Asymptomatic Plasmodium falciparum infections, defined as the presence of parasites in the absence of symptoms, are common across sub-Saharan Africa. In 2015, a geo-spatial meta-analysis estimated a continent-wide prevalence of asymptomatic P. falciparum in children aged 2 to 10 years of 24% based on microscopy and rapid diagnostic test (RDT) results (Snow et al., 2017). In high-transmission settings, these infections are more common, with point prevalence among adults exceeding 30% in the Democratic Republic of the Congo (Taylor et al., 2011) and Malawi (Topazian et al., 2020). Though by definition lacking acute symptomatology, these infections when persistent can adversely affect the individual (Cottrell et al., 2015; Maketa et al., 2015; Matangila et al., 2014; Sifft et al., 2016) as well as serve as a reservoir for onward parasite transmission (Gouagna et al., 2004; Tadesse et al., 2018).

Although epidemiologically important, the natural history of asymptomatic P. falciparum and its relationship to future symptomatic malaria remains unclear. In prior studies, asymptomatic P. falciparum infections have been observed to both decrease (Buchwald et al., 2019; Males et al., 2008; Portugal et al., 2017; Sondén et al., 2015) and increase (Le Port et al., 2008; Liljander et al., 2011; Njama-Meya et al., 2004; Nsobya et al., 2004) the risk of symptomatic malaria, and several have correlated this heterogeneity to people’s age or site transmission intensity (Henning et al., 2004; Wamae et al., 2019). Inferences have been further complicated in these studies owing to their cross-sectional capture of asymptomatic infections (Henning et al., 2004; Liljander et al., 2011; Males et al., 2008; Nsobya et al., 2004; Sondén et al., 2015; Wamae et al., 2019), short follow-up periods (Le Port et al., 2008; Njama-Meya et al., 2004), or limited age ranges (Le Port et al., 2008; Liljander et al., 2011; Males et al., 2008; Njama-Meya et al., 2004; Nsobya et al., 2004; Wamae et al., 2019), which collectively undermine a clear understanding of the risk of symptomatic malaria following the detection of an asymptomatic infection.

We investigated the natural history of asymptomatic P. falciparum infections in a high-transmission setting using a 29-month longitudinal cohort of people aged 1 to 85 years in Western Kenya. Using monthly active case detection of asymptomatic infections and passive capture of symptomatic events, we evaluated the likelihood of symptomatic malaria following an asymptomatic P. falciparum infection. We hypothesized that infection with asymptomatic parasitemia would be associated with a decrease in future risk of symptomatic malaria compared to uninfected people and that, because age serves as a proxy for prior cumulative exposure, this effect would be most pronounced in older people.

For 29 months, we followed 268 participants from three villages in Western Kenya. After excluding participants with less than 2 months of follow-up, the analysis dataset consisted of 257 participants with a median of 222 days (interquartile range [IQR]: 89, 427) of follow-up and a median age of 13 years (range: 1, 85) (Figure 3—figure supplement 2). Overall, 5379 person-months at risk were observed with 1842 (34.2%) person-months of asymptomatic malaria exposure; the median total months of asymptomatic exposure for a participant was 9 (IQR: 5, 17). Exposure status frequently changed for participants and remained constant for only 16 (6.2%) people across follow-up; four people were asymptomatically infected for the entirety of follow-up and only 12 people were never infected (Figure 2A). We recorded 266 symptomatic malaria events. Participants had a median of 1 (IQR: 0, 2) symptomatic infection during follow-up. Median time to symptomatic malaria when asymptomatically infected (173, IQR: 49, 399) was shorter than when uninfected (230, IQR: 98, 402), as well as shorter for participants aged 5 to 15 years or living in the village Maruti (Table 1). Comparison of Kaplan-Meier curves over the full 29 months indicated a difference in the time to symptomatic malaria in the first few months post asymptomatic infection but not long term (p-value = 0.100 by log-rank test) (Figure 2B). Results for secondary case definitions for symptomatic malaria were overall similar and are provided in the supplement (Supplementary files 2 and 3).

