Asymptomatic school-aged children carry the majority of transmissible Plasmodium falciparum infections

doi:10.21203/rs.3.rs-5057702/v1

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Asymptomatic school-aged children carry the majority of transmissible Plasmodium falciparum infections

https://doi.org/10.21203/rs.3.rs-5057702/v1

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Persistent human-to-mosquito parasite transmission hinders malaria control in high burden settings. Understanding the human transmission reservoir can support the design of targeted interventions to reduce transmission. In a year-long cohort study in rural Malawi, we used molecular methods to detect all Plasmodium falciparum (Pf) infections and those containing gametocytes, the parasite stage required for transmission, longitudinally at routine surveillance and sick visits. Using population-level analyses, we determined the demographic, temporal, and spatial clustering of infections containing gametocytes and gametocyte density, which predicts transmission.

Gametocytes were not randomly distributed among the population or among individuals with Pf infections; gametocytes were detected in only 23% of the population. Among all participants, school-age children had significantly higher incidence of gametocyte-containing infections and high-density gametocyte infections compared to other groups. The presence of school-age children was a key driver of gametocyte frequencies and densities within households, even after adjusting for Pf infection levels. Based on the total gametocyte abundance in the population, we estimate that clearing infections from asymptomatic school-age children in the rainy season would decrease gametocyte abundance by 67% in the population. Thus, interventions targeting school-age children are needed to effectively reduce Pf infection risk at a population level.

Health sciences/Diseases/Infectious diseases/Malaria

Health sciences/Medical research/Epidemiology

Despite widespread implementation of core malaria control measures, including distribution of long-lasting insecticidal nets (LLINs), improved case detection, and expanded treatment with effective antimalarials, malaria control has stalled in many high-transmission settings in Africa [1, 2]. One explanation for this stagnation may be a failure of current interventions to target the key sources of malaria transmission [3, 4].

Transmission of Plasmodium falciparum (Pf) from humans to vector mosquitoes occurs when human infections contain gametocytes, the parasite stage that undergoes sexual reproduction in the mosquito. Infected mosquitoes then transmit sporozoites during subsequent blood-feeding on a susceptible human, thus perpetuating the transmission cycle [4–10]. Gametocytes develop 8–12 days after asexual stage parasites become detectable in the blood, and typically comprise less than 5% of the parasite biomass in an individual [11]. Malaria disease is mediated by asexual stage parasites and, thus, individuals who become symptomatic may be treated before gametocytes develop. In high-burden settings, individuals with partial immunity typically suppress high density asexual-stage parasitemia, avoiding clinical malaria disease and the need for anti-Plasmodium drug treatment. However, these individuals often harbor chronic low-density infections, which can produce gametocytes [10]. Indeed, gametocytes are often detected in asymptomatic individuals [8]. Thus, people with symptomatic infection, who are often treated prior to gametocyte development, may not be the primary infection reservoirs who are most important in promoting transmission [4, 8, 9]. This can result in unrecognized human-to-mosquito transmission, with longer-lasting, untreated infections being linked to increased gametocyte density [10]. In high-transmission areas, targeted chemoprevention and vaccination are mostly directed toward children under the age of five (under-5) and pregnant women, the two groups which bear the greatest burden of severe disease and malaria-related mortality. However, decreasing the disease burden among these groups may not decrease population-level infection prevalence. While protecting these groups must continue to be a priority, effective population-level disease reduction in high-burden settings may require specifically targeting other groups who represent important transmission reservoirs.

To date most studies of gametocyte epidemiology have focused on determining which people with Pf infections produce gametocytes. Parasite commitment to gametocyte production among individuals with Pf infections is associated with the people’s immune response, exposure to anti-malarial drugs, and within-host competition between different parasite strains [12–14]. However, to characterize the most important human transmission reservoir groups, it is necessary to understand the population-level distribution of gametocyte-carriage, which combines those groups most often infected with whether these infections contain gametocytes. Furthermore, transmission to mosquitoes is highly correlated with gametocyte density [15, 16]. Prior cross-sectional surveys have compared gametocyte prevalence and density by age group with symptom status [17], but have not quantified the contribution of each group to the overall population-level transmission.

Here we attempt to gain a more complete understanding of gametocyte dynamics in a moderate- to high-transmission setting by analyzing 1) the frequency of gametocyte detection over time, 2) clustering of gametocyte detection within households, and 3) the total abundance of gametocytes in the human population over time. To accomplish this, we conducted a household-based, prospective, longitudinal, open-enrollment cohort study during a one-year period in the catchment areas of two health centers in rural Malawi. All individuals living in clusters of 5–7 households in four eco-geographic quadrants around each of two health centers were offered enrollment (Fig. 1). Using population-level longitudinal and spatial analyses, we evaluated the individual- and household-level distributions of Pf infections containing any gametocytes, high-density gametocytes, and total gametocyte abundance over time, based on routine, scheduled follow-up visits and passive case detection when participants visited the local health center. As school-age children (5–15 years old) have previously been identified as highly prevalent gametocyte carriers, we hypothesized that they would have both the greatest incidence of transmission-relevant infections and make the largest contribution as transmission reservoirs over time. By identifying and targeting the most important Pf transmission reservoir group(s), defined here by the prevalence and density of gametocyte carriage, malaria control interventions may be made more effective.

