Direct costs in impaired glucose regulation: results from the population-based Heinz Nixdorf Recall study

Heinz Nixdorf recall study

Individual characteristics/primary data of the participants were derived from the ongoing Heinz Nixdorf Recall (HNR; ‘Risk Factors, Evaluation of Coronary Calcium and Lifestyle’) study, a prospective, population-based cohort study evaluating modern risk stratification techniques for coronary outcomes. The study was approved by the responsible local ethics committee. In brief, 9484 inhabitants 45-75-years-old were randomly selected from the mandatory registries of residence of three German cities (Essen, Mülheim and Bochum) located in the northwest of Germany. Between 2000 and 2003, 4814 participants gave their informed consent and took part in the baseline study (recruitment efficacy proportion: 55.8%, response proportion: 53.3%).9 Further details have been described previously.10 Of the 4814 participants included in the baseline study, 4157 (86.4%) attended the second examination visit between 2006 and 2008 (5 year follow-up).

Data acquisition

Self-reported data were assessed by a standardized interview. Further clinical data were derived from laboratory and clinical investigations of the participants. In case of participants' approval, the main corresponding health insurances were asked to cooperate with the HNR study. For the current analyses, primary data from the HNR study were individually linked with routine data on healthcare utilization and associated costs from the main statutory health insurances in Germany.

Study population

Health insurance data of the main German statutory health insurances were available from 2184 participants. Of these, 1722 had health insurance data for at least 1 year before the 5 year follow-up and thus were eligible for the analyses. A further 13 participants were excluded because of missing data on their diabetes stage, resulting in 1709 participants for the analyses. Compared with the excluded participants (n=2448), the participants of the final study sample were on average 0.9 years older (p_Wilcoxon<0.001) and less frequently male (44.5% vs 53.0%, pχ²<0.001).

Definition of diabetes stages

According to the information provided by the participants and the results of a fasting plasma glucose (FPG) test at follow-up, all participants were divided into four groups of diabetes regulation. ‘Previously diagnosed diabetes’ was present if the participants reported a physician's diagnosis of diabetes or antihyperglycemic treatment at follow-up. Applying the criteria of the WHO, the International Diabetes Federation (IDF) and the European Diabetes Epidemiology Group (EDEG),11 ,12 ‘previously undetected diabetes’ was assumed if the FPG value was at least 7.0 mmol/L (corresponding to 126 mg/dL) and the participants reported no previous diagnosis of diabetes at follow-up. ‘Impaired fasting glycemia’ (IFG) was defined as an FPG value between 6.1 mmol/L and <7.0 mmol/L (corresponding to 110−<126 mg/dL) at follow-up. If the FPG value was lower than 6.1 mmol/L, participants were classified as having ‘normal fasting glycemia’ (NFG).

Sociodemographic and clinical variables

Sociodemographic data included age, sex, country of birth (Germany/other country), living with a partner (yes/no), education, status of employment and quartiles of equivalent income (net income adjusted for household size according to the Luxembourg Income Study13 ,14).

The following clinical variables from the follow-up were included: type of diabetes, diabetes treatment and diabetes duration among people with previously diagnosed diabetes, standardized analyzed glycated hemoglobin value (%, mmol/mol), prevalent hypertension (mean of the second and third systolic/diastolic blood pressure measurement >140/90 mm Hg or antihypertensive medication) since baseline ongoing self-reported previous medical diagnosis of stroke or myocardial infarction (validated by clinical experts) and current body mass index (BMI (kg/m²)) calculated from standardized measurements of height and weight.

Healthcare utilization and associated costs

Healthcare utilization, associated costs and diagnoses (documented as International Statistical Classification of Diseases and Related Health Problems, German Modification (ICD-10-GM)) or pharmaceutical coding of drugs (Anatomical Therapeutic Chemical (ATC) codes) were derived from the statutory health insurances' data. Numbers and percentages of healthcare utilization per capita as well as resulting costs referred to the four complete quarters before the individual follow-up examination (termed index year). The direct medical costs per capita were determined from the perspective of the statutory health insurance.

