Economic epidemiology

Economic epidemiology is a field at the intersection of epidemiology and economics. Its premise is to incorporate incentives for healthy behavior and their attendant behavioral responses into an epidemiological context to better understand how diseases are transmitted. This framework should help improve policy responses to epidemic diseases by giving policymakers and health-care providers clear tools for thinking about how certain actions can influence the spread of disease transmission.

The main context through which this field emerged was the idea of prevalence-dependence, or disinhibition, which suggests that individuals change their behavior as the prevalence of a disease changes. However, economic epidemiology also encompasses other ideas, including the role of externalities, global disease commons and how individuals’ incentives can influence the outcome and cost of health interventions.

Strategic epidemiology is a branch of economic epidemiology that adopts an explicitly game theoretic approach to analyzing the interplay between individual behavior and population wide disease dynamics.

Prevalence-dependence

The spread of an infectious disease is a population-level phenomenon, but decisions to prevent or treat a disease are typically made by individuals who may change their behavior over the course of an epidemic, especially if their perception of risk changes depending on the available information on the epidemics[1] – their decisions will then have population-level consequences. For example, an individual may choose to have unsafe sex or a doctor may prescribe antibiotics to someone without a confirmed bacterial infection. In both cases, the choice may be rational from the individual’s point of view but undesirable from a societal perspective.

Limiting the spread of a disease at the population level requires changing individual behavior, which in turn depends on what information individuals have about the level of risk. When risk is low, people will tend to ignore it. However, if the risk of infection is higher, individuals are more likely to take preventive action. Moreover, the more transmissible the pathogen, the greater the incentive is to make personal investments for control.[2]

The converse is also true: if there is a lowered risk of disease, either through vaccination or because of lowered prevalence, individuals may increase their risk-taking behavior. This effect is analogous to the introduction of safety regulations, such as seatbelts in cars, which because they reduce the cost of an accident in terms of expected injury and death, could lead people to drive with less caution and the resulting injuries to nonoccupants and increased nonfatal crashes may offset some of the gains from the use of seatbelts.[2]

Prevalence-dependent behavior introduces a crucial difference with respect to the way individuals respond when the prevalence of a disease increases. If behavior is exogenous or if behavioral responses are assumed to be inelastic with respect to disease prevalence, the per capita risk of infection in the susceptible population increases as prevalence increases. In contrast, when behavior is endogenous and elastic, hosts can act to reduce their risks. If their responses are strong enough, they can reduce the average per capita risk and offset the increases in the risk of transmission associated with higher prevalence.[3][4][5][6]

Alternatively, the waning of perceived risk, either through the diminution of prevalence or the introduction of a vaccine, may lead to increases in risky behavior. For example, models suggested that the introduction of highly active antiretroviral therapy (HAART), which significantly reduced the morbidity and mortality associated with HIV/AIDS, may lead to increases in the incidence of HIV as the perceived risk of HIV/AIDS decreased.[7]

Recent analysis suggests that an individual’s likelihood of engaging in unprotected sex is related to their personal analysis of risk, with those who believed that receiving HAART or having an undetectable viral load protects against transmitting HIV or who had reduced concerns about engaging in unsafe sex given the availability of HAART were more likely to engage in unprotected sex regardless of HIV status.[8]

This behavioral response can have important implications for the timing of public interventions, because prevalence and public subsidies may compete to induce protective behavior.[9] In other words, if prevalence induces the same sort of protective behavior as public subsidies, the subsidies become irrelevant because people will choose to protect themselves when prevalence is high, regardless of the subsidy, and subsidies may not be helpful at the times when they are typically applied.

Although STDs are logical targets for examining the role of human behavior in a modeling framework, personal actions are important for other infectious diseases as well. The rapidity with which individuals reduce their contact rate with others during an outbreak of a highly transmissible disease can significantly affect the spread of the disease.[10] Even small reductions in the contact rate can be important, especially for diseases like influenza or severe acute respiratory syndrome (SARS). However, this may also have an impact on policy planning for a biological attack with a disease such as smallpox.

Individual behavioral responses to interventions for non-sexually transmitted diseases are also important. For example, mass spraying to reduce malaria transmission can reduce the irritating effects of biting by nuisance mosquitoes and so lead to reduced personal use of bednets.[6] Economic epidemiology strives to incorporate these types of behavior responses into epidemiological models to enhance a model’s utility in evaluating control measures.

Vaccination

Immunization represents a classic case of a social dilemma: a conflict of interest between the private gains of individuals and the collective gains of a society, and prevalence-dependent behavior may have significant impacts on vaccine policy formation. For instance, it was found in an analysis of the hypothetical introduction of a vaccine that would reduce (though not eliminate) the risk of contracting HIV, that individual levels of risk behavior were a significant barrier to eliminating HIV, as small changes in behavior could actually increase the incidence/prevalence of HIV, even if the vaccine were highly efficacious.[3] These results, as well as others,[11][12][13][14][15][16][17] may have contributed to a decision not to release existing semi-efficacious vaccines.[18]

An individual's self-interest and choice often leads to a vaccination uptake rate less than the social optimum as individuals do not take into account the benefit to others. In addition, prevalence dependent behavior suggests how the introduction of a vaccine may affect the spread of a disease. As the prevalence of a disease increases, people will demand to be vaccinated. As prevalence decreases, however, the incentive, and thus demand, will slacken and allow the susceptible population to increase until the disease can reinvade. As long as a vaccine is not free, either monetarily or through true or even perceived side effects,[19][20] demand will be insufficient to pay for the vaccine at some point, leaving some people unvaccinated. If the disease is contagious, it could then begin spreading again among non-vaccinated individuals. Thus, it is impossible to eradicate a vaccine-preventable disease through voluntary vaccination if people act in their own self-interest.[21][22][23]

