The global burden of malaria has decreased over the last decade. Many countries now aim to banish malaria. One obstacle to elimination is people who carry malaria parasites without showing symptoms. These asymptomatic people are unlikely to be diagnosed and treated and may contribute to further spread of malaria. One way to clear all malaria infections would be to ask everyone in a community to take antimalarial drugs at the same time, even if they do not feel ill. This tactic is most likely to work in communities that are already reducing malaria infections by other means. For example, by treating symptomatic people and using bed nets to prevent bites from malaria-infected mosquitos.

Several studies have shown that mass drug administration is a promising approach to reduce malaria infections. But its success depends on enough people participating. If enough community members take antimalarial drugs, then even those who cannot participate, such as young children or pregnant women, should be less likely to get malaria. This is called the herd effect.

Now, Parker et al. demonstrate that mass antimalarial drug administration reduces infections with malaria caused by the parasite Plasmodium falciparum. The analysis looked at malaria infections among residents of four villages in the Kayin State of Myanmar that used mass antimalarial drug administration. People who lived in neighborhoods with high participation in mass drug administration were almost three times less likely to get malaria than people who lived in communities with low participation. Even people who did not take part benefited.

The analysis suggests that mass antimalaria drug administration benefits individuals and their communities if enough people take part. To be successful, malaria elimination programs that wish to use mass drug administration should approach communities in a way that encourages high levels of participation.

Mass drug administration (MDA) is the provision of medications to entire target populations and the approach has been used for many infectious diseases, including lymphatic filariasis, soil-transmitted helminths, onchocerciasis, schistosomiasis, and trachoma (Keenan et al., 2013). MDA has historically been used for P. falciparum malaria (Poirot et al., 2013) and has recently been trialed in several locations in Africa (Gitaka et al., 2017; Mwesigwa et al., 2018; Shekalaghe et al., 2011) and Asia (Manning et al., 2018; Tripura et al., 2018; Nguyen et al., 2018; Pongvongsa et al., 2018; Landier et al., 2017a). It is being considered by several nations as a tool (to be used in unison with other interventions) for elimination (World Health Organization, 2017; Zuber and Takala-Harrison, 2018), and has already been implemented as an operational strategy in at least one modern setting (Parker et al., 2017; Landier et al., 2018a).

Given that drug pressure (through provision of antimalarial drugs) provides a survival advantage for resistant parasites, there has been some hesitance in using MDA for malaria. One historical malaria eradication campaign relied on the inclusion of sub-therapeutic levels of antimalarials distributed in table salt across large populations (Pinotti et al., 1955). This program likely led to the emergence of parasite resistance in the same regions (Wootton et al., 2002) and this has in part led to hesitance among some institutions (i.e. the World Health Organization and ministries of health) to implement MDA for malaria (World Health Organization, 2015a). MDA should ideally be used in settings with strong public health infrastructure, including easy access to diagnosis and treatment; an up-to-date and responsive surveillance system; and effective community engagement. Used appropriately, MDA can quickly reduce or eliminate parasite reservoirs and can act as a catalyst for subregional elimination of P. falciparum malaria (Landier et al., 2018a).

While antimalarials are usually administered following diagnosis (confirmed or presumed) or used as a prophylactic, MDA is used because of an intended population- or community-level effect. The rationale is that the transmission potential or reproductive rate of malaria is so high that a sufficient amount of the parasite reservoir needs to be removed in order to disrupt transmission. This group-level effect is also referred to as a ‘herd effect’ (John and Samuel, 2000; Pollard et al., 2015) and the concept applies to most communicable diseases. If a sufficient amount of the population participates in MDA, transmission chains cannot be sustained and transmission will cease, ultimately leading to a reduction in malaria morbidity and mortality (World Health Organization, 2017). There is likely to be a context-specific critical threshold for MDA coverage, below which the reduction of the parasite reservoir is not sufficient to halt ongoing transmission. Some literature has suggested that at least 80% coverage and adherence of MDA in the targeted population is necessary in order for the MDA to be successful (World Health Organization, 2015b). If the aim for antimalarial MDA is to interrupt transmission, the notion of a herd effect providing additional levels of population protection is plausible but has not been examined empirically (Cotter et al., 2013).

Drawing from detailed micro-epidemiological and spatial data from an MDA trial in Kayin State, Myanmar (Figure 1), we describe geographic and epidemiological patterns of clinical and subclinical P. falciparum malaria in villages undergoing MDA. We investigate associations between individual- and group-level participation in MDA (potential direct and indirect effects, respectively); subclinical infections; and clinical episodes of P. falciparum post-MDA. Such empirical research is important for providing an evidence base for further research, intervention, and policy work.

Figure 1

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3229 villagers (1689 male) were included in this study. During the study period, 80 study participants were diagnosed with clinical P. falciparum. 201 participants were found to have P. falciparum by uPCR. 325 uPCR-positive participants had Plasmodium infections not identifiable at the species level and thus were not included in these analyses. Total numbers of clinical episodes and uPCR-detected infections were higher than the total number of infected individuals because some participants had multiple infections.

