Seasonality in malaria transmission: implications for case-management with long-acting artemisinin combination therapy in sub-Saharan Africa

Abstract

Background: Long-acting artemisinin-based combination therapy (LACT) offers the potential to prevent recurrent malaria attacks in highly exposed children. However, it is not clear where this advantage will be most important, and deployment of these drugs is not rationalized on this basis.

Methods: To understand where post-treatment prophylaxis would be most beneficial, the relationship between seasonality, transmission intensity and the interval between malaria episodes was explored using data from six cohort studies in West Africa and an individual-based malaria transmission model. The total number of recurrent malaria cases per 1000 child-years at risk, and the fraction of the total annual burden that this represents were estimated for sub-Saharan Africa.

Results: In settings where prevalence is less than 10 %, repeat malaria episodes constitute a small fraction of the total burden, and few repeat episodes occur within the window of protection provided by currently available drugs. However, in higher transmission settings, and particularly in high transmission settings with highly seasonal transmission, repeat malaria becomes increasingly important, with up to 20 % of the total clinical burden in children estimated to be due to repeat episodes within 4 weeks of a prior attack.

Conclusion: At a given level of transmission intensity and annual incidence, the concentration of repeat malaria episodes in time, and consequently the protection from LACT is highest in the most seasonal areas. As a result, the degree of seasonality, in addition to the overall intensity of transmission, should be considered by policy makers when deciding between ACT that differ in their duration of post-treatment prophylaxis.

Fig. 1

Illustration of the effect of seasonality on timing of malaria infections, and the benefit of post-treatment prophylaxis. EIR entomological inoculation rate, PTP post-treatment prophylaxis. Simplified representation of the effect of seasonality on timing of malaria infections. Sites A and B have the same annual EIR (6 infections per person per year). However, the monthly EIR in the seasonal setting (scenario B) is twice that in the perennial setting (scenario A). The monthly EIR during the transmission season in site B is the same as a perennial setting with an annual EIR of 12 (site C). Due to post-treatment prophylaxis provided by case-management for malaria, some malaria infections that occur soon after a previous episode will be prevented. This is more likely to occur where infection rates are higher, and where malaria infections occur close together in time. In seasonal settings, the length of PTP also becomes large in relation to the length of the season. This suggests that seasonal settings may have more preventable malaria than perennial settings with an equivalent annual EIR. Additional complications dealt with in this paper include realistic seasonality patterns, heterogeneity in malaria risk between individuals, immunity (acquisition of which may differ in seasonal and non-seasonal settings) and variable duration of protection from drugs used for case-management

Fig. 2

Distribution of intervals between repeat malaria episodes from 6 sites in West Africa. Top row Niakhar, Senegal, 2003; Farafenni, The Gambia, 2003; Middle row Kati, Mali 2008–2009; Boussé, Burkina Faso, 2008–2009. Bottom row Navrongo, Ghana 2002–2003; Kintampo, Ghana 2010–2011. The mean interval between malaria episodes, in days, was 50.9 (95 % CI 49.0, 52.8) days in Niakhar, 42.3 (95 % CI 37.0, 47.5) days in Farafenni, 43.4 (95 % CI 40.1, 46.7) days in Kati, 52.4 (95 % CI 48.0, 56.7) days in Boussé, 66.1 (95 % CI 60.6, 71.6) days in Navrongo, and 74.6 (95 % CI 71.1, 78.0) days in Kintampo

Fig. 3

Markham seasonality index and interval between episodes, model fits to data. a The estimated mean interval between malaria episodes shown against the Markham seasonality index (as a measure of seasonality 0, not seasonal; 1, highly seasonal) for each of the six studies. The grey dashed line shows the line of best fit. b Markham seasonality index for each of the six studies in West Africa, against the MSI for the model-based seasonality pattern for the first administrative unit in which the site is located: Kintampo, Brong-Ahafo region; Navrongo, Upper East Region; Boussé, Kourwéogo; Kati, Koulikoro; Niakhar, Fatick; Farafenni, North Bank Division. c mean interval between malaria episodes (in days) from the six studies in West Africa, against the mean interval between episodes (in days) in model simulations for the first administrative unit in which the study site is located

Fig. 4

Markham seasonality index by first administrative area. Figure shows Markham seasonality index for larval carrying capacity (LCC) for each first administrative area (the largest administrative sub-division within country. Black areas show those with strongly bimodal seasonality patterns (see Additional file 3 for details)

Fig. 5

Model-predicted mean interval between malaria episodes versus Markham seasonality index. Mean interval between recurrent malaria episodes, according to seasonality as measured by the Markham Seasonality Index. Prevalence: modelled prevalence in 2–10 year olds 5, 10, 20, 40 and 60 %, as indicated

Fig. 6

Burden of repeat malaria episodes per 1000 children by first administrative unit across Africa. Burden of malaria episodes per 1000 children that occur within a 28 days and b 42 of a prior episode. c shows the additional burden per 1000 children that occurs between 28 and 42 days after a previous episode

Fig. 7

Percentage of malaria burden in children occurring within 28 and 42 days by first administrative unit across Africa. Fraction of all malaria episodes in children under 5 years of age predicted to occur within a 28 days and b 42 of a prior episode. c shows the difference in the overall fraction of episodes among children that occurs between 28 and 42 days after a previous episode