From April 2019 through May 2020, 947 individuals in 238 households (median 4 people/household, range 1–11) were followed and tested for Pf infection at 6683 scheduled monthly active case detection (ACD) visits and 416 unscheduled passive case detection (PCD) visits where participants presented at study clinics, accumulating 658 person-years of follow-up time (Median: 329 days, IQR:202–342 days) with a mean of 7.5 visits per person (Supplemental Table 1). Due to withdrawals or household inaccessibility, 104 (11%) individuals from 18 households were sampled only once. Children under-5 attended an average of 8.0 visits/child, while males over 15 years of age (over-15) attended the fewest visits (average 5.4 visits/person). Participants had Pf infection detected by qPCR at 23% (1631/7099) of all visits: 22% (1466/6683) of routine visits and 40% (165/416) of PCD visits. Ninety-five percent (1553/1631) of Pf positive samples were tested for gametocytes by RT-qPCR. Of those tested, 36% (560/1553) were positive for gametocytes with a mean of 0.85 gametocyte-positive visits per person-year during the study. Among all visits, 8% (560/7099) were positive for gametocytes and only 1% (85/7099) had high gametocyte density associated with increased risk of transmission ( > = 10 gametocytes/µl) [15] (Table 1).

Gametocyte-containing infections are not evenly distributed in the study population

Gametocyte-positive observations occurred unevenly across the population, with only 23% (216/947) of participants ever having an infection with detectable gametocytes (Table 1; Fig. 2). Among the 216 participants with gametocytes detected, 118/216 (55%) were gametocyte-positive at multiple visits, with a mean of 2.6 gametocyte-positive visits per person, and a maximum of 10 gametocyte-positive observations for any one individual (Fig. 2). Visits with infections containing high gametocyte densities ( > = 10 gametocytes/µl), which are associated with increased risk of transmission [15], occurred in only 6% of the study population (54/947). These high gametocyte density infections were clustered among individuals who had multiple gametocyte-positive visits, with 39% of individuals with multiple gametocyte-positive visits having high-density infections vs 8% of individuals with only a single gametocyte-positive infection (p < 0.001)(Table 1).

We evaluated the proportion of the study population with any gametocyte-positive infections, more than one gametocyte-positive infection, mean gametocyte density, and gametocyte density cutoffs associated with increased likelihood of transmission to mosquitoes, using fixed covariates assessed at enrollment and visit-specific covariates (Table 1). Comparisons by age-group revealed that a higher proportion of school-age children (5–15 year-olds) had at least one gametocyte-positive observation during the study (37%) compared to younger children (under-5) or adults (over-15) (13% and 16%, respectively, p < 0.001). School-age children were also more likely to ever have had a high-density gametocyte infection (23%, p < 0.001) compared to other age groups, although their median gametocyte density over the study was lower than younger children and comparable to adults. Participants living in households with the lowest tertile of socioeconomic status (SES) and living in unfinished houses (as opposed to finished houses) were significantly more likely to have had at least one gametocyte-positive visit (p < 0.01) and to have had

