Abstract
BACKGROUND: In Czechia, only about a quarter of people suffering from the Alzheimer's disease (AD) receive (usually belated) treatment. Because of their more rapid cognitive decline, untreated patients require extensive assistance with basic daily activities earlier than those receiving treatment. This assistance provided at home and nursing homes represents a substantial economic burden.
AIMS OF THE STUDY: To calculate lifetime costs of care per AD patient and to evaluate potential care savings from early treatment.
METHODS: We use Monte Carlo simulation to model lifetime societal costs of care per patient under two different scenarios. In the first one, a cohort of 100,000 homogeneous patients receives usual care under which the majority of patients are undiagnosed or diagnosed late. The second scenario models a hypothetical situation in which an identical cohort of patients starts receiving treatment early after the disease onset. Data on the rates of cognitive decline for treated and untreated patients, and survival probability for AD patients are derived from foreign clinical studies. Information on costs and population characteristics is compiled on the basis of published Czech research and databases.
RESULTS: Early treatment of AD decreases social lifetime costs of care. This result holds true regardless of gender, age at which the disease is contracted, or whether the patient lives at home or uses a social residential service. The potential savings amount up to Euro 26,800 (23,500) per woman (man), being negatively correlated with the age at which the disease onsets as well as the delay between the onset and treatment initiation DISCUSSION: The results suggest that early treatment of AD would decrease costs of care in Czechia. The main limitation of the simulation arises from the fact that missing domestic information was substituted by input from foreign clinical trials or simplifying assumptions. Because of insufficient data, we do not model hospitalization risk; on the other hand, introduction of this risk into our model would likely increase the savings from early treatment.
IMPLICATIONS FOR HEALTH POLICIES: Makers of AD policies ought to appreciate the trade-off between costs of daily assistance in untreated patients and health care costs in treated patients, notwithstanding that the costs of assistance are largely born by households rather than public budgets. Our results show that the savings on costs of assistance brought about by early treatment would exceed the additional costs of treatment.
IMPLICATIONS FOR FURTHER RESEARCH: A number of missing or insufficient data about the Czech Alzheimer's population were identified. In addition, to determine the total societal cost-effect of early treatment, further research ought to evaluate the related increase in detection costs. Finally, it should also assess cost-effectiveness of early treatment by considering its impact on patients' utility.
Original language | English |
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Pages (from-to) | 147-161 |
Number of pages | 15 |
Journal | The Journal of Mental Health Policy and Economics |
Volume | 21 |
Issue number | 4 |
Publication status | Published - 1 Dec 2018 |
Externally published | Yes |
Funding
* Correspondence to: Hana M. Broulíková, University of Economics, Prague. Department of Statistics and Probability, University of Economics; W. Churchill Sq. 1938/4, 130 67 Prague, Czech Republic. Tel.: +4-206-0792 0481 E-mail: [email protected] Source of Funding: Supported by the grant No. F4/38/2017 from the Internal Grant Agency of the University of Economics (direct funding), Prague and the grant No. 402/12/G097 DYME – Dynamic Models in Economics from the Czech Science Foundation (indirect funding).
Funders | Funder number |
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University of Economics | 402/12/G097 DYME |
Grantová Agentura České Republiky |
Keywords
- Aged
- Aged, 80 and over
- Alzheimer Disease/diagnosis
- Cost of Illness
- Czechoslovakia
- Early Diagnosis
- Early Medical Intervention/economics
- Female
- Health Care Costs/statistics & numerical data
- Home Care Services/economics
- Humans
- Male
- Middle Aged
- Models, Economic
- Monte Carlo Method
- National Health Programs/economics
- Nursing Homes/economics