,780 patients, 5031 (4%) were not attributable (NotAtt) in any of the 5 years, and 14,590 (12%) were categorized as InfAtt. The NotAtt and InfAtt patients were younger, included fewer females, and were less likely to have chronic conditions. They also had the lowest average total annual costs, lowest inpatient hospitalization rate, and lowest rate of ED use.
Most (84%) were categorized as FreqAtt. They had the highest average age (51.5 years), the largest percentage of females (57.1%), and the highest prevalence of chronic conditions, and were more likely than patients in the other categories to have Medicare coverage.
Most of the FreqAtt patients were categorized as Hi-Cont. That group was older (52.1 years) and mostly female (57.0%), and had the highest prevalence of all chronic diseases except depression. Eight percent (7775 of 102,159) of FreqAtt patients were categorized as MedCont, and 3% (3237 of 102,159) were LowCont. The MedCont and LowCont groups were younger than the HiCont group but were similar in the proportion of females and prevalence of asthma and depression.
There were several significant covariates in the multivariate analysis. For total and inpatient costs, the outcomes increased with age, were higher for females, and were higher for those insured by Medicare or Medicaid. Costs and utilization trended upward over time and increased with care complexity (number of prescription medications) and comorbidities. Patients with asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, and depression had marginally higher costs. Surprisingly, patients with diabetes had slightly lower costs than patients without comorbidities, after adjusting for covariates. However, because the claims-based algorithms used to identify diabetes required 2 outpatient diagnostic codes or 1 inpatient diagnostic code and an active prescription, they indicated active diabetes management. Costs for diabetes associated with medication management and comorbidites were reflected in covariates; therefore, the result can be interpreted as the marginal impact of behavioral diabetes management, after adjusting for covariates.
The probability of positive ED utilization decreased with age and was less likely among females than males. The number of ED visits also decreased with age, but was not significantly different for females than for males.
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