Table 1

Gametocyte detection, median gametocyte density, and proportion with high density gametocytes detected in the study population (N = 947 individuals) by fixed covariates assessed at enrollment (A) and time-varying covariates assessed at each visit (N = 7099 visits) (B). SES: socioeconomic status, HH: household.
Population	N Total	N (%) ever positive for gametocytes	N (%) w/ > 1 gametocyte positive visit	Median (IQR) density gametocytes/µl	N (%) with > = 1 gametocytes/µl	N (%) with > = 10 gametocytes/µl
A. Enrollment Characteristics	947	216 (22.8)	118 (12.5)	0.9 (0.2, 4.7)	131 (13.8)	54 (5.7)
Age Group		***	***		***	***
Under 5	177	23 (13)	14 (7.9)	3.6 (0.9, 10.6)	18 (10.2)	11 (6.2)
5–15	337	124 (36.8)	75 (22.3)	0.8 (0.2, 4.8)	79 (23.4)	35 (10.4)
Over 15	433	69 (15.9)	29 (6.7)	0.6 (0.1, 2.2)	34 (7.9)	8 (1.8)
Sex		**	*
Female	558	108 (19.4)	57 (10.2)	1.3 (0.2, 5.3)	67 (12)	29 (5.2)
Male	389	108 (27.8)	61 (15.7)	0.7 (0.2, 3.5)	64 (16.5)	25 (6.4)
Site			**
Namanolo (lower elevation)	437	109 (24.9)	49 (11.2)	1.2 (0.3, 6.4)	69 (15.8)	29 (6.6)
Ntaja (higher elevation)	510	107 (21)	69 (13.5)	0.6 (0.2, 3.4)	62 (12.2)	25 (4.9)
HH head education^# (N = 774)
None	195	53 (27.2)	31 (15.9)	0.7 (0.2, 5.2)	32 (16.4)	14 (7.2)
Primary only	296	73 (24.7)	37 (12.5)	1.1 (0.2, 4.3)	43 (14.5)	18 (6.1)
Some secondary or higher	283	59 (20.8)	31 (11)	0.8 (0.2, 4.1)	40 (14.1)	17 (6)
SES Index^#		**
Low	222	60 (27)	29 (13.1)	1.1 (0.2, 5.4)	38 (17.1)	17 (7.7)
Medium	489	121 (24.7)	69 (14.1)	0.7 (0.2, 3.7)	73 (14.9)	32 (6.5)
High	128	17 (13.3)	10 (7.8)	0.9 (0.1, 5.4)	13 (10.2)	4 (3.1)
HH Construction^#		*	***
Unfinished	530	137 (25.8)	84 (15.8)	0.8 (0.2, 4.1)	87 (16.4)	39 (7.4)
Finished	309	61 (19.7)	24 (7.8)	1.1 (0.2, 5.3)	37 (12)	14 (4.5)
B. Visit specific characteristics	N Total	N (%) positive for gametocytes		Median (IQR) density gametocyte/ul	N (%) with > = 1 gametocyte/ul	N (%) with > = 10 gametocyte/ul
Total Observations	7099	560 (7.9)		0.9 (0.2, 4.7)	269 (3.8)	85 (1.2)
Visit Type
Routine	6683	525 (7.9)		0.9 (0.2, 4.7)	256 (3.8)	82 (1.2)
Passive case detection	416	35 (8.4)		0.4 (0.2, 2.8)	13 (3.1)	3 (0.7)
Fever status
Fever	836	61 (7.3)		0.8 (0.3, 5.7)	28 (3.3)	9 (1.1)
No Fever	6263	499 (8)		0.9 (0.2, 4.3)	241 (3.8)	76 (1.2)
Net Use previous night^#		**
No net used	1597	155 (9.7)		0.9 (0.2, 4.6)	75 (4.7)	26 (1.6)
Net used	5070	369 (7.3)		0.9 (0.2, 5.1)	180 (3.6)	56 (1.1)
Season					*
Dry	3492	259 (7.4)		0.6 (0.2, 3.7)	113 (3.2)	38 (1.1)
Rainy	3607	301 (8.3)		1.1 (0.3, 5.2)	156 (4.3)	47 (1.3)
Proportions of participants with each of the outcomes were compared based on enrollment characteristics using two-sided chi-squared tests. Gametocyte density was compared between enrollment characteristics using Wilcoxon rank-sum tests. ^#missing data - HH head education level (173), SES (108), Household construction (108), Net use (432 – not collected at passive case detection visits [416] and missing in routine visits [16]) p-value *< 0.001, < 0.01, * < 0.05

gametocytes detected during multiple visits (p < 0.001), respectively (Table 1). Among covariates that were measured at each follow-up visit, gametocytes were more likely to be detected when participants reported they did not sleep under a bed net the night prior to the visit (p < 0.01). Furthermore, visits with higher gametocyte densities were more likely to occur during the rainy season compared to the dry season (p < 0.05 for gametocyte densities > 1 gametocyte/ul) (Table 1).

These findings contrast with the distribution of total parasitemia in the population, where a majority (72%: 678/947) of all participants had at least one Pf infection detected during the study (Supplemental Table 2). Among individuals who ever had Pf infections, 32% had gametocyte-containing infections. School-age children were both significantly more likely to have Pf infections (p < 0.001) and, among individuals with Pf infections, were twice as likely to have gametocyte-containing infections than other age groups (p < 0.001). While the type of visit (routine ACD vs. PCD) was not a significant predictor for gametocyte detection, participants were significantly more likely to have Pf parasites detected, and to have higher parasite densities at PCD visits compared to routine visits. Despite this, among visits with Pf infections detected, routine visits were significantly more likely to have gametocytes detected than PCD visits (Supplemental Table 2). These findings indicate that transmissible, gametocyte-containing, infections are not a random subset of all Pf infections.