References

  1. ↑ d'Onofrio A, Manfredi P (2010). "Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases". Journal of Theoretical Biology 256 (3): 473–478. doi:10.1016/j.jtbi.2008.10.005. PMID 18992258.
  2. 1 2 Peltzman S (1975). "The effects of automobile safety regulation". Political Economy 83: 677–726. doi:10.1086/260352.
  3. 1 2 Blower SM, McLean AR (1994). "Prophylactic vaccines, risk behavior change, and the probability of eradicating HIV in San Francisco". Science 265 (5177): 1451–1454. doi:10.1126/science.8073289. PMID 8073289.
  4. ↑ Blythe SP, Cooke KL, Castillo-Chavez C (1991). "Autonomous risk-behavior change, and non-linear incidence rate, in models of sexually transmitted diseases". Biometrics Unit Technical Report B-1048-M.
  5. ↑ Philipson, TJ; Posner, RA (1993). Private Choices and Public Health: The AIDS Epidemic in an Economic Perspective. Cambridge, MA: Harvard University Press.
  6. 1 2 Klein E, Laxminarayan R, Smith DL, Gilligan CA (2007). "Economic incentives and mathematical models of disease". Environment and Development Economics 12: 707–732. doi:10.1017/s1355770x0700383x.
  7. ↑ Blower SM, Gershengorn HB, Grant RM (2000). "A tale of two futures: HIV and antiretroviral therapy in San Francisco". Science 287 (5453): 650–654. doi:10.1126/science.287.5453.650. PMID 10649998.
  8. ↑ Crepaz N, Hart T, Marks, G (2004). "Highly Active Antiretroviral Therapy and Sexual Risk Behavior: A Meta-analytic Review". JAMA 292 (2): 224–236. doi:10.1001/jama.292.2.224. PMID 15249572.
  9. ↑ Geoffard PY, Philipson T (1996). "Rational epidemics and their public control". International Economic Review 37 (3): 603–624. doi:10.2307/2527443. JSTOR 2527443.
  10. ↑ Del Valle S, Hethcote H, Hyman JM, Castillo-Chavez C (2005). "Effects of behavioral changes in a smallpox attack model". Mathematical Biosciences 195 (2): 228–251. doi:10.1016/j.mbs.2005.03.006. PMID 15913667.
  11. ↑ Anderson R, Hanson M (2005). "Potential public health impact of imperfect HIV type 1 vaccines". The Journal of Infectious Diseases 191: S85–S96. doi:10.1086/425267. PMID 15627235.
  12. ↑ Blower SM, Ma L, Farmer P, Koenig S (2003). "Predicting the impact of antiretrovirals in resource-poor settings: preventing HIV infections whilst controlling drug resistance". Current Drug Targets. Infectious Disorders 3 (4): 345–353. doi:10.2174/1568005033480999. PMID 14754434.
  13. ↑ Blower SM, Schwartz EJ, Mills J (2003). "Forecasting the future of HIV epidemics: the impact of antiretroviral therapies and imperfect vaccines". AIDS Reviews 5 (2): 113–125. PMID 12876900.
  14. ↑ Bogard E, Kuntz KM (2002). "The impact of a partially effective HIV vaccine on a population of intravenous drug users in Bangkok, Thailand: a dynamic model". Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 29: 132–141. doi:10.1097/00042560-200202010-00004.
  15. ↑ Gray RH, Li X, Wawer MJ, Gange SJ, Serwadda D, Sewankambo NK, Moore R, Wabwire-Mangen F, Lutalo T, Quinn TC (2003). "Stochastic simulation of the impact of antiretroviral therapy andHIV vaccines onHIV transmission; Rakai, Uganda". AIDS 17 (13): 1941–1951. doi:10.1097/01.aids.0000076313.76477.fc. PMID 12960827.
  16. ↑ Hadeler KP, Castillo-Chavez C (1995). "A core group model for disease transmission". Mathematical Biosciences 128 (1-2): 41–55. doi:10.1016/0025-5564(94)00066-9. PMID 7606144.
  17. ↑ Smith RJ, Blower SM (2004). "Could disease-modifying HIV vaccines cause population-level perversity?". The Lancet Infectious Diseases 4 (10): 636–639. doi:10.1016/S1473-3099(04)01148-X. PMID 15451492.
  18. ↑ Auld MC (2003). "Choices, beliefs, and infectious disease dynamics". Journal of Health Economics 22 (3): 361–377. doi:10.1016/S0167-6296(02)00103-0. PMID 12683957.
  19. ↑ Bauch CT, Earn DJD (2004). "Vaccination and the theory of games". PNAS 101 (36): 13391–13394. doi:10.1073/pnas.0403823101. PMC 516577. PMID 15329411.
  20. ↑ d'Onofrio A, Manfredi P (2010). "Vaccine demand driven by vaccine side effects: Dynamic implications for SIR diseases". Journal of Theoretical Biology 264 (2): 237–252. doi:10.1016/j.jtbi.2010.02.007. PMID 20149801.
  21. ↑ Geoffard PY, Philipson T (1997). "Disease eradication: private versus public vaccination". American Economic Review 87: 222–230.
  22. ↑ May SR (2000). "Simple rules with complex dynamics’". Science 287 (5453): 601–602. doi:10.1126/science.287.5453.601. PMID 10691541.
  23. ↑ Bauch CT, Galvani AP, Earn DJD (2004). "Group interest versus self-interest in smallpox vaccination policy". PNAS 100 (18): 10564–10567. doi:10.1073/pnas.1731324100. PMC 193525. PMID 12920181.

Further reading

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