Chronic diseases are one of this century’s greatest threats towards public health with almost epidemic prevalence increases globally and expectations of significant increases in the future [1] . Diabetes Mellitus is, with around 350 million people globally suffering from this disease [2] [3] , one of the most burdensome chronic diseases associated with major disability, reduced quality of life and shortened length of life [2] [4] .

Various factors are expected to cause future increase in the prevalence of diabetes: demographic changes [5] , sedentary life styles and obesity [6] -[8] , improved survival [9] [10] epidemiology [11] , screening efforts [12] and new morbidity patterns implying that diabetes is increasingly seen in younger ages [13] [14] . Management of the increasing diabetes population implies, among others, an economic challenge, which societies must face, as diabetes patients require increased health care, pharmaceuticals and nursing services for their remaining lifetime [4] [15] [16] . Long term models can identify where a society may be heading, providing policy makers with a foundation on which decisions concerning future strategic prioritization can be grounded [17] .

Forecasts of the burden of diabetes exist in great numbers in the literature, see for example, King et al. 1998 [18] ,Bagust et al. 2002 [19] , Huang et al. 2009 [20] , Mainous et al. 2007 [21] or Tunceli et al. 2009 [22] . Our forecasting model (the BOX-model) is an established and tested epidemiological disease model, which has proven its global applicability for different diseases with largely accurate predictions showing only nonessential deviations [9] [23] [24] . The BOX-model is simple and intuitive, based on epidemiological drivers observed over more than a decade and economic cost estimates for 2011 calculated on the individual level from national registers.

Based on a comprehensive epidemiological framework, this study forecasts diabetes attributable costs in Denmark for the period 2012-2040 according to sectors and patient’s morbidity status. Denmark has optimal conditions due to data availability, coverage of the diabetes population and richness of information in national registers [25] . In addition, Denmark is a typical European country in terms of treatment availability and population structure. The study was part of a large-scale register based on observational investigation, the Diabetes Impact Study 2013 [26] , which investigated epidemiological, health economic and socioeconomic aspects of diabetes in Denmark [11] [16] [27] .

Estimating the size of future costs attributable to diabetes, the epidemiological dynamics underlying the prevalence of diabetes must be taken into account. Each year, new patients are diagnosed, patients develop complications and yet other patients will die. These dynamic structures are appreciated in the forecasts through the underlying epidemiological framework, presented in the BOX-model.

Comparison between three contrasting scenarios was deployed. Each scenario was related to the same base year (2011) and outlines a situation specified according to observed epidemiological trends in incidence, mortality and complication progression from 1997-2011. The three scenarios represent: 1) continuation of observed epidemiological trends under the assumption that these trends will continue as historically observed (core); 2) continuation of the observed trends regarding mortality and complication rates but a constant rate of incidence as observed in 2011, reflecting the assumption that incidence will stabilize and discontinue the increase (intermediate); 3) all epidemiological drivers are kept constant on the level observed in 2011 to reflect no further improvements in mortality and morbidity among diabetes patients and no further incidence increase (constant). Scenarios are presented in Table 2.

For each scenario, the BOX-model calculates a distribution of PYRS. By adding estimates of diabetes attributable costs specific for gender and age at diagnosis, total diabetes attributable sector costs for every calendar year are arrived at.

The cost forecasts mirror the observed cost structure and level in 2011, though it is obvious that the future will not hold the same investments and treatment/cost structures as in 2011. A prerequisite for the proposed epidemiological scenarios is, therefore, to capture some structural changes and potential relevant investment cases. On the one hand, the continuation of treatment improvements as assumed in scenarios (core and intermediate) cannotbe expected without some future investments in pharmaceuticals and health care. On the other hand, the cost levels in health care, nursing and pharmaceuticals will ultimately be decided, by what is politically possible in the years to come. Hence, the challenge is to quantify implications hereof for the cost forecasts. To accommodate this in our model, we suggested a number of hypotheses representing, on one hand, potentials for freeing of resources if certain efficiency improvements are realized or of a given political or administrative initiative and, on the other hand, budget limitation or economic potentials of a given investment. Based on the Core scenario each of the hypotheses was estimated under the assumption of everything else held constant.