Incidence of infections containing gametocytes is higher in school-age children

Longitudinal follow-up allowed us to calculate the incidence rate of Pf infections, gametocyte-containing infections, and infections with high-density gametocytes over time. We estimated the importance of predictors for the incidence rate of each outcome, accounting for individual follow-up time, using negative binomial models (Fig. 3; Supplemental Tables 3–5). School-age children had significantly higher incidence of infections containing gametocytes than other age groups, with a rate ratio (RtR) of 2.59 (95% CI 1.6–4.2) compared to under-5s, and this relationship was more pronounced when compared to the incidence any Pf infection (RtR 1.62 [95% CI 1.34, 1.96] comparing school-age children to under-5s). While school-age children had more frequent high-density gametocyte infections than children under 5, this relationship was not statistically significant. Reported net use at enrollment and higher SES were both significantly associated with lower rates of any gametocytes or high-density gametocytes. Overall, the predictors of the incidence rate of infections containing any gametocytes or high-density gametocytes were similar to predictors of the rate of any Pf infection; however, the magnitude of association, the incidence rate ratio, was larger for predicting gametocytes than Pf infection across all predictors (Fig. 3). Incidence rates, rate ratios and confidence intervals for all three outcomes are reported in Supplemental Tables 3–5.

Visit-specific longitudinal analyses showed increased odds of gametocyte-containing infections at PCD visits and visits with fever (Supplemental Table 6). However, visits with fever and PCD visits were rare (12% and 6%, respectively), limiting their contribution to the population-level transmission reservoir.

Gametocyte-containing infections cluster at the individual and household levels

To quantify the clustering of gametocyte-containing infections among certain individuals over time (Fig. 2), we examined the association of previous Pf infection or gametocyte detection on subsequent gametocyte carriage, accounting for repeated observations among individuals and clustering by household. Analyses were restricted to the 843 individuals with at least two visits with laboratory results, resulting in 6,152 observations where we could assess previous parasite or gametocyte detection (Table 2). Overall, neither ever having any prior Pf infection nor having Pf infection at the previous visit were associated with having a gametocyte-containing infection at the current visit. However, having any prior infections containing gametocytes was strongly associated with having gametocytes at the current visit (OR: 3.82, 95% CI: 1.75, 8.33), confirming clustering of gametocyte-containing infections among a subset of individuals.

Table 2

Association among individuals with at least 2 observations (N = 843) from 6,152 observations between having any gametocytes detected and visit-specific predictor variables from mixed effect logistic regression accounting for repeated measures and clustering by household
Visit-Specific Characteristics	Number of Observations, (% of total observations) (N = 6152)	OR of gametocyte-positive infection (95% CI)
Previously detected Pf parasitemia
Never previously having Pf detected	2735 (44.5%)	Ref
Any Previous Pf detected	3417 (55.5%)	1.42 (0.91, 2.24)
No Pf detected at previous visit	4770 (77.5%)	Ref
Pf detected at previous visit	1382 (22.5%)	0.96 (0.69, 1.34)
Previously detected gametocytes (restricted to all observations from 843 individuals with at least 2 observations)
Never previously had gametocytes	5055 (82.2%)	Ref
Previously had any gametocytes	1097 (17.8%)	3.82 (1.75, 8.33)
No Gametocytes detected at previous visit	5675 (92.2%)	Ref
Gametocytes detected at previous visit	477 (7.8%)	0.77 (0.54, 1.11)

We assessed whether increased incidence of gametocyte-positive visits within certain individuals was explained by clustering at the household level and found that most gametocyte-containing infections clustered within a few households (Fig. 4). After restricting analysis to households with at least 2 follow-up visits (n_household=220, n_individual=890), we found that only 16% (34/220) of households had gametocytes detected at multiple time points among multiple members. These 34 ‘high-gametocyte’ households contained 22% (195/890) of the study population and 63% (349/557) of gametocyte-positive visits (Table 3). This was significantly different (p < 0.001) than the corresponding distribution of Pf infections in the population, with 25% of all Pf infections occurring in households where there were no gametocytes detected at any time during follow-up.

Table 3

Distribution of households, individuals, *P. falciparum* infections, and gametocyte-containing infections by household types among all households with at least two completed study visits. Households defined as ‘high gametocyte’ (at least two individuals with at least two gametocyte-positive visits), ‘low gametocyte’, and ‘no gametocyte’. Examples shown in Fig. 3. SES: socioeconomic status, Pf: *Plasmodium falciparum*, Gam: gametocytes
	Household Type
Household Characteristics (row %)	High Gametocyte Households	Low Gametocyte Households	No Gametocyte Households	Chi square p-value	Total Population
Number of Households	34 (15.5%)	73 (33.2%)	113 (51.4%)		220
Household SES (missing = 21)
Low	9 (15.0%)	20 (33.3%)	31 (51.7%)	0.48	60
Medium	20 (18.5%)	38 (35.2%)	50 (46.3%)		108
High	2 (6.5%)	10 (32.3%)	19 (61.3%)		31
N individuals in households	195 (21.9%)	293 (32.9%)	402 (45.2%)		890
Age Group
Children under 5	41 (25.0%)	42 (25.6%)	81 (49.4%)	0.01	164
School-aged (5–15)	80 (24.9%)	116 (36.1%)	125 (38.9%)		321
Adults over 15	74 (18.3%)	135 (33.3%)	196 (48.4%)		405
Site
Namanolo	79 (19.1%)	168 (40.7%)	166 (40.2%)	< 0.001	413
Ntaja	116 (24.3%)	125 (26.2%)	236 (49.5%)	< 0.001	477
N Pf + infections	614 (38.2%)	592 (36.8%)	403 (25.0%)	< 0.001	1609
N Gam + infections	349 (62.7%)	208 (37.3%)	0 (0%)	< 0.001	557

