A systematic review of scabies transmission models and data to evaluate the cost-effectiveness of scabies interventions

Abstract

Background: Scabies is a common dermatological condition, affecting more than 130 million people at any time. To evaluate and/or predict the effectiveness and cost-effectiveness of scabies interventions, disease transmission modelling can be used.

Objective: To review published scabies models and data to inform the design of a comprehensive scabies transmission modelling framework to evaluate the cost-effectiveness of scabies interventions.

Methods: Systematic literature search in PubMed, Medline, Embase, CINAHL, and the Cochrane Library identified scabies studies published since the year 2000. Selected papers included modelling studies and studies on the life cycle of scabies mites, patient quality of life and resource use. Reference lists of reviews were used to identify any papers missed through the search strategy. Strengths and limitations of identified scabies models were evaluated and used to design a modelling framework. Potential model inputs were identified and discussed.

Findings: Four scabies models were published: a Markov decision tree, two compartmental models, and an agent-based, network-dependent Monte Carlo model. None of the models specifically addressed crusted scabies, which is associated with high morbidity, mortality, and increased transmission. There is a lack of reliable, comprehensive information about scabies biology and the impact this disease has on patients and society.

Discussion: Clinicians and health economists working in the field of scabies are encouraged to use the current review to inform disease transmission modelling and economic evaluations on interventions against scabies.

Fig 1. Systematic review flow diagram.

Fig 2. Scabies transmission model.

Abbreviations: CI & DP, case identification and diagnostic processes; CSd, crusted scabies diagnosed; CSu, crusted scabies undiagnosed; CSt, crusted scabies treatment; I, infectious; R, recovered; S1, susceptible (never has been scabies infected); S2, susceptible (after prior scabies infection); SSd, simple scabies diagnosed; SSu, simple scabies undiagnosed; SSt, simple scabies treatment. The figure shows a susceptible population (S1) of individuals who can be infected (I) with scabies. The probability of infection depends on the interaction between individuals with other, infected, members of their household or community reflected by the network diagrams. Infected patients develop simple scabies (ISS), which is initially undiagnosed (SSu). Case identification and diagnostic processes (CI & DP) determine the probability that a patient becomes diagnosed (SSd). Subsequently, there is a probability that a patient takes up treatment and moves to SSt. Treatment has a probability of success, dependent on treatment efficacy and compliance. In this case, patients are cured and susceptible for reinfection (S2), the probability and infectiousness of which can differ from the probability and infectiousness in individuals who never had scabies before. Re-infection or sustained infection after unsuccessful treatment can go undiagnosed if a patient does not receive appropriate follow-up. A proportion of patients with simple scabies will develop CS. Grade 1 CS (CS1) can progress to grade 2 CS (CS2) and grade 3 CS (CS3). While both simple scabies and CS can result in complications, the probability of this is higher in patients with CS and increases with higher grade CS. Complications can result in death or can resolve. “Background mortality” reflects people who die from other causes than CS, which can differ between the susceptible and infected populations due to differences in comorbidities. In case the model is used to inform an economic evaluation, each of the health states (S1, SSu, SSd, SSt, CSu (grades 1–3), CSd (grades 1–3), CSt (grades 1–3) and S2) is associated with a cost and a utility. Diagnostics, treatments and complications can be associated with additional costs and/or (dis)utilities. The model can be used to simulate a community and how individuals move through the various health states over time, accruing costs, life-years and utility based on the time they spend in each of the health states. Results can be expressed in terms of costs per quality-adjusted life year (QALY) gained, or any other outcome captured in the model (e.g. cost per reinfection prevented or cost per life year gained).