Figure 2

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Short-term effect of asymptomatic malaria exposure

In a univariate frailty Cox proportional hazards model, compared to uninfected people, the 1-month crude hazard ratio of symptomatic malaria for participants with asymptomatic infections was 2.69 (95% CI: 2.12 to 3.43). This association was similar in a model adjusted for covariates (adjusted HR [aHR]: 2.61, 95% CI: 2.05 to 3.33) (Table 2, Figure 3A) as well as when using alternative modeling approaches, alternate outcome case definitions, and in sensitivity analyses. This relationship between asymptomatic malaria and subsequent symptomatic illness was not modified by age (p-value = 0.447 by log-likelihood ratio test), because asymptomatic infections were associated with significantly increased likelihoods of subsequent symptomatic malaria in all age categories: <5 years (aHR: 3.77, 95% CI: 2.02 to 7.04), 5 to 15 years (aHR: 2.45, 95% CI: 1.79 to 3.35), and >15 years (aHR: 2.55, 95% CI: 1.57 to 4.15) (Table 3). In contrast, sex did modify this relationship (p-value = 0.006 by log-likelihood ratio test) (Table 3), whereby the risk of symptomatic malaria following asymptomatic infection was lower for males (aHR: 1.76, 95% CI: 1.24 to 2.50) compared to females (aHR: 3.71, 95% CI: 2.62 to 5.24) (Figure 3—figure supplement 3). We observed similar 1-month elevated risks of malaria in asymptomatically infected people when using both the ‘permissive’ (aHR 1.97, 95% CI: 1.63 to 2.40) and the ‘stringent’ (aHR 2.76, 95% CI: 2.11 to 3.62) alternate case definitions for symptomatic malaria (Figure 3—figure supplement 3).

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Table 2

	1-Month aHR (95% CI)	3-Month aHR (95% CI)	6-Month aHR (95% CI)	12-Month aHR (95% CI)	29-Month aHR (95% CI)
Main exposure
No infection	Ref	Ref	Ref	Ref	Ref
Asymptomatic infection	2.61 (2.05, 3.33)	1.64 (1.40, 1.94)	1.38 (1.20, 1.58)	1.12 (1.00, 1.25)	1.11 (1.01, 1.22)
Age
<5 years	Ref	Ref	Ref	Ref	Ref
5 to 15 years	1.37 (0.90, 2.08)	1.61 (1.00, 2.61)	1.99 (1.07, 3.71)	2.37 (0.97, 5.77)	2.52 (1.26, 5.01)
>15 years	0.56 (0.36, 0.88)	0.74 (0.46, 1.21)	0.83 (0.44, 1.53)	0.88 (0.37, 2.08)	0.97 (0.51, 1.84)
Sex
Male	Ref	Ref	Ref	Ref	Ref
Female	0.93 (0.70, 1.24)	0.84 (0.61, 1.16)	0.80 (0.53, 1.20)	0.68 (0.38, 1.21)	0.63 (0.40, 0.99)
Regular bed net usage*
No	Ref	Ref	Ref	Ref	Ref
Yes	1.00 (0.70, 1.43)	0.81 (0.55, 1.20)	0.70 (0.43, 1.16)	0.59 (0.29, 1.21)	0.52 (0.30, 0.89)
Village
Kinesamo	Ref	Ref	Ref	Ref	Ref
Maruti	1.08 (0.77, 1.52)	1.11 (0.75, 1.64)	1.14 (0.69, 1.88)	1.13 (0.56, 2.31)	1.09 (0.64, 1.85)
Sitabicha	0.72 (0.49, 1.05)	0.80 (0.53, 1.21)	0.76 (0.45, 1.29)	0.73 (0.35, 1.51)	0.70 (0.40, 1.23)

1. Abbreviations: CI, confidence interval; aHR, adjusted hazard ratio; Ref, reference.

   *Regular bed net usage was defined as a person averaging > 5 nights a week sleeping under a bed net.