To examine the drivers of household-level clustering of gametocyte containing infections, we created a continuous variable for ‘household level of gametocyte detection’ which accounted for the number of people in a household, the number of visits each person contributed, and the density of gametocyte-containing infections detected. In adjusted linear regression models accounting for household Pf infection levels, neither household SES nor site were associated with household level of gametocyte detection. However, increasing proportion of school-age children in the household remained highly predictive of increasing household level of gametocyte detection, even after adjustment for Pf infection levels (p < 0.01). This suggests that school-age children drive household-level gametocyte clustering, independent of Pf infections in the household. However, model residuals were not normally distributed, and distributions of both gametocyte levels and Pf infection levels (Fig. 5) showed strong evidence of spatial autocorrelation of infections in households by Moran’s I (p < 0.001). To account for this spatial autocorrelation, spatial lag models which adjusted for Pf infection levels were created. After accounting for spatial autocorrelation, increasing proportion of school-age children in the household was still significantly correlated with increasing household gametocyte levels (p = 0.04), but the magnitude of association was greatly decreased (0.15 vs 2.24) compared to models not accounting for spatial autocorrelating (Supplemental Table 7), suggesting unmeasured genetic or environmental features are also important drivers of household gametocyte levels. In contrast to household gametocyte levels, models examining predictors of household Pf levels showed that the proportion of school-age children did not predict household Pf infection levels after accounting for spatial autocorrelation (p = 0.9).

The majority of gametocytes are detected in school-age children

Finally, we estimated the distribution of gametocytes in the population over the total study period, accounting for density of infection, frequency of infection, frequency of follow-up visits, and probability of enrolling in the study. Overall, 82% of people counted in our census of the study area were enrolled in the study. However, the probability of being enrolled in the study differed significantly by age, which our results showed was highly associated with gametocyte carriage. Individuals over 15 years of age were the least likely age group to enroll in the study (24% not enrolled). After accounting for age-specific probability of enrollment, tree maps of total gametocyte abundance by population characteristics were produced (Fig. 6).

Of the total detected gametocyte abundance (sum of gametocyte densities) determined from routine ACD visits, and after adjusting for age-related enrollment bias, 53% of gametocytes were detected among school-age children who made up only 33% of the census population. In addition, 40% of gametocytes were detected among under-5 children who represented 18% of the census population, with the remaining 7% of gametocytes being detected in adults (49% of population) (Fig. 6a).

Among school-age children and adults, most gametocytes were detected during the rainy season, however among younger children, more gametocytes were detected in the dry season. This suggests that clearing all infections from school-age children during the rainy season would lead to a 67% decrease in the total gametocyte abundance in the population at that time, compared to similarly treating all under-5 children during the rainy season, which would decrease total gametocyte abundance by only 25%.

Figure 6a. Distribution of total gametocyte abundance in the population by characteristic, restricted to routine visit observations with reported net use (missing = 15), and reported SES (missing = 376 visits from 108 individuals), weighted for age-distribution in the census. N = 6,292 visits. *Among young children during the rainy season, reported net use was sufficiently common that the number of gametocytes among non-net users was negligible (0.2% of total) and cannot be seen.

Because we did not inquire about net use at PCD visits, we constructed separate tree maps to examine the difference in total gametocyte abundance between routine ACD and unscheduled PCD visits (Fig. 6b). PCD visits comprised only 6% of gametocyte positive visits. Furthermore, gametocyte densities were low at these health center visits, thus gametocytes detected during PCD visits made up a negligible fraction of all gametocytes in the population.

Figure 6b. Distribution of total gametocyte abundance in the population by characteristic including PCD (passive case/health center visit) vs ACD (routine visit), restricted to observations with reported SES (missing = 399 visits from 108 individuals), weighted for age-distribution in the census. N = 6,700.

