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| Title |
Advancing the application of systems thinking in
health: why cure crowds out prevention |
| Author(s) |
David Bishai, Ligia Paina, Qingfeng Li,et al - Personal Name
|
Subject |
Health System and Policy |
| Publisher |
Bio Med Central Ltd |
| Publishing Year |
2014 |
| Specific Detail Info |
Introduction: This paper presents a system dynamics computer simulation model to illustrate unintended
consequences of apparently rational allocations to curative and preventive services.
Methods: A modeled population is subject to only two diseases. Disease A is a curable disease that can be
shortened by curative care. Disease B is an instantly fatal but preventable disease. Curative care workers are
financed by public spending and private fees to cure disease A. Non-personal, preventive services are delivered
by public health workers supported solely by public spending to prevent disease B. Each type of worker tries to tilt the
balance of government spending towards their interests. Their influence on the government is proportional to their
accumulated revenue.
Results: The model demonstrates effects on lost disability-adjusted life years and costs over the course of several
epidemics of each disease. Policy interventions are tested including: i) an outside donor rationally donates extra
money to each type of disease precisely in proportion to the size of epidemics of each disease; ii) lobbying is eliminated;
iii) fees for personal health services are eliminated; iv) the government continually rebalances the funding for prevention
by ring-fencing it to protect it from lobbying.
The model exhibits a “spend more get less” equilibrium in which higher revenue by the curative sector is used to
influence government allocations away from prevention towards cure. Spending more on curing disease A leads
paradoxically to a higher overall disease burden of unprevented cases of disease B. This paradoxical behavior of the
model can be stopped by eliminating lobbying, eliminating fees for curative services, and ring-fencing public health
funding.
Conclusions: We have created an artificial system as a laboratory to gain insights about the trade-offs between curative
and preventive health allocations, and the effect of indicative policy interventions. The underlying dynamics of this
artificial system resemble features of modern health systems where a self-perpetuating industry has grown up around
disease-specific curative programs like HIV/AIDS or malaria. The model shows how the growth of curative care services
can crowd both fiscal and policy space for the practice of population level prevention work, requiring dramatic
interventions to overcome these trends. |
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