Introduction

In 2023 there were an estimated 263 million malaria cases worldwide, with 94% occurring in the WHO African Region [1]. Human malaria infection is caused primarily by five species of the Plasmodium parasite (falciparum, malariae, ovale curtisi, ovale wallikeri, and vivax), with most cases and morbidity in Africa attributable to Plasmodium falciparum. The clinical burden of non-falciparum species in Africa is not well characterized, but all five species have been reported in multiple countries where they have previously not been considered a threat. Falciparum clearly remains the primary cause of morbidity and mortality, but severe disease has been reported with non-falciparum species as well, including anemia, renal failure and other severe outcomes [2–5]. Increase in non-falciparum prevalence has been observed in areas where falciparum prevalence has steadily declined, presenting opportunities for other Plasmodium species to flourish [6,7].

Diagnosis of malaria in Africa relies principally on rapid diagnostic tests (RDTs) detecting the antigen P. falciparum histidine rich protein 2 (HRP2), often with a second less sensitive band for pan-species lactate dehydrogenase (LDH) [8]. Designed to detect clinical malaria, RDTs for malaria surveillance will often miss low density P. falciparum infections and infections with non-falciparum species. Alternate approaches are needed to accurately define the full asymptomatic reservoir. Molecular detection, such as through real-time PCR, can identify infections at lower parasitemia levels than RDTs, and better characterize non-falciparum malaria infections. Molecular approaches are particularly important for use in asymptomatic community surveillance [9]. Asymptomatic infections may have significant but underestimated morbidity [10], and are often followed by symptomatic infection [11]. In addition, they provide a reservoir for ongoing transmission [12–14]. Thus, characterizing the epidemiology of asymptomatic malaria is important for malaria control and prevention, as malaria elimination requires control of all species of malaria, whether symptomatic or asymptomatic.

Molecular identification of non-falciparum malaria infections (P. malariae, P. ovale curtisi, P. ovale wallikeri, and P. vivax) is critical for addressing morbidity from these parasites and for reaching malaria elimination in settings with falciparum-based diagnostics and treatments. Accurate diagnosis is imperative for malaria control, as the clinical features and treatments differ. In contrast with P. falciparum infection, P. vivax and P. ovale cause relapse through the persistence of hypnozoites (dormant liver stage parasites) [15,16]. As hypnozoites do not respond to blood-stage treatment, namely artemisinin-combination therapies (ACTs), the primary treatment for severe malaria in most countries, relapsing infections require radical cure [17]. Thus, their presence may require national malaria control programs to alter therapeutic options in the country. To-date, P. ovale spp. and P. malariae malaria have been more frequently described, due in part to wider implementation of highly sensitive nucleic acid detection, and also likely due to increasing prevalence as P. falciparum is controlled as was seen for P. vivax in Southeast Asia [6,7,18]. P. vivax is also being reported more frequently due to wider implementation of molecular diagnostics [4,19,20].

Rwanda has historically had strong malaria control, leveraging effective antimalarials, insecticide-treated bed nets, and indoor residual spraying [21]. Malaria cases in Rwanda increased from 48 to 403 per 1,000 between 2012 and 2018, with mortality increasing 41% over the same time [21]. Emerging insecticide resistance, an increase in irrigated agriculture, and insufficient insecticide-treated bed net coverage, among other factors, have been attributed to these increases [21]. From 2018 to 2022, malaria prevalence decreased from an estimated 321–76 cases per 1,000, still higher than 2012 estimates [22], and with detection of non-falciparum species among clinical malaria patients [23]. There has been one recent report of P. vivax commonly occurring in Hue [23]. These inconsistent declines in prevalence suggest the need to characterize the epidemiology of asymptomatic and non-falciparum infections in this period to inform future control efforts.

We used survey, GPS, and biospecimen data collected in the 2014−15 Rwanda Demographic and Health Survey (RDHS), a large nationally population-representative study, to characterize asymptomatic and non-falciparum infections across the country’s adult population.