Inpatient data included the date of, length and diagnosis for each hospitalization (ICD-10-GM), diagnosis-related group (DRG), and corresponding costs reduced by patients' copayments. In case of missing net costs (10.5%), these costs were estimated using data reported by other statutory health insurances assuming sufficient transferability.

In Germany, costs of outpatient consultations are reimbursed according to the respective German compensation scheme (‘Einheitlicher Bewertungsmaßstab’ (EBM) reimbursement). Furthermore, extrabudgetary services, costs of dialysis equipment, and material expenses reported by the statutory health insurances were considered.

Information on prescribed medication comprised prescription date, ATC code, and costs of medication adjusted for mandatory drug discounts and copayments. Missing net medication costs (5.1%) were estimated using reported data by other health insurances. Costs of medication are reported as total medication costs, total costs without antihyperglycemic medication, and costs of cardiovascular medication.

Statistical analyses

The description of the data was presented for the total cohort and stratified by diabetes stage. The continuous variables were described using means and SDs. All categorical variables were described using the percentages with 95% CIs. All variables were age-standardized and sex-standardized in order to adjust for potential demographic differences between the diabetes stages (age strata: <65 years, ≥65 years) to the German population using data from the Federal Statistical Office (reference date: 31 December 2006).

Total costs and different cost categories are given as costs per capita in Euro for the last follow-up year 2008 (2008€). Costs of previous years were inflated to 2008 using the German Consumer Price Index.15 Owing to the right skewed distribution of healthcare utilization data including costs, 95% CIs for the mean values and percentages of health utilization were estimated using bootstrap procedures.16 ,17

The associations between mean total direct healthcare costs, costs of inpatient or outpatient treatment, or medication costs as dependent variables and diabetes stage as main independent variable were estimated using multiple regression analyses. For comparison of the costs between the different diabetes stages, cost ratios (CRs) were estimated adjusting stepwise for age and sex (model 1), additionally country of birth, living with a partner, and equivalent household income (model 2), hypertension, stroke, myocardial infarction, BMI (model 3), and finally healthcare utilization costs of the year before the index year (model 4). Since data contained persons with no healthcare utilization (8.1%), two-part models were used.18 ,19 By combining both parts of the model using generalized linear models (1st Poisson regression model with robust error variance,20 ,21 2nd γ regression model), expected CRs for the whole study population were estimated.

Description of the study population

The final study population is described in detail in table 1. A total of 14.6% of the participants (n=250) had been previously diagnosed with diabetes (type 1 diabetes: n=9, type 2 diabetes: n=207, unknown diabetes type: n=34). Mean diabetes duration was 9.6 years. Regarding diabetes therapy, more than half (56%) of the participants were treated with oral antihyperglycemic drugs, about 14% were treated with insulin, and 9% used both oral antihyperglycemic drugs and insulin (data not shown).

Healthcare utilization

Direct healthcare costs

Total direct healthcare costs (table 3) ranged between €1810 (1634 to 2035) for NFG and €2761 (2378 to 3268) for people with previously diagnosed diabetes.

Regarding the subcategories of healthcare costs, costs were consistently highest among people with previously diagnosed diabetes and lowest among people with NFG. Moreover, after the exclusion of antihyperglycemic drugs, medication costs remained highest among people with previously diagnosed diabetes. The order of costs between people with previously undetected diabetes and IFG varied; however, CIs were large and overlapping.

Comparison with other studies

International comparisons are limited because of differences between healthcare systems and diabetes therapy, differences in the study design, cost analysis, and characteristics of the study population.

To the best of our knowledge, there is no study analyzing and comparing total healthcare costs and costs in different healthcare sectors stratified by diabetes stage including IFG and previously undetected diabetes. Furthermore, costs were predominantly reported in absolute figures without presenting adjusted CRs.

