Introduction
Considerable progress has been made towards eliminating malaria over the years, with an unprecedented reduction in disease burden between 2000 to 2010, albeit with progress stalling between 2010 to 20151. Causality is complex, but reductions have been attributed to the increased use of insecticide treated nets and the adoption of highly effective artemisinin combination therapies (ACTs) as the first line treatment for malaria2–4.
Malaria is transmitted via gametocytes taken up during a blood meal by female Anopheles mosquitoes. Gametocytes are produced when a proportion of the asexual parasites, an average of 1 gametocyte per 156 asexual parasites5, commit to sexual development during a malaria infection. It is not yet clear what factors drive this commitment but it has been proposed that factors such as drug pressure, an unfavourable environment within the host including host immunity6,7 and parasite factors such as the contents of extracellular vesicles released from infected erythrocytes8,9 may play a role. Genetic influences, in particular, variants of the β-globin locus have also been shown to influence gametocyte production in asymptomatic infections where variants that protect against severe malaria10 are associated with an increased rate of gametocyte production11,12.
Older antimalarials such as chloroquine (CQ) and sulfadoxine-pyrimethamine (SP) were active primarily against asexual parasites and had limited activity against gametocytes, particularly when resistance emerged13,14. Primaquine is active against mature gametocytes, and the World Health Organisation (WHO) recommends a single low dose of 0.25 mg/kg primaquine for use in low transmission areas to reduce malaria transmission15. Although not active against the mature gametocytes, ACTs act against early stage gametocytes and consequently reduce gametocyte carriage4,6.
Identifying prognostic indicators of gametocyte carriage is key to the successful implementation of interventions aimed at reducing malaria transmission. Several studies have examined the epidemiology of gametocyte carriage but these have largely been single surveys14,16–18 or limited to short-term follow up19,20. We carried out an analysis of data collected over 19 years of follow-up in a longitudinal cohort established at the Kenyan coast, during a period of changing malaria transmission and changing drug use. Here, we sought to describe the distribution and prevalence of Plasmodium falciparum gametocyte carriage within this cohort and changing prevalence over time, and thus identify potential risk factors for gametocyte carriage.
Methods
Study design and data collection
Cohorts of children recruited into the Kilifi Malaria Longitudinal Cohort study were located in Kilifi County at the Kenyan coast (Figure 1)21–23. Three cohorts located in areas of varying transmission intensity were included, that is, Ngerenya (initially moderate transmission but falling to low transmission), Junju (moderate transmission) and Chonyi (high transmission). Malaria transmission intensity is higher during the rainy seasons with the long rainy season occurring between May-July and short rainy season between October-December21,24. We analysed data from cross-sectional surveys conducted within the three cohorts. Data included in the analyses were from cross-sectional surveys conducted from 1998 to 2016 for Ngerenya (a cross-sectional survey was not conducted in 2006); from 1999 to 2001 for Chonyi; and from 2007 to 2016 for Junju.
Enlarge this image.Figure 1. Map of the Kilifi Malaria Longitudinal Cohort study area located within the Kilifi Health and Demographic Surveillance System (KHDSS).
Coloured points show the location of the participants homesteads within the three cohorts, Chonyi (orange); Junju (green); and Ngerenya (red). KCH, Kilifi County Hospital.
In Ngerenya and Chonyi, households were selected randomly with 72 households selected in Ngerenya (819 participants) and 52 households in Chonyi (783) participants21,22. This sample size was considered appropriate for a study on the definitions of clinical malaria. Participants willing to continue follow-up were included as a pragmatic study size for future studies. For Junju, participants were recruited from 405 children who previously participated in a malaria vaccine trial23. The sample size was determined based on an expected febrile malaria incidence of 50%. Enrolling 400 children would then allow detection of 35% vaccine efficacy with 80% power. Children born into the households were then subsequently recruited into the three cohorts over time. All study participants had access to healthcare facilities with the same study protocol applied to all cohorts per year. Children were actively monitored for malaria by weekly visits to identify febrile episodes and by cross-sectional surveys for asymptomatic parasitaemia, and exited follow up when they were 15 years of age25.