Methods

Results

Prevalence of P. falciparum and non-falciparum infection by real time PCR

A total of 1,478 P. falciparum, 283 P. ovale, 186 P. malariae and 7 P. vivax infections were identified. The overall weighted prevalence of any non-falciparum malaria infection was 8.0% (407/5,099, 95% CI: [6.6, 9.3]) compared to 16.3% (831/5,099 [14.5, 18.1]) for P. falciparum and 22.3% (1,137/5,099 [20.3, 24.3]) overall malaria prevalence with HIV sampling, inverse propensity of selection, and transmission intensity correction weights applied. Species specific weighted prevalences were 3.4% (172/5,099) [2.6, 4.1] and 4.7% (241/5,099) [3.5, 5.9] for P. malariae and P. ovale spp. Unweighted prevalence for P. vivax was 0.14%, (7/5,050) with a manually calculated 95% CI [0.03, 0.25]. Study population characteristics and the distribution of PCR positives by species are shown in Table 1. Unweighted prevalence estimates were calculated for each species based on false-positive rates of high cycle number PCR (S4 Table). Using a more restrictive cut off of 40 cycles (requiring a higher parasitemia to be positive at approximately 1 parasite per microliter of template DNA), resulted in weighted overall prevalences of 4.3% (218/5,099) [3.5, 5.1] for any non-falciparum infection, compared to 13.2% (674/5,099) [11.6, 14.8] for P. falciparum and 16.4% (836/5,099) [14.7, 18.1] for overall malaria. Species specific weighted prevalences at this cut off were 2.7% (139/5,099) [2.0, 3.4] and 1.6% (80/5,099) [1.1, 2.0], and for P. malariae, and P. ovale spp. Unweighted prevalence for P. vivax was 0.06% (3/5,050 [0.00, 0.13]. The largest difference in estimated prevalence was for P. ovale spp. This is not surprising given the distribution of estimated parasitemia values (Fig 1), showing a lower median parasitemia in the non-falciparum species compared to P. falciparum. District level weighted prevalences and their differences by PCR cut off are shown in S5 and S6 Tables. P. falciparum, P. ovale and P. malariae infections were distributed across the country, while P. vivax infections were more localized (S2 Fig). District level overall malaria prevalence is shown in Fig 2, while district level prevalences for each species are illustrated in Fig 3. Among P. ovale spp. P. malariae, and P. vivax infections, 48.4%, (137/283) [42.5, 54.4]), 46.8% (87/186) [39.5,54.2]), and 57% (4/7) [20.2, 88.2], (unweighted counts) were infected with at least one other species of malaria (S7 Table).

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Table 1. Study population characteristics and distribution of PCR positives by species. Weighted counts and percentages for all individuals positive for each species of malaria. Weights applied are DHS sampling weights, propensity score weights, and weights to correct for over-selection of high transmission intensity clusters (transmission intensity weights). Characteristics of the included study population are also shown.

https://doi.org/10.1371/journal.pone.0330480.t001

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Fig 1. Calculated parasitemia estimates for falciparum and non-falciparum infections.

Overall, falciparum had a higher parasite density with a median of 10.90 (IQR 0.96-101.41). The median parasitemia level for P. malariae, P. ovale spp., and P. vivax malaria were 0.35 (IQR: 0.07-1.83), 0.48 (IQR: 0.16-3.25) and 0.94 (IQR: 0.20-3.44), respectively.

https://doi.org/10.1371/journal.pone.0330480.g001

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Fig 2. District level weighted overall malaria prevalence.

Weighted prevalence of any malaria infection, using HIV sampling, inverse propensity for selection and transmission intensity weights (described above). Shapefiles for country borders were downloaded from ArcGIS Hub, district boundaries and the lake shapefile were downloaded from the OCHA Regional Office for Southern and Eastern Africa database.

https://doi.org/10.1371/journal.pone.0330480.g002

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Fig 3. District level weighted prevalence estimates for malaria species.