As early as 2000, Nichols et al6 estimated direct excess medical care costs in the USA (reference: without impending diabetes) up to 8 years preceding type 2 diabetes diagnosis. Mean total excess costs (1998 $1205, ie, €1112), as well as inpatient (1998 $639, ie, €593), outpatient (1998 $390, ie, €363), and medical costs (1998 $177, ie, €164) reported by Nichols et al, were remarkably higher than for people with IFG and unknown diabetes in the present study. In addition, Zhang et al4 ,5 reported excess costs (expressed as cost differences) for people with IFG and unknown diabetes, however, without presenting absolute costs in the groups. Again, the mean excess costs for people with previously undetected diabetes were remarkably higher (2007 $1745, ie, €1303) than in the HNR study, but excess costs for people with IFG (2009 $443, ie, €321) were lower than reported by Nichols et al and slightly higher than in the HNR study (2008 €225). The corresponding unadjusted total CR of people with IFG compared with those without diabetes in the studies of Nichols would be 1.5, that is, higher than in the HNR study. Maybe the lower CR in the HNR study is due to a high healthcare use in people with NFG, which could be explained by the relatively older population, and a high level of healthcare seeking in Germany in general.22

Previous analyses of drug prescriptions in Germany during the years preceding diabetes diagnosis (1993–2002) showed 21%/28% higher prescription costs for men/women before diabetes diagnosis.7 Recently, medication costs by glucose tolerance stage were estimated in the southern German population-based KORA (Cooperative Health Research in the Region of Augsburg) follow-up study.16 ,23 The higher age-adjusted and sex-adjusted medication CRs (when compared with the results of this study) may be explained by differences in the definitions of IFG and previously undetected diabetes (based on oral glucose tolerance tests in the KORA study vs fasting glucose in the HNR study) associated with different susceptibility to diseases,24 ,25 differences in the assessment of medication (KORA: inclusion of non-prescription medication) and regional differences in drug therapy.26 ,27

Study limitations and strengths

Several limitations have to be considered. First, the generalizability of the analyzed data may be limited because severely ill or disabled people as well as institutionalized people were not included in the HNR study. This may explain the relatively low absolute costs. Furthermore, health insurance data were available only for a subsample of the HNR study participants and participants with claims data differed from those without claims data according to age and sex. This finding was expected, since statutory health insurances in Germany contract different population groups.28 However, the analyses were based on data from statutory health insurances from various areas and it is not likely that CRs between the diabetes stages are affected. Moreover, CRs were adjusted for age, sex, socioeconomic status, and a number of additional variables. Second, since the date of diabetes onset was not known for people with newly diagnosed diabetes at follow-up, misclassification regarding the diabetes stage of these participants during the study period cannot be ruled out. In addition, missing repeated measurement of fasting plasma glucose values may have diluted differences between people with IFG and unknown diabetes. Third, the cost estimates and CRs are uncertain as reflected by large CIs. Additionally, healthcare costs may have been underestimated, because the costs of remedies, adjuvants, transportation, and dentistry care were not available.

The strengths of the study are the population-based sample of participants, the link and joint analysis of clinical data and data from the main German statutory health insurances enabling more valid estimates of healthcare utilization and costs than by using self-reported data, and the presentation of results stratified by diabetes stage which has been identified by fasting plasma glucose testing.

In conclusion, using data from various German statutory health insurances supplemented by individual data from a population-based study, healthcare utilization and associated costs were quantified for the first time in people with normal fasting glycemia, impaired fasting glycemia, previously undetected diabetes, and previously diagnosed diabetes. Although age-standardized and sex-standardized direct medical costs were highest for people with previously diagnosed diabetes, the remarkable increase in healthcare costs for people with IFG or previously undetected diabetes reported in the USA was not reproduced in Germany. Given the increasing prevalence of diabetes and its precursors, these data provide useful information for the model-based evaluation of screening programs and interventions to prevent diabetes onset or improve diabetes therapy.