[The future development of dementia diseases in Germany-a comparison of different forecast models]

Potential of prevention strategies for the modifiable risk factor type 2 diabetes with relation to the future number of dementia patients in Germany- a multi-state projection through 2040

BACKGROUND

We assess the impact of prevention strategies regarding type 2 diabetes as a modifiable risk factor for dementia and its consequences for the future number of dementia patients in Germany.

METHODS

We used a random sample of health claims data (N = 250,000) of insured persons aged 50+ drawn in 2014, and data on population size and death rates in 2015 from the Human Mortality Database. Using exponential hazard models, we calculated age- and sex-specific transition probabilities and death rates between the states (no diabetes/no dementia, diabetes/no dementia, no diabetes/dementia, diabetes/dementia). In multi-state projections, we estimated the future number of dementia cases aged 75+ through 2040 depending on the development of the incidence of diabetes among persons without diabetes and without dementia, and the dementia incidence among persons with and without diabetes.

RESULTS

In 2015 there were 1.53 million people with dementia aged 75+ in Germany. A relative annual reduction in death rates of 2.5% and in dementia incidence in persons without diabetes of 1% will increase this number to 3.38 million by 2040. A relative reduction of diabetes incidence by 1% annually would decrease dementia cases by around 30,000, while a reduction of dementia incidence among people with diabetes by 1% would result in 220,000 fewer dementia cases. Both prevention strategies combined would prevent 240,000 dementia cases in 2040.

CONCLUSIONS

The increase in life expectancy is decisive for the future number of people with dementia. Strategies of better diabetes treatment have the potential to lower the increase in the number of dementia patients in the coming decades.

Future prevalence of type 2 diabetes—A comparative analysis of chronic disease projection methods

Background

Accurate projections of the future number of people with chronic diseases are necessary for effective resource allocation and health care planning in response to changes in disease burden.

Aim

To introduce and compare different projection methods to estimate the number of people with diagnosed type 2 diabetes (T2D) in Germany in 2040.

Methods

We compare three methods to project the number of males with T2D in Germany in 2040. Method 1) simply combines the sex- and age-specific prevalence of T2D in 2010 with future population distributions projected by the German Federal Statistical Office (FSO). Methods 2) and 3) additionally account for the incidence of T2D and mortality rates using partial differential equations (PDEs). Method 2) models the prevalence of T2D employing a scalar PDE which incorporates incidence and mortality rates. Subsequently, the estimated prevalence is applied to the population projection of the FSO. Method 3) uses a two-dimensional system of PDEs and estimates future case numbers directly while future mortality of people with and without T2D is modelled independently from the projection of the FSO.

Results

Method 1) projects 3.6 million male people with diagnosed T2D in Germany in 2040. Compared to 2.8 million males in 2010, this equals an increase by 29%. Methods 2) and 3) project 5.9 million (+104% compared to 2010) and 6.0 million (+116%) male T2D patients, respectively.

Conclusions

The results of the three methods differ substantially. It appears that ignoring temporal trends in incidence and mortality may result in misleading projections of the future number of people with chronic diseases. Hence, it is essential to include these rates as is done by method 2) and 3).

Use of a quantitative data report in a hypothetical decision scenario for health policymaking: a computer-assisted laboratory study

BACKGROUND

Quantitative data reports are widely produced to inform health policy decisions. Policymakers are expected to critically assess provided information in order to incorporate the best available evidence into the decision-making process. Many other factors are known to influence this process, but little is known about how quantitative data reports are actually read. We explored the reading behavior of (future) health policy decision-makers, using innovative methods.

METHODS

We conducted a computer-assisted laboratory study, involving starting and advanced students in medicine and health sciences, and professionals as participants. They read a quantitative data report to inform a decision on the use of resources for long-term care in dementia in a hypothetical decision scenario. Data were collected through eye-tracking, questionnaires, and a brief interview. Eye-tracking data were used to generate 'heatmaps' and five measures of reading behavior. The questionnaires provided participants' perceptions of understandability and helpfulness as well as individual characteristics. Interviews documented reasons for attention to specific report sections. The quantitative analysis was largely descriptive, complemented by Pearson correlations. Interviews were analyzed by qualitative content analysis.

RESULTS

In total, 46 individuals participated [students (85%), professionals (15%)]. Eye-tracking observations showed that the participants spent equal time and attention for most parts of the presented report, but were less focused when reading the methods section. The qualitative content analysis identified 29 reasons for attention to a report section related to four topics. Eye-tracking measures were largely unrelated to participants' perceptions of understandability and helpfulness of the report.

CONCLUSIONS

Eye-tracking data added information on reading behaviors that were not captured by questionnaires or interviews with health decision-makers.

Healing, surviving, or dying? - projecting the German future disease burden using a Markov illness-death model

BACKGROUND

In view of the upcoming demographic transition, there is still no clear evidence on how increasing life expectancy will affect future disease burden, especially regarding specific diseases. In our study, we project the future development of Germany's ten most common non-infectious diseases (arthrosis, coronary heart disease, pulmonary, bronchial and tracheal cancer, chronic obstructive pulmonary disease, cerebrovascular diseases, dementia, depression, diabetes, dorsal pain and heart failure) in a Markov illness-death model with recovery until 2060.

METHODS

The disease-specific input data stem from a consistent data set of a major sickness fund covering about four million people, the demographic components from official population statistics. Using six different scenarios concerning an expansion and a compression of morbidity as well as increasing recovery and effective prevention, we can show the possible future range of disease burden and, by disentangling the effects, reveal the significant differences between the various diseases in interaction with the demographic components.

RESULTS

Our results indicate that, although strongly age-related diseases like dementia or heart failure show the highest relative increase rates, diseases of the musculoskeletal system, such as dorsal pain and arthrosis, still will be responsible for the majority of the German population's future disease burden in 2060, with about 25-27 and 13-15 million patients, respectively. Most importantly, for almost all considered diseases a significant increase in burden of disease can be expected even in case of a compression of morbidity.

CONCLUSION

A massive case-load is emerging on the German health care system, which can only be alleviated by more effective prevention. Immediate action by policy makers and health care managers is needed, as otherwise the prevalence of widespread diseases will become unsustainable from a capacity point-of-view.