Epidemiological Modeling of the Impact of Public Health Policies on Hepatitis C: Protocol for a Gamification Tool Targeting Microelimination

Abstract

Background: Hepatitis C is a disease with a strong social component, as its main transmission route is via blood, making it associated with lifestyle. Therefore, it is suitable to be worked on from the perspective of public health policy, which still has a lot of room to explore and improve, contrary to diagnoses and treatments, which are already very refined and effective.

Objective: An interactive gamified policy tool, designated as Let's End HepC (LEHC), was created to understand the impact of policies related to hepatitis C on the disease's epidemiology on a yearly basis until 2030.

Methods: To this end, an innovative epidemiological model was developed, integrating Markov chains to model the natural history of the disease and adaptive conjoint analysis to reflect the degree of application of each of the 24 public health policies included in the model. This double imputation model makes it possible to assess a set of indicators such as liver transplant, incidence, and deaths year by year until 2030 in different risk groups. Populations at a higher risk were integrated into the model to understand the specific epidemiological dynamics within the total population of each country and within segments that comprise people who have received blood products, prisoners, people who inject drugs, people infected through vertical transmission, and the remaining population.

Results: The model has already been applied to a group of countries, and studies in 5 of these countries have already been concluded, showing results very close to those obtained through other forms of evaluation.

Conclusions: The LEHC model allows the simulation of different degrees of implementation of each policy and thus the verification of its epidemiological impact on each studied population. The gamification feature allows assessing the adequate fulfillment of the World Health Organization goals for the elimination of hepatitis C by 2030. LEHC supports health decision makers and people who practice patient advocacy in making decisions based on science, and because LEHC is democratically shared, it ends up contributing to the increase of citizenship in health.

International registered report identifier (irrid): RR1-10.2196/38521.

Keywords: hepatitis C; mobile phone; modeling; patient advocacy; public health policies.

Figure 1

Final qualitative policies of the public health tool and respective outcomes. DAA: direct-acting antiviral.

Figure 2

Schematic of the Markov model for the progression of chronic hepatitis C. C: chronically infected; DC: decompensated cirrhosis; HCC: hepatocellular carcinoma; HCV: hepatitis C virus; LM: liver-related mortality; LT: liver transplant; nc: noncirrhotic; PLT: post–liver transplant; SVR: sustained virologic response; U: uninfected.

Figure 3

Schematic of the Markov model for the cure cascade of chronic hepatitis C. C: chronically infected; D: diagnosed; ND: not diagnosed; NRC: not retained in care; NSVR1: no sustained virologic response after first treatment course; NSVR2: no sustained virologic response after second treatment course; NT: not treated; RC: retained in care; SVR: sustained virologic response; T1: first treatment course; T2: second treatment course; U: uninfected (including individuals with sustained virologic response); 1: annual incidence; 2: annual probability of diagnosis; 3: annual probability of being retained in care; 4: annual probability of treatment.

Figure 4

Conceptual model behind the quantification of individual policy measures’ impact on the cure cascade (exemplified with the annual probability of diagnosis). ACA: adaptive conjoint analysis; HCV: hepatitis C virus; PMi–i^th policy measure.