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2016, 13(5): 1011-1041. doi: 10.3934/mbe.2016028

Modeling the role of healthcare access inequalities in epidemic outcomes

1. 

Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, United States

2. 

SAL MCMSC, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, United States, United States

3. 

School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States

Received  August 2015 Revised  April 2016 Published  July 2016

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.
Citation: Oscar Patterson-Lomba, Muntaser Safan, Sherry Towers, Jay Taylor. Modeling the role of healthcare access inequalities in epidemic outcomes. Mathematical Biosciences & Engineering, 2016, 13 (5) : 1011-1041. doi: 10.3934/mbe.2016028
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show all references

References:
[1]

The Lancet Infectious Diseases, 11 (2011), 131-141. doi: 10.1016/S1473-3099(10)70223-1.  Google Scholar

[2]

$2^{nd}$ edition, Pearson Education, New Jersey, 2003. Google Scholar

[3]

Journal of Theoretical Biology, 215 (2002), 227-237. doi: 10.1006/jtbi.2001.2489.  Google Scholar

[4]

Current Trends in Technology and Sciences, 2 (2013), 253-257. Google Scholar

[5]

Mathematical Biosciences, 96 (1989), 221-238. Google Scholar

[6]

Springer, 2012. doi: 10.1007/978-1-4614-1686-9.  Google Scholar

[7]

SIAM Journal on Applied Mathematics, 56 (1996), 494-508. doi: 10.1137/S003613999325419X.  Google Scholar

[8]

Journal of Mathematical Biology, 35 (1997), 503-522. doi: 10.1007/s002850050063.  Google Scholar

[9]

SIAM Journal on Applied Mathematics, 59 (1999), 1790-1811. doi: 10.1137/S0036139997325862.  Google Scholar

[10]

Math. Biosci. Eng., 1 (2004), 361-404. doi: 10.3934/mbe.2004.1.361.  Google Scholar

[11]

National Bureau of Economic Research, 2014. Google Scholar

[12]

Mathematical and Theoretical Biology Institute archive, 2007. Google Scholar

[13]

C. Cohen, D. Horlacher and F. L. MacKellar, Is urbanization good for a nation's health,, 2010. Available from: , ().   Google Scholar

[14]

Science, American Association for the Advancement of Science, 319 (2008), 766-769. doi: 10.1126/science.1150198.  Google Scholar

[15]

Lancet, 383 (2014). Google Scholar

[16]

Sexually Transmitted Diseases, 24 (1997), 327-333. doi: 10.1097/00007435-199707000-00004.  Google Scholar

[17]

Sexually Transmitted Diseases, 29 (2002), 13-19. doi: 10.1097/00007435-200201000-00003.  Google Scholar

[18]

Journal of Urban Health, 79 (2002), S1-S12. Google Scholar

[19]

Social Science & Medicine, 60 (2005), 1017-1033. doi: 10.1016/j.socscimed.2004.06.036.  Google Scholar

[20]

PLoS Pathogens, 10 (2014). Google Scholar

[21]

Progress in Development Studies, 1 (2001), 113-137. Google Scholar

[22]

Springer, Berlin, 1984. doi: 10.1007/978-3-662-07544-9.  Google Scholar

[23]

Sexually Transmitted Infections, 79 (2003), 62-64. doi: 10.1136/sti.79.1.62.  Google Scholar

[24]

Princeton University Press, 2008.  Google Scholar

[25]

Mathematical Biosciences, 147 (1998), 207-226. doi: 10.1016/S0025-5564(97)00101-6.  Google Scholar

[26]

International Journal of Epidemiology, 37 (2008), 4-8. doi: 10.1093/ije/dym271.  Google Scholar

[27]

Journal of Mathematical Biology, 47 (2003), 547-568. doi: 10.1007/s00285-003-0235-5.  Google Scholar

[28]

Science, 300 (2003), 1966-1970. doi: 10.1126/science.1086616.  Google Scholar

[29]

Theoretical Population Biology, 60 (2001). 59-71. Google Scholar

[30]

Proceedings of the Royal Society of London. Series B: Biological Sciences, 268 (2011), 985-993. Google Scholar

[31]

Nature, 438 (2005), 355-359. Google Scholar

[32]

Social Science & Medicine, 68 (2009), 2240-2246. Google Scholar

[33]

The Lancet, 365 (2005), 1099-1104. doi: 10.1016/S0140-6736(05)74234-3.  Google Scholar

[34]

PLoS Pathogens, 9 (2013), e1003467. doi: 10.1371/journal.ppat.1003467.  Google Scholar

[35]

B. Morin, Variable susceptibility with an open population: A transport equation approach,, preprint, ().   Google Scholar

[36]

Journal of Biological Dynamics, 4 (2010), 456-477. doi: 10.1080/17513758.2010.510212.  Google Scholar

[37]

Sexually Transmitted Infections,91 (2015), 610-614. doi: 10.1136/sextrans-2014-051932.  Google Scholar

[38]

London: Allen Lane, 2009. Google Scholar

[39]

The Quarterly Journal of Economics, 131 (2016), 519-578. doi: 10.1093/qje/qjw004.  Google Scholar

[40]

Environment and Urbanization, 8 (1996), 9-30. doi: 10.1177/095624789600800211.  Google Scholar

[41]

Addison-Wesley Pub., 1994. Google Scholar

[42]

Math. Biosci., 180 (2002), 29-48. doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[43]

, United Nations, World Urbanization Prospects: The 2011 Revision,, 2012. Available from: , ().   Google Scholar

[44]

Proceedings of the Royal Society B: Biological Sciences, 274 (2007), 599-604. Google Scholar

[45]

BMJ: British Medical Journal, 314 (1997), 591-595. doi: 10.1136/bmj.314.7080.591.  Google Scholar

[46]

Social Science & Medicine, 62 (2006), 1768-1784. doi: 10.1016/j.socscimed.2005.08.036.  Google Scholar

[47]

Mathematical Biosciences, 211 (2008), 166-185. doi: 10.1016/j.mbs.2007.10.007.  Google Scholar

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