Article

Relationship between glycemic control and diabetes-related hospital costs in patients with type 1 or type 2 diabetes mellitus.

Boston Health Economics, Inc., 20 Fox Rd., Waltham, MA 02451, USA.
Journal of managed care pharmacy: JMCP (Impact Factor: 2.68). 05/2010; 16(4):264-75.
Source: PubMed

ABSTRACT Diabetes mellitus requires continuous medical care and patient self-management in order to prevent short-term complications and decrease the risk of long-term complications, which can result in substantial increases in the total economic burden of the disease. Findings from randomized clinical trials have shown that improved glycemic control may reduce the risk of long-term complications as long as a target for hemoglobin A1c is not set below 7% for intensive glycemic control. However, limited data from clinical practice are available regarding the relationship between glycemic control and medical costs associated with diabetes care.
To assess the potential relationships between glycemic levels, diabetes-related hospitalizations, and hospital costs among adult patients with either type 1 or type 2 diabetes mellitus who were assigned to a primary care provider (PCP) in a clinic that was affiliated with a managed care organization (MCO).
A retrospective cohort analysis was conducted using data from approximately 200,000 members of the Fallon Clinic Health Plan who were assigned to a clinic PCP at any time during a 5-year study period beginning January 1, 2002, and ending December 31, 2006. Patients aged 30 years or older with at least 2 medical claims with any listed diagnosis of diabetes mellitus (ICD-9-CM code 250.xx) during the study period and 2 or more A1c values within 1 year of each other during the study period (mean 7.6 tests over 39 months; median=6.8), were identified and stratified into 1 of 5 groups defined by 1% increments of A1c, based on their mean A1c values during the entire study period. A1c data were available only for tests ordered by a clinic provider; tests ordered by other specialists in the MCO's network were absent from the database. The study follow-up period started with each patient's first A1c test (index date) and continued until plan disenrollment, death, or December 31, 2006, whichever was earlier (end date), regardless of when the diagnosis of diabetes mellitus was made. Study measures included the proportion of patients with 1 or more diabetes-related hospitalizations, number of diabetes-related inpatient stays, and the associated estimated hospitalization costs over the follow-up period. Diabetes-related hospitalizations were identified based on a diagnosis, in any of 10 diagnosis fields, for 1 of 16 selected complications of diabetes identified by the authors. Hospital costs were estimated using discharge data (diagnoses and costs calculated from cost-to-charge ratios) contained in the 2004 Healthcare Cost and Utilization Project (HCUP) database and inflated to 2007 dollars using the medical care component of the Consumer Price Index. Multivariate models controlled for age, sex, number of A1c tests, diagnosis of cancer, and follow-up time. A multivariate logistic regression analysis was conducted with the occurrence of at least 1 diabetes-related hospital admission as the dependent variable. In the logistic regression analysis, follow-up time was defined as time from the index date to the date of the first diabetes-related hospitalization, plan disenrollment, death, or the study end date, whichever occurred first. A generalized linear model with a Poisson distribution and a log link was employed to estimate the rate of hospital admissions. In the Poisson regression analysis, follow-up time was defined as duration of the entire study follow-up period and was an offset variable. Costs were estimated using a 2-part model: first, we calculated the probability of having a hospitalization, as determined by the logistic regression above; second, a generalized linear model with a negative binomial distribution and a log link was used to predict the mean cost of diabetes-related hospitalizations only for patients with an inpatient stay, with the duration of the entire study follow-up period as an offset variable. We calculated the mean per patient cost of diabetes-related hospitalizations by multiplying the probability of having a hospitalization (as determined by the first part of the model) by the mean costs for patients who had such admissions (as determined by the second part of the model).
9,887 patients met study selection criteria. Mean A1c level was < 7% for 5,649 (57.1%) patients, 7% to < 8% for 2,747 (27.8%), 8% to < 9% for 1,002 (10.1%), 9% to < 10% for 312 (3.2%), and 10% or more for 177 (1.8%). Over a mean (median) 40 (40) months of follow-up (interquartile range = 30-50 months), 28.7% (n = 2,838) of patients had 1 or more diabetes- related hospital admissions. In the logistic regression analysis, odds of having at least 1 diabetes-related hospital stay did not significantly differ for patients with mean A1c of < 7% compared with patients in most higher mean A1c categories (7% to < 8%, 8% to < 9%, or 9% to < 10%); however, odds of having a diabetes-related hospitalization were significantly higher for patients with mean A1c of 10% or more compared with patients with mean A1c of < 7% (odds ratio = 2.13, 95% confidence interval = 1.36-3.33). In the negative binomial regression analysis of those with at least 1 hospital admission, estimated costs per hospitalized patient increased by mean A1c level. In the Poisson regression analysis, the rate of diabetes-related hospitalizations significantly increased by A1c level (13 per 100 patient-years for patients with mean A1c of < 7% vs. 30 per 100 patient-years for mean A1c of 10% or more when covariates were held at mean levels, P<0.001). In the 2-part model results, adjusted mean estimated costs of diabetes-related hospitalizations per study patient were $2,792 among those with mean A1c of < 7% and $6,759 among those with mean A1c of 10% or more.
In this managed-care plan, the odds of having at least 1 diabetes-related hospitalization were not significantly associated with higher mean A1c except for patients with mean A1c of at least 10%. However, higher mean A1c levels were associated with significantly higher estimated hospitalization costs among those with at least 1 hospitalization and with higher rates of diabetes-related hospital utilization per 100 patient-years.