We compared the distribution of gametocytes to the distribution of total Pf abundance using total Pf parasite density. At routine visits 84% of all Pf parasites were detected among school-age children, with only 7% detected among adults, and 10% among under 5 children (Fig. 6c). While the number of both gametocytes and total parasites was highest in school-age children, when comparing the treemaps for gametocytes and all Pf infection, the distribution of total parasite distribution by age group were significantly different than the distribution of gametocytes in the population (p < 0.0001). Among school-age children, most parasites were detected during the dry season, in contrast to the distribution of gametocytes.

When PCD visits were included (Fig. 7d), most Pf parasites in the population were still detected in school-age children. In contrast to gametocyte abundance, nearly half of all parasites among school-age children, and more than half of all parasites found in other age groups, were detected at PCD visits (Fig. 6d). While clearing parasites among school-age children at PCD visits alone would clear 61% of the total parasite abundance, it would decrease only 1% of the total gametocyte abundance in the population.

At a population level, school-age children in our study sites were the primary human reservoirs of transmissible P. falciparum parasites. Among a broad range of predictors and diverse analyses, we demonstrated that being of school-age was consistently the most important factor associated with gametocytemia. As a whole, our results reveal that abundant gametocyte carriage in school-age children occurs in a manner that goes beyond their higher overall rates of Pf infection. Thus, clearing infections in school-age children, especially during the rainy season, would remove a substantial portion of the gametocyte reservoir suggesting such targeted interventions could disproportionately reduce transmission in the community.

Defining the contributions of specific human reservoir groups of Pf transmission is complex and, as we demonstrated here, not simply a reflection of Pf infection risk. While Pf infection is, of course, necessary for gametocyte development, the distribution of gametocytes in the population is not random among people with Pf infections. While 72% of our study population had Pf infections detected at some point during follow-up, only one-third of these infected people ever had gametocytes detected. Although age is a strong predictor of both Pf infection and gametocyte detection, the association between age and gametocyte detection was stronger than the association between age and any Pf infection. School-age children with Pf infection were also more likely to have gametocytes than other age groups. These findings agree with prior studies evaluating gametocyte carriage among individuals with Pf infections, which have shown that people’s age is a strong predictor of gametocyte detection [17–19]. However, by using longitudinal data of a carefully characterized cohort, we showed that school-age children contribute disproportionately more to gametocyte carriage than would be expected by Pf infection rates alone, because gametocyte carriage occurs more frequently, and at transmission-relevant densities, than seen in other age groups.

Our finding that gametocyte-containing infections occur more often within certain individuals and households is intriguing. This gametocyte clustering was consistent in both spatial and longitudinal analyses that compared prior gametocyte carriage and prior Pf infection as predictors of subsequent gametocyte carriage, and was not fully explained by measured predictors, including age group. These results suggest that additional genetic or immunologic host characteristics, or perhaps relevant environmental factors, may influence gametocyte carriage. We anticipated that age group would explain most of the variation, as age is also a surrogate for developing immunity, with most school-age children having achieved immunity to clinical malaria disease permitting chronic infections with higher parasite densities. Development of immunity to early-stage gametocytes [20] could result in some individuals, especially older children and adults, being less likely to have gametocyte-containing infections. Environmental factors that elevate mosquito vector density will inherently influence the distribution of Pf infections, but could additionally contribute to gametocyte carriage, as they reflect local heterogeneity in exposure and longer-term development of immunity. Clustering of gametocyte carriage within households that was not explained by age, immunity, or environmental factors could be related to host genetics, as those living in the same household are likely related. Other studies have also described how a few highly infectious individuals can drive transmission [10].

This study was able to quantify the relative contribution of clinical disease and asymptomatic infections to gametocyte abundance in the population because we collected information from a large number of community members of all ages and both sexes across high- and low-transmission seasons, using both routine ACD spaced regularly over time and PCD of study participants presenting to the health center. Specifically, we observed that most gametocytes were detected during routine ACD visits. This result is consistent with prior studies of human Pf infection showing that gametocytes are frequently detected in asymptomatic infections [21–23]. Our results suggest that interventions aimed at improved case management or adding transmission blocking drugs to treatment regimens are unlikely to substantially reduce transmission, as most gametocytes in the population are detected outside of health center visits.

This population-based study with intensive follow-up is the largest and most comprehensive examination of gametocyte distribution in a moderate-to-high transmission setting. However, there were a few limitations. First, the study was designed to capture, to the best of our ability, the total population allowing household members to enroll over time if they missed the initial enrollment visit. Despite this, a significant fraction of adult males in the census population did not enroll in the study, and among those that did enroll, adult males contributed fewer observations than any other group. We attempted to correct for this by weighting the study observations by the census population distribution. However, the baseline census was done during the household registration of the national net distribution campaign, which may have led to inclusion of non-permanent household members. Furthermore, many men in the study area work outside of the district or country. Thus, we may have overestimated these individuals’ contributions to transmission if they were truly not present in the study area during the study period. Second, easier access to care at the local health center due to the presence of a study nurse to support participants may have increased the likelihood that participants would seek care. This may have biased our results toward having more PCD visits than in a non-study population. Third, our study took place in two districts in rural Southern Malawi, where malaria transmission is perennial with a seasonal peak. While transmission patterns are geographically specific, the epidemiologic patterns we observe in terms of distribution of infection are similar to other areas in Malawi and across the region. Thus, our results are likely generalizable to these areas.

