School of Public Health Graduate Research

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    Population-level antibiotic treatment policies in the setting of antibiotic resistance: A mathematical model of mass treatment of Helicobacter pylori in Mexico
    (SAGE, 2017-10-23) Alarid-Escudero, Fernando
    Purpose: Helicobacter pylori (H. pylori) is the strongest known risk factor for gastric cancer and peptic ulcer disease. Programs under consideration in high risk countries to prevent H. pylori- related diseases via broad population treatment could be complicated by increasing levels of antibiotic resistance (ABR). We evaluate the impact of different mass-treatment policies on H. pylori infection and ABR in Mexico using a mathematical model. Methods: We developed an age-structured, susceptible-infected-susceptible (SIS) transmission model of H. pylori infection in Mexico that included both treatment-sensitive and treatment- resistant strains. Antibiotic treatment was assumed to either clear sensitive strains or induce acquired resistance. In addition, the model included the effects of both background antibiotic use and antibiotic treatment specifically intended to treat H. pylori infection. Model parameters were derived from the published literature and estimated from primary data. Using the model, we projected H. pylori infection and resistance levels over 20 years without treatment and for three hypothetical population-wide treatment policies assumed to be implemented in 2018: (1) treat children only (2-6 year-olds); (2) treat older adults only (>40 years old); (3) treat everyone regardless of age. Clarithromycin -introduced in Mexico in 1991- was the antibiotic considered for the treatment policies. In sensitivity analyses, we considered different mixing patterns and trends of background antibiotic use. We validated the model against historical values of prevalence of infection and ABR of H. pylori. Results: In the absence of a mass-treatment policy, our model predicts infection begins to rise in 2021, mostly caused by treatment-induced resistant strains as a product of background use of antibiotics. The impact of the policies is immediate on decreasing infection but also increasing ABR (see Figure). For example, policy 3 decreases infection by 11% but increases ABR by 23% after the first year of implementation. The relative size of the decrease in infection is 50% the increase in ABR for policies 2 and 3, and 20% for policy 1. These results agree across all scenarios considered in sensitivity analysis. Conclusions: Conclusions: Mass-treatment policies have a higher effect on increasing ABR letting resistant strains take over infection. Given the high proportion of ABR at the time of the policy implementation, mass treatment strategies are not recommended for Mexico.
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    Potential bias associated with modeling the effectiveness of treatment using an overall hazard ratio
    (SAGE, 2014-10) Alarid-Escudero, Fernando
    Purpose: Clinical trials often report treatment efficacy in terms of the reduction of all-cause mortality [i.e., overall hazard ratio (OHR)], and not the reduction in disease-specific mortality [i.e., disease-specific hazard ratio (DSHR)]. Using an OHR to reduce all-cause mortality beyond the time horizon of the clinical trial may introduce bias if the relative proportion of other-cause mortality increases with age. We aim to quantify this bias. Methods: We simulated a hypothetical cohort of patients with a generic disease that increases the age-, sex-, and race-specific mortality rate (μASR) by a constant additive disease-specific rate (μDis). We assumed a DSHR of 0.75 (unreported) and an OHR of 0.80 (reported, derived from DSHR and assumptions of clinical trial population). We quantified the bias in terms of the difference in life expectancy (LE) gains with treatment between using an OHR approach to reduce all-cause mortality over a lifetime [(μASR+ μDis)xOHR] compared with using a DSHR approach to reduce disease-specific mortality [μASR+(μDis)xDSHR]. We varied the starting age of the cohort from 40 to 70 years old. Results: The OHR bias increases as DSHR decreases and with younger starting ages of the cohort. For a cohort of 60 year-old sick patients, the mortality rate under OHR approach crosses μASR at the age of 90 (see figure) and LE gain is overestimated by 0.6 years (a 3.7% increase). We also used OHR as an estimate of DSHR [μASR+(μDis)xOHR] (as the latter is not often reported). This resulted in a slight shift in the mortality rate compared to the DSHR approach (see figure), yielding in an underestimation of the LE gain. Conclusions: The use of an OHR approach to model treatment effectiveness beyond the time horizon of the trial overestimates the effectiveness of the treatment. Under an OHR approach it is possible that sick individuals at some point will face a lower mortality rate compared with healthy individuals. We recommend either to derive a DSHR from trials and use the DSHR approach, or to use the OHR as an estimate of DSHR in the model, which is a conservative assumption.