The weighted prevalence estimate for each species is shown. Panel A, B, C and D represent P. falciparum, P. malariae, P. ovale spp. and P. vivax malaria, respectively. Shapefiles for country borders were downloaded from ArcGIS Hub, district boundaries and the lake shapefile were downloaded from the OCHA Regional Office for Southern and Eastern Africa database.

https://doi.org/10.1371/journal.pone.0330480.g003

Bivariate associations for infection

Bivariate regression models using weighted (as previously described) survey data found multiple associations for infection with P. falciparum malaria, but few for non-falciparum malaria (Fig 4). Covariates with more than two categories were dichotomized to provide interpretable comparisons across the study population. For example, Anopheles mosquito vectors generally do not thrive at elevations over 1,500 meters, although this is not a fixed limit [41]. Land cover classifications were dichotomized to compare less vegetation/more human activity to more vegetation/less human activity. No adjusted analyses were conducted as we are only exploring the strength of individual associations based on prevalence measures, and generalizability of participants analyzed to the country or survey as a whole is provided through weighting. Similar to previous work, a higher prevalence of P. falciparum malaria in our dataset was significantly (at a 0.05 confidence level for all associations) associated with multiple study covariates related to socioeconomic status (lower wealth quintile, lower education status, and unpiped drinking water). Secondary or higher education, residence in a household with at least 1 bed net per 1.8 household members, piped drinking water, continuous increase (and clusters at or above the study sample average) in prior month’s average rainfall, and higher altitude (>1,500 meters) were all associated with significantly lower prevalence of P. falciparum malaria. Rural clusters compared to urban, clusters designated as perennial cropland or grassland (compared to woodland or sparse forest), lower wealth index (first and second quintiles compared to the upper three), younger age (15–24 years compared to over 24 years) and residence in clusters with monthly average temperatures at or above the average for all clusters in analysis were significantly associated with higher P. falciparum prevalence. Fewer associations were found for non-falciparum malaria. No significant associations were found for P. malariae infection at an alpha level of 0.05. P. ovale infection was significantly associated with residence in clusters at or above the average monthly temperature, while lower prevalence of infection was associated with younger age in both the binary categorization described above and continuous decrease in age. Associations for P. vivax infection were not attempted due to the limited number of infections in the survey (n = 7).

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Fig 4. Bivariate associations and between demographic and environmental risk factors and Plasmodium spp. prevalence using weighted survey data.

Models incorporate 2014−15 Rwanda Demographic and Health Survey weights, inverse probability of selection weights, and cluster transmission intensity weights (described in Table 1). Point estimates of prevalence difference are surrounded by confidence intervals. The reference is the second variable listed. Panel A, B and C represent P. falciparum, P. malariae, and P. ovale sp., respectively. Note that each panel has a different scale.

https://doi.org/10.1371/journal.pone.0330480.g004

Discussion

In Rwanda, a setting with robust malaria control efforts but inconsistently declining prevalence, we conducted the largest assessment of asymptomatic and non-falciparum malaria to-date, primarily among adults. We found an asymptomatic malaria weighted prevalence of 22.3%, using PCR, substantially higher than estimates produced using RDT and microscopy. Additionally, we observed 8.0% of the population was infected with a non-falciparum species. While we observed P. falciparum prevalence to be 16.3% in adults, malaria detection by microscopy in the DHS reported a 2% malaria prevalence among children age 6–59 months and 0.6% among women age 15–49 [42]. This high prevalence in adults found using molecular detection is consistent with a study of school-aged individuals in the Huye District in the same year, finding a 22% prevalence of any malaria (19% P. falciparum prevalence) using a combined microscopy and PCR approach [43].