Previous research indicates that targeting malaria interventions to school-age children can lead to indirect decreases in transmission among other groups [24–26]. Large cluster-randomized trials have found that, even with imperfect coverage, targeting parasite-clearing treatment to all school-age children leads to decreased community-level malaria prevalence [24, 25]. The community-level impacts of clearing infections in school-age children have also been predicted by agent-based simulation models across transmission settings [26]. Here, we provide evidence from a large, well-characterized population suggesting that clearing infections in asymptomatic school-age children could reduce the total population of transmission-relevant malaria parasites in the community by more than half. In combination with previous trials, we believe this is strong evidence to support recommendations to target school-age children in moderate-to-high transmission settings with additional interventions to clear and prevent infections. Indeed, progress toward further malaria control and elimination may not be achieved until infections in this reservoir group are reduced.

Study Design: We conducted a prospective, longitudinal, open-enrollment, cohort study in two districts (Machinga and Balaka, Fig. 1) in southern Malawi from April 2019 until May 2020. In the year prior to the study the estimated incidence of clinical malaria was 195 and 572 per 1000 people in the health-center catchment areas of Ntaja (Machinga District) and Namanolo (Balaka District), respectively. Both areas have perennial transmission, with a seasonal peak typically from January to May. The health catchment area of Namanolo is low-lying, along the Shire River, and received pyrethroid-only LLINs in the 2018 national bed net distribution campaign. The catchment of Ntaja, however, is higher in elevation with rolling hills and streams, and most recently received LLINs containing piperonyl-butoxide in addition to pyrethroid.

During September and October 2018, households in the catchment area of each health center were mapped and enumerated. Each catchment area was divided into quadrants, and four index households were sampled randomly using a random number generator from each quadrant. The “head of household” from each index household and its nearest five-six neighboring households were offered enrollment. Thus, we enrolled sixteen (four per quadrant) clusters of households per health center (32 clusters in total). If households relocated out of the catchment area, became inaccessible during the rainy season, or withdrew from the study, they were replaced. At household enrollment, individual consent was sought from/for each household member.

Participants were invited to attend monthly routine active case detection (ACD) visits at a convenient location every 4 to 6 weeks after enrollment. At ACD visits, data were collected on bed net use, symptom status, and body temperature, and a finger-prick blood sample was obtained for later detection and quantification of total Pf parasites and gametocytes. Passive case detection (PCD) for malaria was conducted at the local health center. Individuals who presented to the health center were assessed for fever and other symptoms, tested with malaria rapid diagnostic test (both SD Bioline Malaria Ag Pf, Standard Diagnostics and Paracheck Pf by Orchid Biomedical Sciences were used based on availability at the health center), and treated as appropriate. A study nurse supported participants’ flow through the regular evaluation process provided by health center staff, and also collected the study-specific blood sample. Individuals who reported symptoms or fever at routine visits were referred to the health center.

Laboratory Assays: During both active and passive case detection, finger-prick blood samples were obtained from 50µl whole blood dried on Whatmann 3mm filter paper and stored with desiccant for Pf infection detection. For gametocyte detection and quantification, 100µl whole blood was placed in RNA Protect cell reagent (Qiagen, CA) and stored at -80°C. Methanol extraction was used to obtain DNA from filter papers [27], and the Pf 18S ribosomal RNA gene was detected and quantified using qPCR [28]. Samples positive for Pf underwent gametocyte detection. To detect and quantify gametocytes, male (pfmget) and female (ccp4) gametocyte-specific RNA transcripts were detected and quantified using RT-qPCR [29, 30]. Analyses are based on total gametocyte density. The relationship of total gametocyte density and gametocyte sex ratio stratified by age group is presented in Supplemental Fig. 1.

Data analysis: Household data collected within 29 days of household enrollment included household construction materials, presence of bed nets in the house, socioeconomic indicators, household study site (Namanolo vs Ntaja), and head of household education level (no education, any primary school, and secondary school or beyond). Household construction was categorized as “finished” if at least two of the following was observed: roof, floors, or walls constructed from modern materials. SES index was calculated using inverse probability weights of standard socioeconomic indicators including possession of a radio, tv, mobile phone, bike, knives, chairs, tables, bed, mattress, cabinet, livestock, and a private toilet. Elevation (< 600 meters or > 600 meters) was correlated with site and was, thus, not considered in the analysis.

Participant characteristics assessed/defined at enrollment included age group [categorized as under-5 years, school-age children (5–15 years), and over-15 years] and sex.