2. Significant estimates are bolded.

Table 3

Comparison	Age aHR (95% CI)				Sex aHR (95% CI)
	<5 years	5 to 15 years	>15 years		Male	Female
1-Month main model	3.77 (2.02,7.04)	2.45 (1.79,3.35)	2.55 (1.57,4.15)		1.76 (1.24,2.50)	3.71 (2.62,5.24)
1-Month pre-symptomatic	2.85 (1.19,6.79)	1.61 (1.05,2.46)	1.90 (0.93,3.86)		1.24 (0.75,2.05)	2.34 (1.47,3.71)
3-Month main model	2.47 (1.59,3.84)	1.49 (1.21,1.85)	1.69 (1.23,2.32)		1.29 (1.01,1.64)	2.03 (1.62,2.55)
3-Month pre-symptomatic	2.00 (1.20,3.34)	1.16 (0.90,1.48)	1.35 (0.94,1.93)		1.05 (0.79,1.39)	1.52 (1.18,1.97)
6-Month main model	1.94 (1.34,2.80)	1.32 (1.11,1.57)	1.31 (1.01,1.70)		1.13 (0.93,1.39)	1.62 (1.35,1.94)
6-Month pre-symptomatic	1.63 (1.08,2.46)	1.11 (0.92,1.34)	1.10 (0.83,1.46)		0.98 (0.78,1.22)	1.33 (1.09,1.62)
12-Month main model	Not calculated*	Not calculated*	Not calculated*		1.10 (0.86,1.19)	1.21 (1.05,1.41)
12-Month pre-symptomatic	1.24 (0.88,1.74)	1.00 (0.86,1.17)	0.85 (0.68,1.07)		0.91 (0.77,1.09)	1.04 (0.88,1.22)
29-Month main model	1.38 (1.05,1.81)	1.16 (1.02,1.32)	0.96 (0.81,1.13)		1.08 (0.94,1.24)	1.14 (1.01,1.30)
29-Month pre-symptomatic	1.23 (0.92,1.64)	1.06 (0.93,1.21)	0.88 (0.74,1.05)		Not calculated*	Not calculated*

1. Abbreviations: CI, confidence interval; aHR, adjusted hazard ratio.

   *Not calculated due to data sparsity.

2. Statistically significant effect measure modification by the log-likelihood ratio test is bolded.

In a subset analysis accounting for potentially pre-symptomatic infections, compared to uninfected people, the risk of symptomatic malaria was increased in those with asymptomatic infections by more than 1.7 times (aHR: 1.77, 95% CI: 1.26 to 2.47) when limited to those with events more than 14 days after exposure ascertainment (Figure 3A). In this subset, we did not observe effect measure modification by participant age or sex (Table 3). The 1-month adjusted risk of symptomatic malaria was not substantially different in models incorporating seasonality (aHR: 2.46, 95% CI: 1.93 to 3.15), the number of prior asymptomatic infections (aHR: 2.60, 95% CI: 2.03 to 3.31), or prior antimalarial treatment (aHR: 2.61, 95% CI: 2.05 to 3.33), nor in a model using the dataset without imputation (aHR: 2.75, 95% CI: 2.05 to 3.66).

Short-term effect of detectability of asymptomatic infections

Owing to the consistently elevated short-term risk of symptomatic malaria in people with asymptomatic infections, we investigated the effect of parasite density in these infections on the risk of subsequent symptomatic malaria within 1 month. Compared to uninfected people, the 1-month hazard of symptomatic malaria was significantly increased by asymptomatic infections of all parasite densities, with the highest risk for those with densities > 1000 parasites/μL (aHR 3.99, 95% CI: 2.41 to 6.62) (Figure 4). This observed increase in the hazard of symptomatic malaria with increasing parasite density was most pronounced among adults >15 years (Figure 4—figure supplement 1); however, children’s likelihood of symptomatic infection did not appear to be influenced by parasite density.

Figure 4 with 1 supplement see all

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Using a 29-month longitudinal cohort in a high malaria transmission region of Kenya, we investigated the association between asymptomatic P. falciparum infections and the risk of symptomatic malaria. In the short term, compared to uninfected individuals, people of any age with asymptomatic infections were associated with a more than twofold increased hazard of symptomatic malaria within 1 month. This elevated likelihood of symptomatic malaria was associated with asymptomatic infections at all parasite densities. As follow-up time was expanded, the association between asymptomatic infection and the increased risk of subsequent symptomatic malaria remained significant but attenuated. Collectively, our findings that detection of an asymptomatic P. falciparum infection confers an elevated risk of future symptomatic malaria supports the routine treatment of infections even in the absence of symptoms to prevent clinical cases.