To reflect the marked decline in transmission intensity observed in Ngerenya over the follow-up period, for analytical purposes, Ngerenya was divided into Ngerenya early, which included data collected during a period of moderate transmission (1998–2001), and Ngerenya late, which included data collected during the period of moderate to low transmission (2002–2016)26.
Ethics approval and consent to participate
Approval for participation in these cohort studies was given by Kenya Medical Research Institute Ethics Review Committee (reference numbers KEMRI/SERU/CGMRC//3149 and SSC1131), and research was conducted according to the principles of the Declaration of Helsinki, which included the administration of informed consenting in the participant’s local language prior to any study procedure. Written informed consent for participation in this study was provided by the parents of the children included in this study.
Case detection
Active malaria surveillance was performed during weekly follow-up visits, carried out as described previously21,27. Briefly, households in the three cohorts were visited by a field worker every week where axillary temperature was recorded for each study participant. If the participant had a fever or history of fever, they had a blood smear performed by the field worker to diagnose malaria infection. From 2007 onwards, rapid diagnostic tests (RDTs) were available in the field to guide treatment decisions, but even before RDTs were available all febrile malaria episodes were treated. Field workers were resident in the villages where study participants lived, and were available to assess febrile episodes arising before a scheduled visit was planned. Treatment was freely provided by the field workers and the drug administered in a particular year was based on the Government of Kenya national guidelines for treatment of malaria.
Cross-sectional parasitological surveys
To analyse gametocyte and asexual parasite prevalence, data from cross-sectional surveys mainly taken before the beginning of the long rainy season to assess asymptomatic P. falciparum infections were used. There were approximately 364 (range, 139–556) participants in each survey, and a detailed summary of the cross-sectional surveys included in the analysis with the number of participants attending each survey is presented in Supplementary Table 128.
Laboratory investigations
Thick and thin blood films were taken from all children at each cross-sectional survey and for children presenting with fever during the weekly follow-up visits. The thin blood films were fixed with 100% methanol and stained with 3% Giemsa stain for 45 minutes before being examined for parasites. Thick films were air-dried before staining. If there were more than 25 parasites per high powered field on the thick film then the thin film was used for counting, otherwise the thick film was used. Asexual parasite densities were determined per microliter of blood and were calculated as the number of parasites per 200 white blood cells (WBCs) for thick films or per 500 red blood cells (RBCs) for thin films. The final parasitaemia was then calculated in reference to the actual full blood count (if available) or estimated assuming a WBC count of 8 × 109 per litre or an RBC count of 5 × 1012 per litre. In total, 100 high-powered fields of a thick film were read before ascertaining that no parasites were present.
Gametocytes were counted when observed during application of the protocol for asexual parasites, and hence the numbers of fields examined during which gametocytes may be observed varied depending on the asexual parasitaemia. Malaria parasite and gametocyte counts were determined by two independent readers and discordant readings resolved by a third reader. Quality assurance over the study period included comprehensive microscopy training during induction and at regular intervals using internal and external quality control. For internal quality control, a subset of slides selected quarterly are re-read by the microscopy team and concordance between the results checked. For external quality control, at the beginning of the cohort study, this involved reading reference blood films from a partner lab in the United Kingdom. Currently, external quality control involves participating in three annual surveys carried out by the National Institute of Communicable Diseases (NICD) based in South Africa where they send 20 slides per survey to our lab for proficiency testing.
A subset of samples were typed for sickle cell genotype and α-thalassaemia, as previously described29,30.
Case definitions
To determine malaria episodes, data from the weekly follow-up visits were used and malaria episodes defined using previously described cut-offs21. For the weekly follow-up visits, temperature was recorded for the participants, and for those with a fever, a blood film was taken and analysed as above. For children <1 year of age, clinical malaria was defined as a fever (axillary temperature ≥37.5°C) with any parasitaemia while for children between 1–15 years of age malaria was defined as fever accompanied by parasitaemia of ≥2,500 parasites/µl of blood. For estimates of the number of malaria episodes per participant per survey, malaria episodes were considered as unique only if the time difference between two consecutive malaria episodes was ≥28 days. Malaria episodes occurring in the interval between two cross-sectional surveys, the respective survey (survey x) and the prior survey (survey x-1), were identified, summed up and defined as the number of malaria episodes occurring in the period leading up to each respective survey.