Download full-text

Full-text

Available from: Joseph Menzin, Jul 04, 2015
0 Followers
 · 
132 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Left ventricular dysfunction (LVD) in type 2 diabetes mellitus (DM) (DYDA) study is a prospective investigation enrolling 960 with DM without overt cardiac disease. At baseline, a high prevalence of LVD was detected by analysing midwall shortening. We report here the incidence of clinical events in DYDA patients after 2-year follow-up and the frequency of LVD detected at baseline and 2-year evaluation. Systolic LVD was defined as midwall shortening ≤15%, diastolic LVD as any condition different from "normal diastolic function" identified as E/A ratio on Doppler mitral flow between 0.75 and 1.5 and deceleration time of E wave >140ms. Major outcome was a composite of major events, including all-causes death and hospital admissions. During the study period, any systolic/diastolic LVD was found in 616 of 699 patients (88.1%) in whom LVD function could be measured at baseline or at 2 years. Older age and high HbA1c predicted the occurrence of LVD. During the follow-up 15 patients died (1.6%), 3 for cardiovascular causes, 139 were hospitalized (14.5%, 43 of them for cardiovascular causes, 20 for a new cancer). During a 2-year follow-up any LVD is detectable in a large majority of patients with DM without overt cardiac disease. Older age and higher HbA1c predict LVD. All-cause death or hospitalization occurred in 15% of patients, cardiovascular cause was uncommon. Independent predictors of events were older age, pathologic lipid profile, high HbA1c, claudicatio and repaglinide therapy. Echo-assessed LVD at baseline was not prognosticator of events.
    Diabetes research and clinical practice 06/2013; 101(2). DOI:10.1016/j.diabres.2013.05.010 · 2.54 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: To provide nurse practitioners (NPs) with a review of the 2012 Standards of Care for the management of hospitalized patients who are hyperglycemic. Data sources: The 2012 American Diabetes Association's (ADA) Standards of Care for the treatment of inpatient hyperglycemia and selected evidence-based articles. Conclusions: Because hyperglycemia occurs at alarming rates in the inpatient setting when hyperglycemia is not controlled, there is a great impact on acute and even chronic conditions. These complications will lead to increased healthcare costs. Implications for practice: It is essential that NPs who care for hospitalized, hyperglycemic patients are aware of the 2012 ADA Standards of Care. To obtain CE credit for this activity, go to http://www.aanp.org and click on the CE Center. Locate the listing for this article and complete the post-test. Follow the instructions to print your CE certificate. Disclosure The authors report no competing interests.
    Journal of the American Academy of Nurse Practitioners 12/2012; 24(12):683-9. DOI:10.1111/j.1745-7599.2012.00770.x · 0.87 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this study was to define the association between the medical utilization of osteoarthritis patient and its related factors. We used the 2005 Korean National Health and Nutrition Survey data and we enrolled 2833 participants who were forty or older and who were diagnosed as having osteoarthritis by a doctor within 1 year and who had suffered from osteoarthritis for more than 3 months. The Andersen behavioral model was used as the analytic framework, and the variables were categorized into predisposing, enabling, and need factors. To determine the influence of each variable on the medical utilization of osteoarthritis patient, we applied hierarchical logistic regression analysis with two stages: the first stage included the predisposing and enabling factors and the second stage included the need factors. On the hierarchical logistic analysis, the variables of personal income, the type of medical security, the duration of arthritis related symptoms within 1 month, the subjective health status and the duration of osteoarthritis showed a statistically significant difference between whether the medical utilization in men patients. And the variables of age, limitation activity due to osteoarthritis, arthritis related symptoms within 1 month, and the subjective health status had a statistically significant difference between whether the medical utilization in women patients. The patients who tend to receive less care are those suffer less from symptoms of osteoarthritis, those who are within the initial phase, or those with a low-level severity of osteoarthritis. It is necessary to encourage patients to receive the treatment in the initial phase.
    Journal of Preventive Medicine and Public Health 11/2010; 43(6):513-22. DOI:10.3961/jpmph.2010.43.6.513