Previous studies that detected asymptomatic infections using microscopy also reported an increased short-term hazard of symptomatic illness among children within 9 to 30 days after having an asymptomatic malaria infection (Le Port et al., 2008; Njama-Meya et al., 2004). We built upon these studies by detecting asymptomatic infections using qPCR, a highly sensitive method with a low limit of detection (Taylor et al., 2019), in participants of all ages and similarly found that asymptomatic infections have a high probability of being quickly followed by symptomatic illness. The increased short-term hazard could reflect misclassification of a ‘pre-symptomatic’ infection that progressed to symptoms as an asymptomatic exposure (Njama-Meya et al., 2004). This interpretation is partially supported by the diminished risk observed in a sub-analysis censoring asymptomatic infections that occurred within 14 days prior to a symptomatic event (Figure 3A). The increased hazard of symptomatic malaria could also have been due to the presence of new genotypes in infections (i.e. superinfection), although we previously reported that such newly apparent genotypes were associated with symptoms only in previously uninfected people (Sumner et al., 2021). It is notable that the increased risk of symptomatic malaria following asymptomatic infection was observed in all age groups (Figure 3—figure supplement 2): though children under 5 years were consistently at highest risk, the increased risk in those >15 years was surprising given the presumption that adults develop functional immunity to clinical disease possibly in part from asymptomatic carriage. Our results indicate that asymptomatic infection is associated with an increased short-term risk of malaria irrespective of age.

Elevated risk of symptomatic malaria within 1 month was present for asymptomatic infections of any parasite density. We did observe a dose-response of the risk of symptomatic malaria as a function of parasite density, particularly among adults, but the risk of malaria was increased relative to uninfected people even when they harbored low-density infections. Prior studies observed conflicting relationships between asymptomatic parasite density and subsequent malaria: PCR-positive infections below the limit of detection of microscopy were not associated with subsequent symptomatic malaria in Ugandan children (Nsobya et al., 2004), but were associated with a reduced risk in Malawian children (Buchwald et al., 2019). To our knowledge, our data are the first to analyze a broad range of parasite densities in asymptomatic infections and their association with subsequent symptomatic malaria. Though higher densities were associated with slightly higher risk of symptomatic illness, the elevated risk across all clinically detectable parasitemia does not clearly support risk stratification by detectability for the purposes of preventing clinical disease. Our results indicate that in a high-transmission setting, despite the absence of symptoms, the detection of P. falciparum parasites of any density is significantly associated with an increased risk of malaria in the forthcoming month and suggest that detection modality should not influence a decision to treat.

We observed significant modification of the relationship between asymptomatic infection and symptomatic malaria risk by sex. Specifically, despite an overall lower burden of symptomatic malaria among females that is consistent with prior studies (Houngbedji et al., 2015; Mulu et al., 2013; Newell et al., 2016), we observed that the short-term hazard of symptomatic malaria following an asymptomatic infection was significantly higher among females (aHR 3.71) compared to males (aHR 1.76). To our knowledge, this effect modification by sex has not been previously reported, with prior studies typically including sex as a covariate in models. A recent study (Briggs et al., 2020) highlighted large gaps in knowledge related to sex-based differences in malaria epidemiology, while reporting that Ugandan females of all ages cleared asymptomatic infections at nearly twice the rate of males. That observation, while not directly comparable, is a challenge to reconcile with ours, which suggests that asymptomatic infections in females, compared to males, are more likely to culminate in symptomatic malaria than in natural clearance. Despite similarities in cohort membership, follow-up, and outcome assessment, a key difference may be the far higher transmission intensity in our cohort: the recent application of control measures in Uganda reduced the incidence of malaria in the area by more than 10-fold and the prevalence of PCR-detectable infections more than threefold. Either the recent fall or the current low-transmission intensity may have differentially affected the natural history of infections in that region. Reconciling these sex-based findings and exploring additional impacts relevant to prevention and control will require rigorous assessments of sex as an effect measure modifier in malaria epidemiology.

We observed that the increased risk for symptomatic disease associated with asymptomatic infection weakened as the follow-up length extended from 3 to 29 months, illustrated in both the multi-level models and Kaplan-Meier curves. One possible explanation for this observation could be inherent to the methodology whereby the magnitude of the average hazard ratio decreases as follow-up time increases (Hernán, 2010). Alternatively, it is biologically plausible that the further removed in time an asymptomatic exposure is, the weaker the relationship to disease outcomes, possibly by waning immunity. This is supported by our observation that older children and adults are no longer at increased risk of symptomatic disease by 29 months, although small children still maintain significantly elevated risk even for this extended follow-up period.