Statistical analysis
To assess the relationship between variables, Spearman’s rank correlation coefficients were calculated. Models to predict gametocyte positivity were fitted using the following known covariates shown to be associated with gametocyte carriage6,31: asexual parasite positivity, age, year, number of malaria episodes and whether the participant had a malaria episode, asexual parasite positive blood film or gametocyte positive blood film in the prior cross-sectional survey. The variable asexual parasite positive included all infections – asymptomatic and symptomatic. Age was included as a categorical variable with the age-group ‘5–9 years’ was chosen as the reference group as the numbers in this group were large and allowed clearer presentation of the risks in other groups. Poisson and logistic regression models were evaluated and the best model for the data determined by comparing the Akaike information criterion (AIC). To correct for repeated measures per individual, robust standard errors were calculated with allowance for clustering to account for non-independence of observations. Observations with missing data were excluded from the analyses. Variance inflation factors were also calculated to assess multicollinearity among the covariates included in the model (Supplementary Table 228). Probability values (p) of less than 0.05 were considered statistically significant. All statistical analyses were carried out in R statistical software via RStudio version 1.1.46332.
Results
Demography
For the study, a total of 19,580 observations from 2,703 children (Figure 2) derived from cross-sectional surveys carried out between 1998 and 2016 (Figure 3) were considered for analysis. There were 3 study participants missing in the cohort registry, 2,817 observations were aged >15 years and therefore excluded from the main analysis leaving 16,760 observations from 2,223 study participants for the main analysis, translating to over 9,134 person-years of observation. A total of 557,237 observations from the weekly follow-up data were also used in the analysis. The demographic characteristics of study participants participating in the cross-sectional surveys are presented in Table 1, the characteristics of the study participants participating in the weekly follow-up visits are provided in Table 2.
Enlarge this image.Figure 2. Flow diagram showing observations from cross-sectional surveys and weekly follow-up visits carried out on children recruited into the Kilifi Malaria Longitudinal Cohort.
Reasons for exclusion at each step are also included.
Enlarge this image.Figure 3. Summary of the number of cross-sectional surveys carried out per cohort and malaria drug in use per year for each cohort.
Bar graphs showing the number of cross-sectional surveys and the year of the surveys carried out during the follow-up period included in this analysis. Arrows above the bar plots indicate the malaria drug in use for each year with blue lines denoting chloroquine (CQ), red lines denoting sulfadoxine-pyrimethamine (SP) and green lines denoting artemisinin combination therapies (ACT).
Table 1. Demographic characteristics of study participants participating in the cross-sectional surveys.
| Variable | Cohort | |||
|---|---|---|---|---|
| Ngerenya | Chonyi | Junju | ||
| Early | Late | |||
| Total number of observations from study participants | 3636 | 5261 | 3102 | 4761 |
| Total number of females (%) | 1714 (47.1) | 2417 (45.9) | 1513 (48.8) | 2391 (50.2) |
| Person-years of follow-up | 882 | 4164 | 984 | 3104 |
| Number per age group (%) | ||||
| <0.5 years | 151 (4.2) | 133 (2.5) | 117 (3.8) | 77 (1.6) |
| 0.5–1 year | 157 (4.3) | 186 (3.5) | 152 (4.9) | 182 (3.8) |
| 1–5 years | 1199 (33.0) | 1745 (33.2) | 970 (31.2) | 1577 (33.1) |
| 5–9 years | 1078 (29.6) | 1900 (36.1) | 957 (30.9) | 1792 (37.6) |
| 9–12 years | 725 (19.9) | 889 (16.9) | 598 (19.3) | 729 (15.3) |
| 12–15 years | 326 (9.0) | 408 (7.8) | 308 (9.9) | 404 (8.5) |
| Total number of asexual parasite positive observations (%) | 984 (27.1) | 199 (3.8) | 1183 (38.1) | 850 (17.9) |
| Total number of gametocyte positive observations (%) | 164 (4.5) | 20 (0.4) | 142 (4.6) | 38 (0.8) |
| Total number of malaria episodes* | 899 | 419 | 530 | 2941 |
| Missing data (%) | ||||
| Gametocyte density | 0 | 47 (0.9) | 0 | 71 (1.5) |
| Asexual parasite density | 0 | 34 (0.6) | 0 | 69 (1.4) |
| Temperature | 432 (11.9) | 22 (0.4) | 21 (0.7) | 0 |
*Malaria episodes calculated from weekly follow-up data for all study participants who had complete data on gametocyte density.
