Antibiotic drug-resistance as a complex system driven by socio-economic growth and antibiotic misuse

Abstract

Overwhelming antibiotic use poses a serious challenge today to the public-health policymakers worldwide. Many empirical studies pointed out this ever-increasing antibiotic consumption as primary driver of the community-acquired antibiotic drug-resistance, especially in the middle- and lower-income countries. The association is well documented across spatio-temporal gradients in many parts of the world, but there is rarely any study that emphasizes the mechanism of the association, which is important for combating drug-resistance. Formulating a mathematical model of emergence and transmission of drug-resistance, we in this paper, present how amalgamating three components: socio-economic growth, population ecology of infectious disease, and antibiotic misuse can instinctively incite proliferation of resistance in the society. We show that combined impact of economy, infections, and self-medication yield synergistic interactions through feedbacks on each other, presenting the emergence of drug-resistance as a self-reinforcing cycle in the population. Analysis of our model not only determines the threshold of antibiotic use beyond which the emergence of resistance may occur, but also characterizes how fast it develops depending on economic growth, and lack of education and awareness of the population. Our model illustrates that proper and timely government aid in population health can break the self-reinforcing process and reduce the burden of drug-resistance in the community.

Figure 1

Antibiotic drug-resistance as an integrated system driven by population ecology of infectious disease, socio-economic growth of population and antibiotic consumption by individuals in the population.

Figure 2

Schematic of the model. For detail explanation, see the Supplementary Information.

Figure 3

Impact of economic status (h(0)) on the development of the drug-resistance. (a) Time series of resistant strain for different values of h(0) - lower income drives early development of z(t). (b) Stabilization time, (c) income reduction, and (d) increase in antibiotic consumption when h(0) varies 0.05–1.5. Low income introduce more self medication, that drives early development of resistance, which in turn reduces more capital. Drug-resistance develops relatively quickly in low-income countries and so higher income reduction. For detail explanation, see the text.

Figure 4

Stabilization time of emergence of resistant strain under different volume and duration of antibiotic consumption. The volume of antibiotic use corresponds to the area inside the triangle for each h₀. When both volume and duration are high, the resistance develops very quickly, but volume of consumption has larger impact than the duration on the emergence process. For details, see the text.

Figure 5

(a) Impact of government aid to control the drug-resistance. While the dotted curve depicts the dynamics of system before implementation of aid, the bold curve represents the effect after implementing the aid by reducing the cost of treatment c_z. The original cost of treatment is assumed c_z = 12. The government provides aid at the 5^th month onward, then (i) curve of resistant population started declining, (ii) income curve rises up (iii) antibiotic consumption curve goes down. (b) Plot of area bounded by the dotted and bold curves in respective figures. Along with baseline values, other parameter values used for this simulation are $R_o=\mathrm{7,}{\delta }_1=\mathrm{0.11,}{\delta }_2=\mathrm{2,}{h}_o=\mathrm{7,}\hat{a}=\mathrm{2,}\tilde{a}=\mathrm{0.1,}{r}_h^c=\mathrm{0.9,}{r}_l^c=\mathrm{0.9,}\rho =\mathrm{0.07.}$ For details, see the text.

Figure 6

Resistance and gross National income percapita (GNIP). (a) Clusters of Klebsiella sp. data and model estimation. (b) Clusters of E. Coli data and model estimation. Refer the Fig. S1. The black cross mark and curve represents the HIC and red cross mark and curve represents the LIC. For details, see the main text.