Effect of screening for type 2 diabetes on healthcare costs: a register-based study among 139,075 individuals diagnosed with diabetes in Denmark between 2001 and 2009

Full details of the programme have been reported elsewhere [5, 6, 9]. In brief, we performed a population-based, stepwise screening programme in people aged 40–69 years without known diabetes between 2001 and 2006 [7, 8, 10]. All general practices in five out of 16 counties in Denmark (Copenhagen, Aarhus, Ringkoebing, Ribe and South Jutland; n = 744) were invited to take part in ADDITION-Denmark; of these, 209 (28%) accepted.

Eligible individuals registered with the 181 practices that agreed to take part were sent a diabetes risk-score questionnaire [8, 10], with an invitation to visit their general practitioner (GP) for a diabetes test and a cardiovascular risk assessment if they scored ≥5 (maximum 15 points), or were invited when visiting the practice for another reason (n = 35 practices). No reminders were sent. Participants who attended a screening appointment underwent measurement of height, weight and blood pressure. A capillary blood sample was taken for testing of random blood glucose. A venous blood sample was taken for measurement of total cholesterol and HbA_1c. GPs were encouraged to calculate participants’ scores from European Heart (Systematic COronary Risk Evaluation [SCORE]) [11] charts during the appointment, inform participants about their score and provide appropriate advice and treatment to those at high risk. Individuals with a random blood glucose ≥5.5 mmol/l or HbA_1c ≥39.9 mmol/mol (5.8%) were invited to return to the practice for a fasting blood glucose capillary test. An OGTT was performed at the same consultation if the fasting blood glucose was 5.6–6.1 mmol/l and/or the HbA_1c ≥ 39.9 mmol/mol (5.8%). WHO 1999 criteria were used to diagnose diabetes [12].

In the intervention group, participants diagnosed with type 2 diabetes were subsequently managed according to the treatment regimens to which their practice was allocated: routine care or intensive treatment [9]. Intensive treatment included: small group or practice-based educational meetings with GPs and nurses to discuss treatment targets, algorithms and lifestyle advice; audit and feedback in group meetings up to twice per year or coordinated by post; and patient education materials. GPs underwent a 1.5 day educational seminar to train in motivational interviewing to encourage lifestyle change.

We identified all eligible individuals in the original ADDITION-Denmark study (n = 153,107), including those who did not attend for screening, in the Danish National Diabetes Register [13] (the screening group). Using the same registry, we also identified all individuals aged 40–69 years without known diabetes who, between 2001 and 2006, were registered with general practices that were not invited to take part in ADDITION-Denmark or who declined to take part in ADDITION-Denmark (n = 1,759,285) (the no-screening control group). We then identified individuals from the screening and no-screening groups who were diagnosed with incident diabetes between 1 January 2001 and 31 December 2009 (Fig. 1). We included individuals diagnosed with diabetes during this period based on recent estimates of lead time, which suggest that around 2.2 years elapses between detection by screening and clinical diagnosis [6]. Including individuals diagnosed with diabetes in the 3 years (2006–2009) after the end of the ADDITION screening phase (2001–2006) would therefore capture most individuals in the no-screening group who could have been diagnosed by screening if they had been in the screening group. Our definition of incident diabetes for both groups was a proxy measure based on date of inclusion in the Danish National Diabetes Register [14].

We linked information about individuals diagnosed with diabetes to other Danish registers using unique civil registration numbers. We retrieved information on age, sex, education, immigration/emigration, citizenship, redeemed cardioprotective medication and chronic disease. Education was categorised according to UNESCO’s International Standard Classification of Education [15]. We grouped data on citizenship into European and non-European as a proxy for ethnicity. Data on healthcare service usage in primary and secondary care, as well as information on redeemed medication, were retrieved from the National Patient Register, the National Health Insurance Register and the National Prescription Register [16].

We quantified healthcare usage and calculated average healthcare costs in each group over the follow-up period by dividing the total healthcare costs for each individual by the time they spent in the study. As data from the health registers were available for different time periods, we conducted analyses for two time periods: 2001–2012 (with an average of 7.0 years follow-up; n = 139,048) and 2007–2012 (with an average of 5.2 years follow-up; n = 133,307). In this second analysis, we only included individuals who were alive and living in Denmark on 1 January 2007.

To examine potential differences in healthcare costs between the groups, we used ordinary least squares regression, adjusting for age, sex, education and GP county. We also controlled for prevalent chronic disease at diagnosis (ischaemic heart disease [IHD], stroke and cancer). We quantified costs from more than 3 years before diagnosis to more than 6 years after diagnosis using difference-in-difference regression on panel data (data covering more years over a time period) with mean correction for confounders. The difference-in-difference method allowed us to estimate the ‘isolated’ effect of the ADDITION intervention on healthcare costs by examining the differences between the screening group and the no-screening group within the periods before and after diagnosis. We included mean-adjusted covariates in the regression, for which we applied effect coding for the categorical variables [20] in order to adjust for the demographic, epidemiological and regional differences. We were therefore able to interpret the constant term as the predicted healthcare costs for an individual in the no-screening group in the year of diagnosis.

To estimate the potential economic benefits of the screening programme among individuals with incident diabetes, we subtracted the cost of the screening programme from the estimated healthcare cost savings accruing in the screening group. We calculated cost savings over a 5 year period after the screening phase using an annual discount rate of 5%.

We also examined the difference in healthcare costs between the screening group assigned to routine care only and the no-screening group.

Of 153,107 eligible people in the screening group who were sent a diabetes risk-score questionnaire, 27,177 (18%) attended their GP for a diabetes test and a cardiovascular risk assessment. A total of 1533 participants (1% of those eligible for screening) were diagnosed with diabetes; 1406 of these were subsequently included in the diabetes register. There were 1,759,285 individuals in the no-screening group.

