How to Predict Drug Expenditure: A Markov Model Approach with Risk Classes.

BACKGROUND: Although pharmaceutical expenditures have been rising for decades, the question of their drivers remains unclear, and long-term projections of pharmaceutical spending are still scarce. We use a Markov approach considering different cost-risk groups to show the possible range of future drug spending in Germany and illustrate the influence of various determinants on pharmaceutical expenditure. METHODS: We compute different medium and long-term projections of pharmaceutical expenditure in Germany up to 2060 and compare extrapolations with constant shares, time-to-death scenarios, and Markov modeling based on transition probabilities. Our modeling is based on data from a large statutory sickness fund covering around four million insureds. We divide the population into six risk groups according to their share of total pharmaceutical expenditures, determine their cost growth rates, survival and transition probabilities, and compute different scenarios related to changes in life expectancy or spending trends in different cost-risk groups. RESULTS: If the spending trends in the high-cost groups continue, per-capita expenditure will increase by over 40% until 2040. By 2060, pharmaceutical expenditures could more than double, even if these groups would not benefit from rising life expectancy. By contrast, the isolated effect of demographic change would "only" lead to a long-term increase of around 15%. CONCLUSION: The long-term development of pharmaceutical spending in Germany will depend mainly on future expenditure and life expectancy trends of particularly high-cost patients. Thus, appropriate pricing of new expensive pharmaceuticals is essential for the sustainability of the German healthcare system.

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.

[The future development of dementia diseases in Germany-a comparison of different forecast models]. / Die zukünftige Entwicklung von Demenzerkrankungen in Deutschland ­ ein Vergleich unterschiedlicher Prognosemodelle.

Dementia is one of the most frequent diseases of people aged 65 and older. As a result of the upcoming demographic transition, a significant increase is expected to the current number of around 1.7 million dementia patients. A precise estimate of this increase is especially important for decision-makers and payers to the health-care system. This study examined the effects of different assumptions on the future frequency of disease using a time-discrete Markov model with population-related and disease-specific components. Based on health insurers' administrative data from AOK Baden-Württemberg, we determined age- and gender-specific prevalence rates, incidence rates, and mortality differences of dementia patients and combined them with demographic components from German population statistics. As a result, our Markov model showed a 20 to 25% higher number of dementia patients in 2030, compared to the results of the status quo projection applied in most previous studies, with the assumption of constant prevalence rates over time. Hence, our results indicate that even in the medium term payers will have to face significant increases in dementia-related health expenditures. By 2060, the number of dementia patients in Germany would rise to 3.3 million assuming a further increase to life expectancy and constant incidence rates over time. The assumption of a compression of the morbidity would reduce this number to 2.6 million.