Table 2. Demographic characteristics of the participants participating in the weekly follow-up visits.
| Variable | Cohort | |||
|---|---|---|---|---|
| Ngerenya | Chonyi | Junju | ||
| Early | Late | |||
| Total number of observations from study participants | 85910 | 179713 | 65431 | 226183 |
| Total number of females (%) | 41011 (47.7) | 82995 (46.2) | 31780 (48.6) | 113122 (50.0) |
| Number per age group (%) | ||||
| <0.5 years | 3612 (4.2) | 5032 (2.8) | 2714 (4.1) | 4968 (2.2) |
| 0.5–1 year | 3818 (4.4) | 6326 (3.5) | 2937 (4.5) | 7497 (3.3) |
| 1–5 years | 29211 (34.0) | 57704 (32.1) | 20434 (31.2) | 76800 (34.0) |
| 5–9 years | 25393 (29.6) | 63612 (35.4) | 20074 (30.7) | 76496 (33.8) |
| 9–12 years | 16577 (19.3) | 32156 (17.9) | 12693 (19.4) | 37452 (16.6) |
| 12–15 years | 7299 (8.5) | 14883 (8.3) | 6579 (10.1) | 22970 (10.2) |
| Total number of asexual parasite positive observations (%) | 4114 (4.8) | 1072 (0.6) | 3015 (4.6) | 5900 (2.6) |
| Total number of gametocyte positive observations (%) | 179 (0.2) | 73 (0.04) | 180 (0.3) | 69 (0.03) |
| Total number of malaria episodes | 1055 | 349 | 605 | 3493 |
Parasite prevalence and density over time
Variation in the proportion with a positive blood film for P. falciparum asexual parasites or gametocytes over the period of follow-up was analysed for each of the cohorts (Figure 4). In all the cohorts, the proportion positive for gametocytes was much lower than the proportion positive for asexual parasites. The overall correlation of gametocyte and asexual parasite prevalence over time is ρ = 0.78 (Spearman’s rank correlation, p<0.0001) indicating a paralleled decline in sexual and asexual parasitaemia. This was only true, however, in Ngerenya (both early and late transmission periods) and Chonyi. For Junju the temporal variation was more random, (the calculated correlation coefficient ρ = -0.09, p = 0.8) demonstrating the absence of a strong relationship between asexual and sexual parasite prevalence over time in this cohort. On the other hand, gametocyte and asexual parasite densities did not differ significantly over time in the cohorts (Figure 5 and Figure 6).
Line plot showing the temporal variation in P. falciparum parasite prevalence as determined by microscopy. Overall Spearman’s rank correlation of the gametocyte and asexual parasite prevalence temporal variation was ρ=0.78 (p<0.0001).
Blue lines indicate mean values with the shaded grey areas representing 95% confidence intervals. (A) Ngerenya early; (B) Chonyi; (C) Junju; and (D) Ngerenya late.
Enlarge this image.Figure 6. Scatter plots showing the change in asexual parasite densities over time.
Blue lines indicate mean values with the shaded grey areas representing 95% confidence intervals (A) Ngerenya early; (B) Chonyi; (C) Junju; and (D) Ngerenya late.