Hypotheses, rationale and corresponding model adjustments are described in Table 3 and Table 4.

All cost estimates are presented in 2011 EUR based on a conversion rate from DKK to EUR of 7.4647 DKK.

The point of departure for the forecasted scenarios are all centrally available data in Danish national health registers for all Danish diabetes patients in 2011 providing comprehensive estimates of real world evidenced costs attributable to diabetes forecasted according to 14 years of epidemiological data and a categorization of diabetes patients in three complication groups. This is a novel approach enabling an intuitive understanding of forecasting results as indicators of where diabetes, in a public health perspective, is heading. This study attempts to forecast trends in the future diabetes patient population and, hence, expected costs given the current resource consumption and productivity loss among diabetes patients. Model input are of highest possible quality, distinguishing the BOX-model from majority of international models based on data from population surveys, and the model has been validated showing only nonessential deviations [9] [23] [24] . Our analysis is distinct using societal attributable costs to diabetes including both resource consumption and productivity loss. Furthermore, we take into account the dynamics of diabetes and the expected natural history of disease in relation to development of late complications.

The BOX-model is general and intuitive aiming to guide decision makers as to where this disease is heading more than making accurate future projections. Trends from the forecasted scenarios may probably be generalized across countries. They indicate that increasing prevalence of diabetes and, hence, costs of diabetes are difficult to change within the shorter time span and will approximately double the next 10 years primarily due to the already achieved historic improvements in diabetes mortality and morbidity. Hereafter, the span is wider depending on the epidemiological trends occurring, however, it is realistic to assume a 2.5 or tripling of the patient population and, hence costs in 2040. Such estimates correspond well with international projections [20] . On the cost side, the predictions concerning health care, pharmaceuticals and nursing services are conditional on current rates of utilization and supply, which of course will change over time. From a societal perspective, the constant scenario can be viewed as a minimum cost under the assumption that 2011 cost structures and supplies are continued. This means that incidence rates are stable and no further progress in the health of diabetes patients in relation to morbidity or mortality occurs. This is probably unrealistic expectations, however,it sets the frame for comparison with the core scenario where the difference in costs (2.8 billion EUR in 2040) reflects the amount of extra resources necessary, if prevalence increases continues as observed until 2011. The intersmediate scenario compared to the core reflects the general public health expectation that primary prevention will result in stable or decreased incidence rates compared to historic trends. If this succeeds, a 25% reduction in costs can be expected in 2040 compared to the costs in the core scenario.

We believe that our estimations present intuitive understandable perspectives valuable for decision makers, for instance, for the health care system to be ready to meet this chronic disease challenge of a doubling in resource demand already in 2025 under current structures. With estimation of economic potentials to the core scenario, we aim to highlight how the scenarios can guide cost effectiveness discussions. For instance, interventions aiming to shift treatment of diabetes patients from secondary care to primary care can be compared to the threshold of around 500 million EUR in 2025 freed, if a goal of an annual 2.5% decrease is reached. In comparison, a 5% increased investment in primary care will cost an amount in the range of 45 million EUR in 2025. We do not argue for a given causal effect of a specific intervention, but merely point out the economic potentials if suggested goals were reached or specific investments were made.

Another important conclusion is that prevalence is a poor measure of disease control when it comes to chronic diseases. Lower cost per patient year might be more desirable than lower prevalence as this means that each patient is living better with his or her disease contributing to a larger prevalent population. Categorization of patients according to their complication status in three groups is a novel approach, which allows a more general view on the disease, which is easy to interpret and communicate. We have previously shown how health care costs and nursing costs increased markedly when patients with diabetes develop minor or major complications. Hence, there is great cost saving potential in preventing development of complications among patients with diabetes. This is reflected in the intermediate scenario, where focus is placed on efforts to sustain historic improvement in epidemiological indicators into the future, but incidence rates are assumed constant. We further project a shift in resource consumption from patients with major complications to patients without complications due to the volume of patients living with diabetes without complications in the future.