After MDA, the vast majority of clinical P. falciparum episodes occurred in only one of the study villages (TOT). 66 out of the 80 participants who had a clinical P. falciparum episode were from TOT village (three from HKT, seven from TPN and four from KNH).

Eleven of the 80 participants (14%) who had a clinical P. falciparum episode during the study period had repeated clinical episodes and 19 of the 80 (24%) participants who had a clinical episode were found to have a uPCR-detected P. falciparum infection in at least one of the surveys. uPCR-detected P. falciparum infections were more prevalent in males than females (UOR: 2.03; CI: 1.50–2.76).

Spatiotemporal patterns in clinical episodes, uPCR-detected infections, and MDA adherence

uPCR-detected P. falciparum infections were widespread in all villages at baseline (Figure 2). These infections were significantly reduced following MDA in all villages. The prevalence of uPCR-detected P. falciparum infections had reduced in two control villages (villages TPN and HKT) prior to MDA.

Figure 2

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There were statistically significant clusters of uPCR-detected P. falciparum infections in each village at baseline but subsequently no significant clusters were detected (Figure 2). Clusters of clinical P. falciparum episodes occurred in two villages (KNH and TOT). The cluster in KNH occurred from M5 through M7 but included only four episodes. There were two separate clusters in village TOT. A cluster in the western portion of the village began in M12 and lasted until M18 (with a total of 35 episodes). A single-house cluster occurred in the eastern portion of the village (M15 through M18) with five episodes among four house members (2 in a 10 yo male, 1 in a 48 yo male, 1 in a 16 yo male, and 1 in a 48-year-old female).

There were significant clusters of non-participation in the MDAs in three of the study villages (TPN, HKT and TOT (Appendix 1—figure 2)). The non-participation cluster in TOT made up a large portion of the western part of the village and included 115 individuals not participating in the MDA (out of 919 total individuals in TOT). The non-participation clusters in HKT and TPN included 206 and 15 individuals respectively.

Sporadic clinical P. falciparum episodes occurred in village TOT following MDA (MDA was completed by M3), followed by a small outbreak beginning in M12 (Figures 2 and 3). The first clinical P. falciparum episodes during this outbreak occurred among villagers who lived in the cluster of non-MDA participation (Figure 3). By M15 the clinical episodes were occurring through much of the village (Figure 3).

Figure 3

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Cumulative hazards plots of clinical P. falciparum episodes in village TOT illustrate the temporal patterns in infections according to neighborhood MDA non-adherence (aggregated into terciles) and individual participation in MDA (Figure 4). The proportion of individuals who had acquired a clinical P. falciparum episode began consistently increasing in M12 for those living in either mid or high MDA non-adherence neighborhoods. P. falciparum episodes among low MDA non-adherence neighborhoods began increasing approximately 1 month after the increase in high non-adherence neighborhoods but never reached the level experienced in either mid or high MDA non-adherence neighborhoods. 4.4% of all individuals in low MDA non-adherence neighborhoods had at least one clinical P. falciparum episode by the end of the study period, in comparison to 7.6% in mid and 9.6% in high MDA non-adherence neighborhoods (log-rank test p-value=0.0485; Figure 4A).

Figure 4

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The increase in clinical P. falciparum episodes in M12 also coincided with an increase in HBR in village TOT (Appendix 1—figure 3).

The primary objective of this research was to look for a potential herd effect and at the impact of non-adherence with regard to MDA for P. falciparum malaria. There was an apparent group level effect from MDA adherence, suggesting herd protection and evident from three lines of evidence.

First, clinical episodes decreased among all groups for a longer period than the prophylactic effect (approximately 1 month) of the administered antimalarials. This group level protective effect from MDA was also evident in the rainy season following MDA (Figure 3) which corresponded to a surge in vector activity (Appendix 1—figure 3). Once the P. falciparum outbreak began (M12), there was a lag of approximately 1 month between the onset of clinical episodes in neighborhoods with mid and low MDA adherence and then occurred in neighborhoods with high MDA adherence (Figure 4A).

Second, neighborhoods with high MDA adherence never experienced the same levels of infection as those with mid or low MDA adherence (Figure 4A). Individuals who participated in MDA but lived in a neighborhood with low adherence had the highest risk of having a clinical episode whereas those who did not participate in MDA but lived in a neighborhood with high adherence had the lowest risk of having a clinical P. falciparum episode (Figure 4B). As has been described in other settings, this individual-level finding may be related to relative perceptions of risk; with potential complacency among individuals living in areas with lower levels of malaria (Koenker et al., 2013).

Third, the results from the multivariable logistic regression also suggest that living in a neighborhood with a high proportion of people who did not adhere to MDA was a significant risk factor for acquiring a clinical P. falciparum episode, after adjusting for individual MDA adherence and other important predictors of having a clinical episode (Table 1). To our knowledge, this is the first documentation of a herd effect conferred by MDA for P. falciparum malaria.