We used an ultra-sensitive assay for P. falciparum to detect primarily asymptomatic, lower density infections, improving the characterization of P. falciparum prevalence. While a higher PCR cycle cut-off may cause concern about false-positive detection, our assays have been run extensively at 45 cycles with little to no evidence of false positives [31], and are consistent with estimates using a lower cut off (40 cycles). The largest change in prevalence occurred with P. ovale spp., where estimated prevalence dropped from 4.7% to 1.6% at different cycle cut offs, reflecting the high number of low density infections detected. The relative decrease in prevalence for each species based on cycle cutoff was not always consistent, with some regions having no decline in P. falciparum prevalence with large declines in P. ovale malaria (e.g., Karongi near Lake Kivu) or the opposite with no decline in non-falciparum but lower P. falciparum prevalence estimates (e.g., Rutsiro and Nyamasheke). With asymptomatic P. falciparum malaria increasing risk of symptomatic disease at one month and contributing to significant morbidity, the high observed prevalence of asymptomatic, low-parasitemia infections offers an important direction for malaria control [10,11]. Estimating prevalence of sub-patent malaria should be prioritized as a complement to RDT or microscopy detected prevalence to accurately characterize transmissible reservoirs in endemic regions.

Non-falciparum malaria was detected in 8.0% of individuals nationally, a prevalence not previously appreciated in the country. P. ovale spp. and P. malariae were both common in Rwanda (4.7% and 3.3% prevalence, respectively) and distributed in regions of both high and low transmission. A recent household survey estimated that P. falciparum is responsible for 97% of malaria infections in Rwanda, with P. malariae and P. ovale spp. each responsible for 1%–2% of total infections [44]. P. vivax is present but remains relatively uncommon (0.14% unweighted prevalence) and sporadic, but consistent with reporting of occasional clinical cases [44]. More recently, a cluster of P. vivax was reported in the Huye District [23]. Given distinct treatment approaches by species, the higher than expected prevalence of P. ovale spp. and occasional P. vivax infection underscores the need for molecular monitoring to guide control efforts towards elimination. Regular surveillance for non-falciparum malaria is needed to inform regional treatment guidelines, particularly if relapsing malaria species continue to expand their prevalence.

Not surprisingly, mixed species infections were common and widely distributed, but occurred more commonly in clusters in the south and east where malaria transmission was the highest (S2 Fig). Among P. ovale spp. P. malariae, and P. vivax infections, 44%, 45% and 57% (unweighted) were infected with at least one other species of malaria (S7 Table). Bivariate associations for mixed infections largely reflected those observed for P. falciparum, albeit with lower magnitude and wider confidence intervals attributable to the smaller total counts. Mixed infections are often underappreciated by clinicians and laboratorians and may lead to severe disease complications. A recent meta-analysis suggested that patients with mixed infections have a higher proportion of pulmonary complications and multiple organ failure than patients with P. falciparum infection alone [45]. The impact of mixed species infections on clinical malaria outcomes in Rwanda is unknown and requires additional evaluation in symptomatic infections, which were not included in this study.

The associations we observed between P. falciparum infection and lower household wealth, no bed net use and lower elevation are consistent with previous studies using these data, with malaria positivity defined by RDT or microscopy [46,47]. We observed malaria prevalence was highest in the South and East as expected, but asymptomatic infection remained common in other areas. Like similar studies, associations of covariates for non-falciparum malaria were few and traditional risk factors for P. falciparum were less strongly associated with non-falciparum malaria [20,28,31,37]. This raises concern for how the control program can target non-falciparum infections without better diagnostics in the community. The reasons for the relative lack of risk factors, especially for P. ovale spp., remains unclear. Relapsing malaria, caused by P. ovale and P. vivax, may not be associated with typical covariates due to the inability to discern between incident or relapse infections in the study. Additionally, non-falciparum infections are under-detected in this sample compared to falciparum due to the ultra-sensitive assay used for falciparum detection, which may bias estimates and increase uncertainty for associations with non-falciparum infections.

While this data reflects the epidemiology from nearly a decade ago, it remains important for malaria control. The baseline set here is useful for understanding the interventions that have been used in Rwanda for malaria control if future similar surveys, such as the 2019/20 DHS, Malaria Indicator Surveys, or other broad sampling efforts, are genotyped similarly. The longitudinal data across surveys can be used to understand how malaria interventions impact both asymptomatic malaria and non-falciparum malaria, both of which are not captured by standard DHS data but still important for malaria elimination.