To determine the association with age, an age-dependent variation in the proportion parasitaemic was also analysed in each cohort (Figure 7). There was a peak prevalence of gametocytaemia among younger children in Chonyi (a high transmission setting) and Ngerenya early (a moderate to high transmission setting). For Junju (moderate to low transmission setting) and Ngerenya late (low transmission setting) sexual parasitaemia was less prevalent with no clear evidence of a peak.
Line graphs showing the variation in the proportion with gametocyte and asexual parasite positive blood films in the different age groups (0–0.5 years, 0.5–1 years, 1–5 years, 5–9 years, 9–12 years and 12–15 years) within the (A) Ngerenya early; (B) Chonyi; (C) Junju; and (D) Ngerenya late cohorts. The error bars indicate 95% confidence intervals.
We further analysed the distribution of the number of parasite positive events per study participant for each of the cohorts. For each cohort the number of blood films taken (per individual) with the highest frequency was determined separately (Supplementary Figure 128) and the analysis then restricted to individuals who had had the same number of blood films taken per cohort to avoid bias. The distribution of gametocyte positive events was approximately binomial (Figure 8), while that of asexual parasite positive events was not (Figure 9). This indicates that the frequencies of gametocyte carriage by individual approximates a binomial distribution, in contrast to the frequencies of asexual parasite carriage where we see a disproportionate number of individuals with a higher number of asexual parasite positive events than would be predicted.
Enlarge this image.Figure 8. Distribution of individuals with multiple gametocyte positive blood films.
Bar plot showing the number of individuals positive for gametocytes across all cohorts at the maximum blood film number for each cohort. Lines indicate the expected values for a binomial distribution. (A) Ngerenya early; (B) Chonyi; (C) Junju; and (D) Ngerenya late.
Enlarge this image.Figure 9. Distribution of individuals with multiple asexual parasite blood films.
Bar plot showing the number of individuals positive for asexual parasites across all cohorts at the maximum blood film number for each cohort. Lines indicate the expected values for a binomial distribution. (A) Ngerenya early; (B) Chonyi; (C) Junju; and (D) Ngerenya late.
Factors predicting gametocyte positivity
We tested associations between the following covariates: asexual parasite positivity, age, cohort, number of malaria episodes and whether an individual was gametocyte positive or asexual parasite positive during the prior survey or had malaria episodes in the prior survey. Additionally, we compared using asexual parasitaemia as a binary variable (positive versus negative) and as a log-transformed continuous variable (with parasite negative individuals indicated as having one parasite per microlitre) (Supplementary Table 328). We found a better fit for the model with asexual parasitaemia as a binary variable.
From the univariable analysis, having a blood film positive for asexual parasites, increased number of clinical malaria episodes in the survey period, and being positive for either asexual parasitaemia or gametocytes in the prior survey were all associated with increased odds of being gametocyte-positive (Table 3). Residing in a lower-transmission setting (Junju and Ngerenya late) relative to high transmission setting (Chonyi) and older age, however, were associated with a decreased odds of being gametocyte positive. In the multivariable analysis all these factors remained significant independent predictors, except for asexual parasite positivity in the prior survey.