The increase in clinical P. falciparum episodes post-MDA also corresponded to an increase in village HBR. HBR also peaked in one other village (HKT) at the same time as in village TOT (Appendix 1—figure 3), but occurred in the absence of a detectable parasite reservoir and the HBR did not persist at high levels. Evidence also suggests that the MP in TOT was not functioning well in the first year of the study (reported in Landier et al., 2017a). The combination of a persisting parasite reservoir and persistently high HBR (from M13 – M18) in TOT likely explains the drastically different results between the study villages with regard to P. falciparum malaria elimination (Figure 2). A better functioning MP in TOT would likely have reduced the size of the outbreak.

Clustering of uPCR-detected P. falciparum infections across houses occurred for limited periods of time only prior to MDA (Figure 2). This clustering suggests that interventions such as reactive case detection would have resulted in the detection of extra cases (of both clinical and uPCR-detected P. falciparum) when searching within houses and occasionally in neighboring houses, but these would have only been a small proportion of all infections within the villages (Parker et al., 2016) and would not have halted transmission. Conversely, community based early diagnosis and treatment and MDA with high participation, targeted at the village scale or larger, appear effective at reducing prevalence, incidence, and transmission of P. falciparum (Landier et al., 2018b).

There are several limitations to this work. Individuals who did not participate in MDA also did not participate in blood screenings immediately after MDA (i.e. M3 in village TOT). uPCR-detected infections are therefore likely to be underdiagnosed for these individuals, and it is likely that such infections clustered and overlapped with the clusters of non-adherence to MDA. While no genetic analyses were done with samples from the village, it is likely that these underdiagnosed infections, combined with the high HBR, led to a resurgence of clinical episodes. There is also evidence of a poorly functioning MP in this village, which could have led to undiagnosed clinical episodes, especially during the beginning of the study. Some infections are likely to be acquired outside of the village, leading to complex spatial patterns in infections that are mapped at the house level. Within-household clustering can be the result of within-household transmission, or shared exposure outside of the household or village among household members. Finally, these data come from a limited number of villages (total of 4), with analysis of P. falciparum episodes coming from the sole village that continued to have P. falciparum after MDA. Given that clinical P. falciparum episodes post-MDA were only possible to analyze in a single village, and that neighborhoods were not discrete and overlapped (100 m buffer around each house), a neighborhood-level effect was not included in this analysis. It is possible that the confidence intervals around the neighborhood MDA adherence variable are therefore too small and would not have been statistically significant had a neighborhood effect been included. This work would benefit from analyses with larger datasets.

This work has relevance with regard to further research and practice concerning MDA. While participation is obviously crucial to success, there is unlikely to be a single adherence proportion that can be applied in all situations. In this study, the elimination efforts were successful in three out of four villages even though one of those villages (HKT) had a similar overall adherence to the village with P. falciparum remaining after MDA (TOT). Likewise, this work points toward the need for considering spatial scale in MDA and in MDA adherence. Most current MDA trials and programs in the GMS and elsewhere consider a single village or community as the target unit. In some cases, especially when high prevalence villages are spatially clustered across a landscape, it may be necessary to target units above the village level (i.e. groups of villages).

The data used in these analyses are human subjects data from a sensitive population and organizational policy restricts data sharing for ethical and security considerations. Data can be accessed through the Data Access Committee at Mahidol Oxford Tropical Medicine Research Unit (MORU). The data sharing policy (including information on how to access the data) can be found here: http://www.tropmedres.ac/data-sharing.

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Author details

1. Daniel M Parker

   Department of Population Health and Disease Prevention, University of California, Irvine, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   dparker1@uci.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5352-7338

2. Sai Thein Than Tun

   1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom

   Contribution

   Formal analysis, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

3. Lisa J White

   1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom

   Contribution

   Conceptualization, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Ladda Kajeechiwa

   Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand

   Contribution

   Data curation, Supervision, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. May Myo Thwin

   Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand

   Contribution

   Supervision, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

6. Jordi Landier

   Institut de Recherche pour le Développement, University of Montpellier, Montpellier, France

   Contribution

   Data curation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8619-9775

7. Victor Chaumeau

   1. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom
   2. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
   3. Centre Hospitalier Universitaire de Montpellier, Montpellier, France
   4. Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle IRD 224-CNRS 5290UM1-UM2, Institut de Recherche pour le Développement (IRD), University of Montpellier, Montpellier, France

   Contribution

   Data curation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0171-2176

8. Vincent Corbel

   Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle IRD 224-CNRS 5290UM1-UM2, Institut de Recherche pour le Développement (IRD), University of Montpellier, Montpellier, France

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

9. Arjen M Dondorp

   1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom

   Contribution

   Data curation, Supervision, Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5190-2395

10. Lorenz von Seidlein

    1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom

    Contribution

    Conceptualization, Data curation, Supervision, Methodology, Writing—original draft, Writing—review and editing

    Competing interests

    No competing interests declared

11. Nicholas J White

    1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom

    Contribution

    Supervision, Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1897-1978

12. Richard J Maude

    1. Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand
    2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom
    3. Harvard TH Chan School of Public Health, Harvard University, Harvard, United States

    Contribution

    Conceptualization, Formal analysis, Visualization, Writing—review and editing

    Competing interests

    No competing interests declared

13. François Nosten

    1. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United kingdom
    2. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Nakhon Pathom, Thailand

    Contribution

    Supervision, Visualization, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7951-0745

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