Visit-specific characteristics examined included presence or absence of reported or measured fever, passive case detection (sick visits to the health center) vs routine ACD visit, season of the visit (dry vs rainy), and presence or absence of gametocytes or any Pf parasites at previous visits.

Ever had an infection containing gametocytes:

We first examined enrollment and visit-specific characteristics for associations between the following outcomes: ever detected gametocytes during follow-up, detected gametocytes at more than one visit, ever detected gametocytes at density greater than or equal to 1/µl and ever detecting gametocytes at a density greater than or equal to 10/µl during follow-up. These cut points were chosen based on previous research indicating densities over these thresholds are associated with increased probability of transmission [15]. Proportions of participants with each of the outcomes were compared based on enrollment characteristics using two-sided chi-squared tests. Gametocyte density was compared by enrollment characteristics using Wilcoxon rank-sum tests.

Rate of gametocyte detection:

We calculated the incidence rate of 1) any Pf infection, 2) any gametocyte detection in the total population, and 3) any high-density (≥ 1 or ≥ 10/µl) gametocyte detection in the total population per individual participant follow-up time by enrollment predictors among the total population. The number of observations positive for gametocytes was overdistributed (variance greater than mean) for all outcomes, so negative binomial regression with an offset for log(follow-up time) was used to estimate the association between enrollment predictors and incidence rate of gametocyte detection.

Odds of gametocyte detection over time:

We examined the longitudinal odds of gametocyte detection at a given visit based on enrollment and follow-up predictors. Mixed effect logistic regression was used to account for clustering due to repeated measures within individuals and multiple individuals per household. To examine the impact of previous Pf or gametocyte detection, we restricted the analysis to individuals with at least two visits where PCR for Pf was done.

Household clustering of gametocyte detection:

To explore how gametocytes were distributed among households, we categorized households into three groups: High-gametocyte households had at least two household members with high-density gametocytes detected on two or more visits. Low-gametocyte households had at least one member with gametocytes detected at least once, but did not meet the criteria for high gametocyte households. No-gametocyte households had no individuals in the household with gametocytes ever detected. To compare whether the clustering of gametocytes could be predicted by detection of any Pf infection, we compared the proportion of all observations with Pf infections detected in the three household types to the proportion of all observations with gametocytes detected in the three household types using two-sided chi-squared tests.

To better examine the drivers of household clustering, we further evaluated the level of household gametocyte detection by adding up all gametocyte detections in a household, weighting a given detection by 3 if it was a high-density (> 10gams/µl) gametocyte detection [15] to account for the non-linear increase in probability of transmission at high gametocyte densities, and dividing by the total number of person-samples a household contributed to the study (total number of visits of all people in the household). This produced a continuous index of household-level gametocyte intensity in each household. Similar methods were used to examine clustering of any Pf parasites, however Pf detections were weighted by 5 if they were high-density (> 200 parasites/µl) [31].

Ordinary least squares regression was used to evaluate household socioeconomic status, study site, and the proportion of school-age children in the household as predictors of household gametocyte intensity. We additionally assessed spatial autocorrelation of household gametocyte level between households using Moran’s I test, and conducted spatial lag regressions to determine the extent that non-household environmental characteristics were driving household gametocytemia levels.

Gametocyte distribution in the population:

We quantified the distribution of total gametocyte abundance in the population by examining the distribution of total summed gametocytes detected. For instance, the number of gametocytes detected among children under age five was calculated as the sum of all gametocyte density values for all children under five. This allows us to account for not just the number and percent of individuals with gametocytes but the subgroup contribution to the total gametocyte abundance in the population.

Not all individuals in all selected households enrolled into the study, and this enrollment was not equal across groups. To account for bias in enrollment demographics, we used an initial census of selected households to create census-weighted estimates of the distribution of gametocytes in the population. To do this, we multiplied the age-specific numbers of total gametocytes by inverse-probability weights for the age-specific probability of enrolling in the study.

Data Availability

Prior to publication analysis code will be available on GitHub. Data will be available on ClinEpiDB. Interim data requests should be submitted via email to both Lauren Cohee ([email protected]) and Alfred Matengeni ([email protected]).

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Asymptomatic school-aged children carry the majority of transmissible Plasmodium falciparum infections

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Abstract

Figures

Introduction

Results

Gametocyte-containing infections are not evenly distributed in the study population

Incidence of infections containing gametocytes is higher in school-age children

Gametocyte-containing infections cluster at the individual and household levels

The majority of gametocytes are detected in school-age children

Discussion

Methods

Ever had an infection containing gametocytes:

Rate of gametocyte detection:

Odds of gametocyte detection over time:

Household clustering of gametocyte detection:

Gametocyte distribution in the population:

Data Availability

References

Additional Declarations

Supplementary Files

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