gems: An R Package for Simulating from Disease Progression Models

Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application

Hepatitis C virus transmission among HIV-infected men who have sex with men: Modeling the effect of behavioral and treatment interventions.

The incidence of hepatitis C virus(HCV) infections among HIV-infected men-who-have-sex- with-men(MSM) increased in recent years and is associated with high-risk sexual behavior. Behavioral interventions that target high-risk behavior associated with HCV transmission and treatment with direct-acting antivirals(DAAs) may prevent further HCV infections. We predicted the effect of behavioral and treatment interventions on HCV-incidence and -prevalence among HIV-infected MSM up to 2030 using a HCV transmission model parameterized with data from the Swiss HIV Cohort Study. We assessed behavioral interventions associated with further increase, stabilization and decrease in the size of the population with high-risk behavior. Treatment interventions included increase in treatment uptake and use of DAAs. If we assumed that without behavioral interventions high-risk behavior spread further according to the trends observed over the last decade, and that the treatment practice did not change, HCV-incidence converged to 10.7/100 person-years(py). All assessed behavioral interventions alone resulted in reduced HCV transmissions. Stabilization of high-risk behavior combined with increased treatment uptake and the use of DAAs reduced incidence by 77%(from 2.2 in 2015 to 0.5/100 py) and prevalence by 81%(from 4.8% in 2015 to 0.9%) over the next 15 years. Increasing treatment uptake was more effective than increasing treatment efficacy to reduce HCV-incidence and -prevalence. A decrease in high-risk behavior, led to a rapid decline in HCV-incidence, independent of treatment interventions. CONCLUSION Treatment interventions to curb the HCV epidemic among HIV-infected MSM are effective if high-risk behavior does not increase as it has during the last decade. Reducing high-risk behavior associated with HCV transmission would be the most effective intervention for controlling the HCV epidemic, even if this was not accompanied by an increase in treatment uptake or efficacy. This article is protected by copyright. All rights reserved

Tuberculosis in Cape Town: An age-structured transmission model.

BACKGROUND Tuberculosis (TB) is the leading cause of death in South Africa. The burden of disease varies by age, with peaks in TB notification rates in the HIV-negative population at ages 0-5, 20-24, and 45-49 years. There is little variation between age groups in the rates in the HIV-positive population. The drivers of this age pattern remain unknown. METHODS We developed an age-structured simulation model of Mycobacterium tuberculosis (Mtb) transmission in Cape Town, South Africa. We considered five states of TB progression: susceptible, infected (latent TB), active TB, treated TB, and treatment default. Latently infected individuals could be re-infected; a previous Mtb infection slowed progression to active disease. We further considered three states of HIV progression: HIV negative, HIV positive, on antiretroviral therapy. To parameterize the model, we analysed treatment outcomes from the Cape Town electronic TB register, social mixing patterns from a Cape Town community and used literature estimates for other parameters. To investigate the main drivers behind the age patterns, we conducted sensitivity analyses on all parameters related to the age structure. RESULTS The model replicated the age patterns in HIV-negative TB notification rates of Cape Town in 2009. Simulated TB notification rate in HIV-negative patients was 1000/100,000 person-years (pyrs) in children aged <5 years and decreased to 51/100,000 in children 5-15 years. The peak in early adulthood occurred at 25-29 years (463/100,000 pyrs). After a subsequent decline, simulated TB notification rates gradually increased from the age of 30 years. Sensitivity analyses showed that the dip after the early adult peak was due to the protective effect of latent TB and that retreatment TB was mainly responsible for the rise in TB notification rates from the age of 30 years. CONCLUSION The protective effect of a first latent infection on subsequent infections and the faster progression in previously treated patients are the key determinants of the age-structure of TB notification rates in Cape Town

Modelling the impact of deferring HCV treatment on liver-related complications in HIV coinfected men who have sex with men

BACKGROUND AND AIMS Hepatitis C (HCV) is a leading cause of morbidity and mortality in people who live with HIV. In many countries, access to direct acting antiviral agents to treat HCV is restricted to individuals with advanced liver disease (METAVIR stage F3 or F4). Our goal was to estimate the long term impact of deferring HCV treatment for men who have sex with men (MSM) who are coinfected with HIV and often have multiple risk factors for liver disease progression. METHODS We developed an individual-based model of liver disease progression in HIV/HCV coinfected men who have sex with men. We estimated liver-related morbidity and mortality as well as the median time spent with replicating HCV infection when individuals were treated in liver fibrosis stages F0, F1, F2, F3 or F4 on the METAVIR scale. RESULTS The percentage of individuals who died of liver-related complications was 2% if treatment was initiated in F0 or F1. It increased to 3% if treatment was deferred until F2, 7% if it was deferred until F3 and 22% if deferred until F4. The median time individuals spent with replicating HCV increased from 5 years if treatment was initiated in F2 to almost 15 years if it was deferred until F4. CONCLUSIONS Deferring HCV therapy until advanced liver fibrosis is established could increase liver-related morbidity and mortality in HIV/HCV coinfected individuals, and substantially prolong the time individuals spend with replicating HCV infection