Between 1 January 2001 and 31 December 2009, 139,075 people from our sampling frame were diagnosed with incident diabetes and included in the Danish National Diabetes Register. Of these, 13,992 (10%) were in the screening group, and 125,083 (90%) in the no-screening group. The groups were well balanced for age and citizenship (Table 2). There were slightly fewer men in the screening group (54%) than the no-screening group (56%). A larger proportion of the screening group had received over 15 years of education. Slightly lower proportions of the screening group had experienced IHD, stroke or cancer before the diagnosis of diabetes compared with the no-screening group.

Figures 2, 3, 4, 5 show the mean-adjusted annual costs per participant with incident diabetes by healthcare sector both before and after the time of diagnosis in the screening and no-screening groups. The results from the difference-in-difference regression analyses are presented in electronic supplementary material [ESM] Table 1.

After diagnosis, healthcare usage and subsequent costs were significantly higher in the no-screening group than the screening group. For example, primary care costs were approximately 5% higher in the no-screening group than the screening group during follow-up. The costs of redeemed medication were already higher in the no-screening group before diagnosis, but the difference between the groups increased further after diabetes diagnosis. Costs were approximately 12% higher in the no-screening group compared with the screening group in the 6 years following diagnosis. For inpatient and outpatient visits, there were no clear trends associated with the point of diabetes diagnosis. On average, however costs were lower among the screening group compared with the no-screening group both prior to and following diagnosis.

The mean annual cost estimates for primary care and redeemed medication were based on 2001–2012 data, while inpatient and outpatient costs were based on 2007–2012 data. The mean total cost difference is therefore estimated as the sum of the four mean annual cost components: inpatient and outpatient costs, primary care cost and cost of redeemed medication. After adjustment for confounders, annual healthcare costs per individual with incident diabetes were significantly lower in the screening group than the no-screening group across all healthcare sectors and for redeemed medication: annual difference in inpatient care costs −€662 (95% CI −€865, −€459), outpatient care costs −€82 (95% CI −€157, −€7), primary care costs −€51, (95% CI −€63, −€38) and pharmaceuticals −€94 (95% CI −€111, −€76). The difference in mean total annual healthcare costs per individual with incident diabetes between groups was −€889 (95% CI −€1196, −€581).

After subtracting the cost of introducing the ADDITION screening programme (€967 per participant with incident diabetes) from the estimated annual healthcare cost savings (€889 per participant with incident diabetes), and discounting this figure in the 5 years following the intervention, there was a cost saving associated with each individual with incident diabetes over a 5 year period of €2688 (95% CI €1421, €3995). This finding suggests that the intervention was associated with cost savings in the healthcare system within 2 years of the screening programme being introduced. Based on the 13,992 individuals with diabetes in the screening group, we estimate that the intervention saved the Danish healthcare system almost €37.6 million (95% CI €19.8 million, €55.3 million) in the 5 years after the introduction of the screening programme.

In our sensitivity analysis, the annual healthcare costs of the screening population assigned to routine care remained lower than costs in the no-screening group for: inpatient care −€633 (95% CI −€898, −€368), primary care −€41 (95% CI −€58, −€24) and medication −€90 (95% CI −€117, −€63). The outpatient care costs were similar in both groups. As the costs of the screening programme within the routine care group were €936 per individual, and the total mean annual difference in costs between the screening and no-screening groups was −€752 (95% CI −€1,169, −€335) per individual, ADDITION brings cost savings of €2158 (95% CI €444; €3871) per individual within 5 years following the screening period. For the whole population of 13,992 individuals, the cost savings would be €30.1 million (95% CI €6.2 million, €54.1 million).

In this study, we used real-world, individual participant-level data to examine healthcare costs among participants with incident type 2 diabetes in a screened group compared with an unscreened group. We applied a healthcare sector perspective in our cost analysis. Only costs related to healthcare services and redeemed medication were included in the study. As such, costs relating to individuals’ lost labour market productivity and utilisation of nursing services or other services such as transportation and helping aids in the house were not included in our analysis. These costs have previously been demonstrated to be high among people with diabetes, making up as much as 80% of the total societal costs [21, 23]. Furthermore, we did not include quality of life or the value of higher life expectancy in our analyses. Data on quality of life would enable a better comparison with the literature on the cost-utility of screening and prevention [22, 24‐27].

We also did not include the healthcare cost savings accruing from an individual’s death, as it will always be less expensive if a person dies and hence there is no argument for treatment or prevention [18].

A limitation of our study was the non-randomised design, as we cannot eliminate the possibility of selection bias and residual confounding. Groups were well balanced for the characteristics investigated at baseline. However, our findings might have been influenced by the higher levels of education and the slightly lower levels of pre-existing chronic disease in the intervention group. The cost of redeemed medication was also lower in the screening group before the introduction of the ADDITION-Denmark screening intervention. We adjusted for age, sex, education, GP county and prevalent chronic disease at diagnosis, which impacted on the size of the cost difference, although the final result remained significant. It is likely that adjusting for county took account of some of the potential socioeconomic differences across different regions in Denmark. We tried to minimize lead- and length-time biases by comparing costs for all individuals diagnosed with diabetes in the screening and no-screening groups.

We could have applied bootstrapping and cost acceptability curves to handle uncertainty [18]. As the intervention proved to be cost-saving within a short time period, we chose instead to use the lowest CI levels to describe uncertainty.

Healthcare costs were lower among individuals with incident type 2 diabetes in a screened group compared with an unscreened group. The relatively modest cost of screening per participant discovered to have developed diabetes was offset within 2 years by savings in the healthcare system.