Perennial malaria chemoprevention with and without malaria vaccination to reduce malaria burden in young children: a modelling analysis

Abstract

Background: A recent WHO recommendation for perennial malaria chemoprevention (PMC) encourages countries to adapt dose timing and number to local conditions. However, knowledge gaps on the epidemiological impact of PMC and possible combination with the malaria vaccine RTS,S hinder informed policy decisions in countries where malaria burden in young children remains high.

Methods: The EMOD malaria model was used to predict the impact of PMC with and without RTS,S on clinical and severe malaria cases in children under the age of two years (U2). PMC and RTS,S effect sizes were fit to trial data. PMC was simulated with three to seven doses (PMC-3-7) before the age of eighteen months and RTS,S with three doses, shown to be effective at nine months. Simulations were run for transmission intensities of one to 128 infectious bites per person per year, corresponding to incidences of < 1 to 5500 cases per 1000 population U2. Intervention coverage was either set to 80% or based on 2018 household survey data for Southern Nigeria as a sample use case. The protective efficacy (PE) for clinical and severe cases in children U2 was calculated in comparison to no PMC and no RTS,S.

Results: The projected impact of PMC or RTS,S was greater at moderate to high transmission than at low or very high transmission. Across the simulated transmission levels, PE estimates of PMC-3 at 80% coverage ranged from 5.7 to 8.8% for clinical, and from 6.1 to 13.6% for severe malaria (PE of RTS,S 10-32% and 24.6-27.5% for clinical and severe malaria, respectively. In children U2, PMC with seven doses nearly averted as many cases as RTS,S, while the combination of both was more impactful than either intervention alone. When operational coverage, as seen in Southern Nigeria, increased to a hypothetical target of 80%, cases were reduced beyond the relative increase in coverage.

Conclusions: PMC can substantially reduce clinical and severe cases in the first two years of life in areas with high malaria burden and perennial transmission. A better understanding of the malaria risk profile by age in early childhood and on feasible coverage by age, is needed for selecting an appropriate PMC schedule in a given setting.

Keywords: EMOD; Malaria chemoprevention; Malaria modeling; Malaria prevention; Malaria vaccine; Mathematical modeling; Nigeria; PMC; RTS,S.

Fig. 1

Modelled intervention efficacies, intervention schedules, and simulated cohort populations. A Intervention efficacy of modelled SP. Simulations ran with an EIR of 32 infectious bites per person per annum (ibpa), 60% effective clinical treatment coverage, and 95% coverage of a single dose of PMC with SP. Reference points are smoothed estimates based on the averaged effect of four doses in children less than 15 months in Ghana 2005 trial [33, 34]. B Age schedules of PMC and RTS,S deployments. C Intervention efficacy of the modelled malaria vaccine RTS,S [29] at 100% coverage. Simulations ran with an EIR of 11 ibpa and 90% treatment coverage of clinical cases. Data points correspond to RTS,S Phase 3 trial data from the Kintampo trial site [13], obtained from [27]. D Schematic of birth cohorts (n = 12) in simulation setup, truncated at five of ten follow-up years. Colored points indicate events when individuals receive either PMC, RTS,S, or both interventions. The inset figure shows the transmission seasonality relative to birth month of each cohort

Fig. 2

Projected clinical and severe malaria cases and cases averted by PMC-3 and/or RTS,S. A Clinical and severe malaria cases per 1000 population per year by age without either RTS,S or PMC-3. Projections were smoothed using a 3-week rolling average. B Clinical and severe malaria cases per 1000 population per year by age with PMC-3 or RTS,S at 80% coverage. The arrows indicate the timing of each PMC dose, or the 3rd RTS,S priming dose plus booster dose. C Clinical and severe cases averted per 1000 population per year in children U2 by transmission intensity with PMC-3, RTS,S, or both PMC-3 and RTS,S. D Percent reduction in clinical and severe cases in children U2 by transmission intensity and intervention. A–D The lines show the average across birth cohorts and stochastic replications, and the shaded areas the 90% prediction interval

Fig. 3

Relative importance of first PMC dose in PMC-3 when omitted or shifted by transmission intensity and disease severity. Projected relative reduction in clinical cases (top) and severe cases (bottom) in children under the age of 2 years. PMC-3 was simulated with 80% coverage for each dose. Prediction intervals were truncated at − 10%

Fig. 4

Predicted impact on malaria case incidence of PMC schedule with and without RTS,S in children under the age of two years at high transmission intensity. A Clinical and severe case incidence per 1000 population per year by age in the absence of PMC or RTS,S (gray line) or with PMC at various schedules or RTS,S, at 80% coverage. The solid line shows median and shaded area shows 90% PI. B Number of cases averted per 1000 population per year by PMC-RTS,S scenario at 80% coverage for children 0–1 and 1–2 years and U2 (0–2 years). The bar shows median and the error bars show 90% PI (relative reductions: Additional file 1: Fig A1.2.8). C Impact on clinical and severe cases of PMC-RTS,S scenarios at varying coverage levels, with median number of cases averted per 1000 population per year on the primary, y-axis and PE on the secondary y-axis. Projections varying by levels of transmission shown in Additional file 1: Fig A1.2.9

Fig. 5

Projected intervention impact of PMC and/or RTS,S at operational and target coverage for Southern Nigeria. A Nigerian States with PMC-eligible areas (n = 20 States). B–E Maps of Southern Nigeria showing (B) PfPR in children U5 according to rapid diagnostic test from the NDHS 2018; (C) simulated clinical cases per 1000 population per year in children U2; (D) coverage of first PMC dose by State; (E) predicted relative reduction in clinical cases in children U2 by State. F Coverage by potential PMC touchpoint, showing mean and range across 20 States in Southern Nigeria. See "Methods" section on estimation process for likely PMC coverage. G Annual clinical cases averted in children U2 at operational coverage and at target coverage (80%) for five PMC and/or RTS,S scenarios