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Modeling the role of healthcare access inequalities in epidemic outcomes

1. Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA
2. SAL MCMSC, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ
3. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ

## Abstract    Related pages

Urban areas, with large and dense populations, offer conditions that favor the emergence and spread of certain infectious diseases. One common feature of urban populations is the existence of large socioeconomic inequalities which are often mirrored by disparities in access to healthcare. Recent empirical evidence suggests that higher levels of socioeconomic inequalities are associated with worsened public health outcomes, including higher rates of sexually transmitted diseases (STD's) and lower life expectancy. However, the reasons for these associations are still speculative. Here we formulate a mathematical model to study the effect of healthcare disparities on the spread of an infectious disease that does not confer lasting immunity, such as is true of certain STD's. Using a simple epidemic model of a population divided into two groups that differ in their recovery rates due to different levels of access to healthcare, we find that both the basic reproductive number ($\mathcal{R}_{0}$) of the disease and its endemic prevalence are increasing functions of the disparity between the two groups, in agreement with empirical evidence. Unexpectedly, this can be true even when the fraction of the population with better access to healthcare is increased if this is offset by reduced access within the disadvantaged group. Extending our model to more than two groups with different levels of access to healthcare, we find that increasing the variance of recovery rates among groups, while keeping the mean recovery rate constant, also increases $\mathcal{R}_{0}$ and disease prevalence. In addition, we show that these conclusions are sensitive to how we quantify the inequalities in our model, underscoring the importance of basing analyses on appropriate measures of inequalities. These insights shed light on the possible impact that increasing levels of inequalities in healthcare access can have on epidemic outcomes, while offering plausible explanations for the observed empirical patterns.
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Citation: Oscar Patterson-Lomba, Muntaser Safan, Sherry Towers, Jay Taylor. Modeling the role of healthcare access inequalities in epidemic outcomes. Mathematical Biosciences and Engineering, 2016, 13(5): 1011-1041. doi: 10.3934/mbe.2016028

References

• 1. The Lancet Infectious Diseases, 11 (2011), 131-141.
• 2. $2^{nd}$ edition, Pearson Education, New Jersey, 2003.
• 3. Journal of Theoretical Biology, 215 (2002), 227-237.
• 4. Current Trends in Technology and Sciences, 2 (2013), 253-257.
• 5. Mathematical Biosciences, 96 (1989), 221-238.
• 6. Springer, 2012.
• 7. SIAM Journal on Applied Mathematics, 56 (1996), 494-508.
• 8. Journal of Mathematical Biology, 35 (1997), 503-522.
• 9. SIAM Journal on Applied Mathematics, 59 (1999), 1790-1811.
• 10. Math. Biosci. Eng., 1 (2004), 361-404.
• 11. National Bureau of Economic Research, 2014.
• 12. Mathematical and Theoretical Biology Institute archive, 2007.
• 13. 2010. Available from: http://epc2010.princeton.edu/papers/100012.
• 14. Science, American Association for the Advancement of Science, 319 (2008), 766-769.
• 15. Lancet, 383 (2014).
• 16. Sexually Transmitted Diseases, 24 (1997), 327-333.
• 17. Sexually Transmitted Diseases, 29 (2002), 13-19.
• 18. Journal of Urban Health, 79 (2002), S1-S12.
• 19. Social Science & Medicine, 60 (2005), 1017-1033.
• 20. PLoS Pathogens, 10 (2014).
• 21. Progress in Development Studies, 1 (2001), 113-137.
• 22. Springer, Berlin, 1984.
• 23. Sexually Transmitted Infections, 79 (2003), 62-64.
• 24. Princeton University Press, 2008.
• 25. Mathematical Biosciences, 147 (1998), 207-226.
• 26. International Journal of Epidemiology, 37 (2008), 4-8.
• 27. Journal of Mathematical Biology, 47 (2003), 547-568.
• 28. Science, 300 (2003), 1966-1970.
• 29. Theoretical Population Biology, 60 (2001). 59-71.
• 30. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268 (2011), 985-993.
• 31. Nature, 438 (2005), 355-359.
• 32. Social Science & Medicine, 68 (2009), 2240-2246.
• 33. The Lancet, 365 (2005), 1099-1104.
• 34. PLoS Pathogens, 9 (2013), e1003467.
• 35. preprint, arXiv:1310.1648.
• 36. Journal of Biological Dynamics, 4 (2010), 456-477.
• 37. Sexually Transmitted Infections,91 (2015), 610-614.
• 38. London: Allen Lane, 2009.
• 39. The Quarterly Journal of Economics, 131 (2016), 519-578.
• 40. Environment and Urbanization, 8 (1996), 9-30.
• 42. Math. Biosci., 180 (2002), 29-48.
• 43. 2012. Available from: http://www.un.org/en/development/desa/publications/world-urbanization-prospects-the-2011-revision.html.
• 44. Proceedings of the Royal Society B: Biological Sciences, 274 (2007), 599-604.
• 45. BMJ: British Medical Journal, 314 (1997), 591-595.
• 46. Social Science & Medicine, 62 (2006), 1768-1784.
• 47. Mathematical Biosciences, 211 (2008), 166-185.