We used a novel approach to capture how asymptomatic malaria varied over time. Most previous work used an intention-to-treat approach for asymptomatic infections identified in cross-sectional surveys (Henning et al., 2004; Liljander et al., 2011; Males et al., 2008; Nsobya et al., 2004; Portugal et al., 2017; Sondén et al., 2015; Wamae et al., 2019); however, this method can misclassify person-time if the exposure frequently changes, as happens with asymptomatic infections in high-transmission areas. For previous studies with more frequent asymptomatic sampling, the projects had short follow-up periods (9 to 30 days) (Le Port et al., 2008; Njama-Meya et al., 2004), or coded the exposure as static after an asymptomatic infection occurred (Buchwald et al., 2019). We recorded asymptomatic malaria exposure using a time-varying method proposed by Hernán et al., 2005 that allows participants to change exposure status throughout follow-up, which may capture a more complete view of infection dynamics with lower risk of exposure misclassification. By producing an effect estimate predictive of future risk regardless of prior exposure, this method is less prone to left truncation bias, which can occur with methods that create additive measures of months of exposure. To our knowledge, though this method (Hernán et al., 2005) has been used in studies of cardiovascular or kidney disease (Danaei et al., 2013; Hernán et al., 2008; Secora et al., 2020), it has not before been used to study malaria. Given the frequency of outcome events in high-transmission settings and the complexity of risk factors for them, this approach could be a useful addition to the analytic toolkit to assess time-varying exposures and their association with malaria outcomes.

This study had some limitations that should be considered when weighing the findings. First, asymptomatic infections were only captured at monthly follow-up visits, potentially missing transient asymptomatic infections between visits. By allowing participant exposure to vary over time, we assumed exchangeability between the exposed and unexposed groups. This was mitigated by the observation that approximately 94% of the study population changed exposure status at least once during follow-up. Finally, we estimated parasite densities using molecular methods and only at a single time point, though densities are known to fluctuate during infections. However, this potential bias in density estimations should be random and non-directional, and therefore mitigated by the analysis of over 1600 density measurements in asymptomatic infections.

In conclusion, using a novel exposure coding method and frequent sampling of both children and adults over 29 months, we found that asymptomatic P. falciparum infections were associated with a high likelihood of being shortly followed by symptomatic illness across all ages and parasite densities. These results suggest interventions focus on treating and reducing asymptomatic malaria in high-transmission settings.

Data will be shared under the auspices of the Principal Investigators. Investigators and potential collaborators interested in the datasets will be asked to submit a brief concept note and analysis plan. Requests will be vetted by Drs. O'Meara and Taylor and appropriate datasets will be provided through a password protected secure FTPS link. No personal identifying information will be made available to any investigator. Relevant GPS coordinates would only be provided when (1) the planned analysis cannot reasonably be accomplished without them and (2) the release of the coordinates is approved by the Institutional Review Board. A random error in the latitude and longitude of 50-100 meters will be added to each pair of coordinates to protect individual household identities. General de-identified datasets will be prepared that can accommodate the majority of requests. These will be prepared, with documentation, as the data is cleaned for analysis in order to reduce time and resources required to respond to individual requests. Recipients of study data will be asked to sign a data sharing agreement that specifies what the data may be used for (specific analyses), criteria for acknowledging the source of the data, and the conditions for publication. It will also stipulate that the recipient may not share the data with other investigators. Requests for data use must be made directly to the PI and not through third parties.

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Author details

1. Kelsey M Sumner

   1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States
   2. Division of Infectious Diseases, School of Medicine, Duke University, Durham, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Judith N Mangeni

   School of Public Health, College of Health Sciences, Moi University, Eldoret, Kenya

   Contribution

   Data curation, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Andrew A Obala

   School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya

   Contribution

   Conceptualization, Supervision, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

4. Elizabeth Freedman

   Division of Infectious Diseases, School of Medicine, Duke University, Durham, United States

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Lucy Abel

   Academic Model Providing Access to Healthcare, Moi Teaching and Referral Hospital, Eldoret, Kenya

   Contribution

   Supervision, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

6. Steven R Meshnick (deceased)

   Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

7. Jessie K Edwards

   Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States

   Contribution

   Formal analysis, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

8. Brian W Pence

   Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

9. Wendy Prudhomme-O'Meara

   1. Division of Infectious Diseases, School of Medicine, Duke University, Durham, United States
   2. School of Public Health, College of Health Sciences, Moi University, Eldoret, Kenya
   3. Duke Global Health Institute, Duke University, Durham, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Contributed equally with

   Steve M Taylor

   Competing interests

   No competing interests declared

10. Steve M Taylor

    1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States
    2. Division of Infectious Diseases, School of Medicine, Duke University, Durham, United States
    3. Duke Global Health Institute, Duke University, Durham, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing

    Contributed equally with

    Wendy Prudhomme-O'Meara

    For correspondence

    steve.taylor@duke.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2783-0990

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