It must be stressed that the basic patient population in the scenarios has obtained its size and age composition as a consequence of access to diabetes treatment and care during decades prior to year 2001. Therefore, a comparison of PYRS experienced under competing scenarios reflects the cumulative effect of access to treatment over previous decades. In prolonging of this, it is important to bear in mind that costs are an expression of supply and demand meaning that patients’ demand will only increase to the extent that the supply is available. In the model, discrete time intervals of one calendar year are used and not continuous time reflecting our wish for a simple and intuitive modeling approach. Age at diagnosis, and not running age, was used to reflect that the model follows a patient with diabetes from diagnose until death concerning age and gender specific costs and morbidity and mortality drivers. Forecasting 25 years ahead in time it is obvious that changes over time, in health care queues, waiting lists and treatment offers cannot be accommodated for in the model, as these are unknown. It is inevitable that modelers will make different choices and apply different assumptions. The included hypotheses can throw light on consequences of different assumptions, however, the model will never be a perfect representation of the real world [42] .

Our projections indicate that within the shorter time span increases in the prevalent population, and therefore the associated cost, are difficult to change primarily due to the already achieved historic improvements in diabetes mortality and morbidity. These will approximately double societal costs of diabetes the next 10 years,assuming current trends in morbidity and mortality are maintained.The resulting diabetes population will incur three times the current costs in 2040, although the costs per PYRS are falling during the whole period.A 20% reduction in cost per PYRS shows how the distribution of patients with complications are expected to change over time with patients living better and, hence, on average become less resource demanding with their disease. Prevalence is, therefore, a poor measure of disease control in a public health perspective. With marked increases in diabetes prevalence, not only resource demand for health care, nursing and pharmaceuticals will increase but also societal productivity loss due to the increasing number of patients in the working age.Despite wide uncertainty around projections of the future, they enable us to appreciate better the implications for societies of currently observed epidemiological trends. Hereby, projections provide a basis for discussing future resource demand and consequently the necessary investments and structural changes.

This study has been conducted by ApEHR in cooperation with the Danish Diabetes Association and supported by a PhD program from COHERE, funded by The Danish Centre for Strategic Research in Type 2 Diabetes, DD2. A consortium of sponsors from the pharmaceutical industry comprising Astra Zeneca/BMS, Novo Nordisk, Merck, Sanofi Aventis and Bayer has provided an unrestricted grant to ApEHR for the conduct of this research.We thank professor Kristian Bolin for useful commenting.

Neither the Danish Diabetes Association nor the consortium of sponsors from the pharmaceutical industry has had any influence on the conduct of the study.

This study was conducted on behalf of the Danish Diabetes Association and supported by a PhD program at COHERE supported by the Danish Centre for Strategic Research in Type 2 Diabetes, DD2. We thank Mrs. Sabrina I. Imeroski for editorial assistance. We thank professor Kristian Bolin for useful commenting.

CamillaSortsø,MarthaEmneus,AndersGreen,Peter BjødstrupJensen,ThomasEriksson, (2015) Societal Costs of Diabetes Mellitus 2025 and 2040—Forecasts Based on Real World Cost Evidence and Observed Epidemiological Trends in Denmark. Modern Economy,06,1150-1166. doi: 10.4236/me.2015.610109

CG0: Complication group 0 (no complications)

CG1: Complication group 1 (minor complications)

CG2: Complication group 2 (minor complications)

M: Men

PIN: Danish Personal Identification Number

PP: Per person

PYRS: Patient Years

PWD: Patients with Diabetes

SD: Statistics Denmark

W: Women

^aValue indicates classification state (0, 1 or 2, respectively). ^bValues 1 and 0 indicate that item is specific for diabetes and unspecific for diabetes, respectively.