While this national molecular evaluation offers important perspectives, we are limited by a few key points. As noted above, the cross-sectional nature of a single DHS limits inference regarding transmission and time trends, but use of subsequent (2019−20 DHS) and future DHSs, would allow for ongoing surveillance of these parasites, as has been done in the DRC [48]. We also could not determine if P. ovale spp. and P. vivax infections were newly acquired or the result of relapse from hypnozoites. Detection of these parasites is still important for malaria control programming. Additionally, the relative lack of data for children under 15 makes this survey not representative of the full asymptomatic reservoir and non-falciparum prevalence. We are limited by the sampling framework of the DHS. School aged children are a particularly vulnerable group and often have the highest rate of malaria infection in Africa, including for P. vivax [49]. Without this group, a true population prevalence of asymptomatic infection is difficult to determine. However, we likely underestimate overall population prevalence given the 2017 MIS had an overall prevalence of 14% for children aged 6 months to 14 years of age using a rapid diagnostic test which typically detects 41% of infections compared to PCR [9,50]. In addition, males were overrepresented. We used an assay that targets 18S ribosomal RNA genes for non-falciparum species, thus could underreport infections that could have been detected with assays that target genes with higher copy numbers in the parasite such as the assay used for falciparum in this study [51–53]. Despite these limitations, the use of existing samples and individual level data from a DHS is a highly informative method to gain insights into national malaria prevalence.

This study represents the first nationwide molecular investigation of asymptomatic Plasmodium falciparum and non-falciparum malaria in Rwanda and highlights a significant parasite reservoir and the essential role of molecular testing to appropriately detect them. First, we found that asymptomatic P. falciparum infections in adults were significantly more prevalent than previously estimated by rapid diagnostic tests (RDTs) and microscopy in children [42]. These infections are not benign: they carry clinical consequences and serve as a hidden reservoir for continued transmission, underscoring the importance of identifying and addressing asymptomatic carriers in control strategies [10,12]. Second, molecular methods were necessary to detect P. ovale spp., P. malariae, and P. vivax, which are better characterized by molecular methods as they can be frequently missed by standard diagnostics. In this study, most non-falciparum infections were low-density and often co-occurred with P. falciparum, particularly P. ovale spp. and P. vivax, which would contribute to under-recognition of mixed infections. The observed prevalence of P. ovale spp. and P. malariae was higher than expected and lacked consistent risk factors. In order to assess if these species may be expanding into niches left by successful P. falciparum control, continued monitoring with molecular methods is needed. Third, comprehensive malaria elimination efforts must address all human malaria species—not just P. falciparum—including those capable of relapse and silent transmission. Therefore, ongoing molecular surveillance, ideally led by the national malaria control program, is critical to track changing epidemiology, guide targeted interventions, and support Rwanda’s path toward malaria elimination.

Supporting information

Acknowledgments

The following reagent was obtained through BEI Resources, NIAID, NIH: Plasmodium falciparum, Strain 3D7, MRA-102, contributed by Daniel J. Carucci. The following reagent was obtained through BEI Resources, NIAID, NIH: Diagnostic Plasmid Containing the Small Subunit Ribosomal RNA Gene (18S) from Plasmodium vivax, MRA-178, contributed by Peter A. Zimmerman. The following reagent was obtained through BEI Resources, NIAID, NIH: Diagnostic Plasmid Containing the Small Subunit Ribosomal RNA Gene (18S) from Plasmodium malariae, MRA-179, contributed by Peter A. Zimmerman. The following reagent was obtained through BEI Resources, NIAID, NIH: Diagnostic Plasmid Containing the Small Subunit Ribosomal RNA Gene (18S) from Plasmodium ovale, MRA-180, contributed by Peter A. Zimmerman.