Table 3. Logistic regression model predicting gametocyte positivity.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 6.70 | 5.38, 8.34 | <0.0001 | 4.68 | 3.42, 6.39 | <0.0001 |
| Age group | ||||||
| 5 – 9 years | 1.00 | . | . | 1.00 | . | . |
| 0 – 0.5 years | 0.53 | 0.22, 1.31 | 0.1683 | 1.69 | 0.38, 7.48 | 0.4868 |
| 0.5 – 1 years | 1.21 | 0.71, 2.08 | 0.4842 | 2.36 | 1.29, 4.33 | 0.0054 |
| 1 – 5 years | 1.44 | 1.12, 1.85 | 0.0041 | 1.75 | 1.30, 2.36 | 0.0002 |
| 9 –12 years | 0.62 | 0.43, 0.90 | 0.0293 | 0.62 | 0.41, 0.93 | 0.0219 |
| 12 – 15 years | 0.55 | 0.32, 0.94 | 0.0118 | 0.67 | 0.37, 1.23 | 0.1961 |
| Cohort | ||||||
| Chonyi | 1.00 | . | . | 1.00 | . | . |
| Junju | 0.17 | 0.12, 0.25 | <0.0001 | 0.21 | 0.13, 0.34 | <0.0001 |
| Ngerenya early | 0.98 | 0.77, 1.25 | 0.8991 | 1.16 | 0.87, 1.54 | 0.3240 |
| Ngerenya late | 0.08 | 0.05, 0.13 | <0.0001 | 0.18 | 0.11, 0.32 | <0.0001 |
| Number of malaria episodesi | 1.27 | 1.17, 1.38 | <0.0001 | 1.37 | 1.20, 1.56 | <0.0001 |
| Number of malaria episodes in the prior survey | 1.04 | 0.89, 1.21 | 0.6268 | 0.91 | 0.74, 1.12 | 0.3722 |
| Gametocyte positive in the prior survey | 4.58 | 2.95, 7.13 | <0.0001 | 2.00 | 1.25, 3.20 | 0.0039 |
| Asexual parasite positive in the prior survey | 2.16 | 1.68, 2.80 | <0.0001 | 0.86 | 0.64, 1.16 | 0.3321 |
Log likelihood test for model including age as a covariate p<0.0001. Robust standard error estimation; Wald test F statistic - 36.17, p<0.0001. i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. The p values in bold are statistically significant at significance level 0.05
Associations were consistent when the models were fitted separately for each cohort (Table 4–Table 7), except that the number of clinical malaria episodes in the survey period had different associations in the different cohorts. The number of malaria episodes occurring in the period leading up to a cross-sectional survey were associated with increased odds of gametocyte positivity in Chonyi (OR 1.49, 95% CI 1.06-2.12, p = 0.0234), Ngerenya early (OR 1.68, 95% CI 1.34-2.12, p <0.0001) and Ngerenya late (OR 1.30, 95% CI 0.60-2.81, p = 0.5018). However, in Junju the number of malaria episodes was associated with reduced odds of gametocyte positivity (OR 0.73, 95% CI 0.40-1.33, p = 0.3037). We tested the interaction between the number of malaria episodes and cohort in a logistic regression and confirmed that the variation in effect of malaria episodes was statistically significant (p = 0.0297) (Table 8). Furthermore, noting that asexual parasitaemia was quantitatively more strongly associated with gametocytaemia in Junju than in other cohorts, we tested the interaction between being asexual parasite positive in the three cohorts. We found that relative to Chonyi, there was an observed increased odds of gametocyte positivity with asexual parasite positivity only in Junju (p = 0.0007) and Ngerenya late (p = 0.0008).
Table 4. Logistic regression model predicting gametocyte positivity using data from Ngerenya early cohort only.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 3.16 | 2.29, 4.35 | <0.0001 | 3.14 | 2.07, 4.78 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | 0.25 | 0.06, 1.04 | 0.0570 | 3.01 | 0.66, 13.80 | 0.1565 |
| 0.5–1 years | 0.62 | 0.24, 1.59 | 0.3225 | 0.88 | 0.28, 2.78 | 0.8336 |
| 1–5 years | 1.36 | 0.94, 1.95 | 0.1025 | 1.63 | 1.03, 2.57 | 0.0363 |
| 9–12 years | 0.46 | 0.26, 0.80 | 0.0057 | 0.59 | 0.31, 1.13 | 0.1094 |
| 12–15 years | 0.36 | 0.14, 0.94 | 0.0361 | 0.52 | 0.17, 1.56 | 0.2430 |
| Number of malaria episodesi | 1.85 | 1.54, 2.22 | <0.0001 | 1.68 | 1.34, 2.12 | <0.0001 |
| Number of malaria episodes in the prior survey | 1.01 | 0.73, 1.39 | 0.9738 | 0.81 | 0.58, 1.13 | 0.2134 |
| Gametocyte positive in the prior survey | 2.23 | 1.20, 4.16 | 0.0113 | 1.75 | 0.85, 3.60 | 0.1266 |
| Asexual parasite positive in the prior survey | 1.12 | 0.75, 1.67 | 0.5731 | 0.82 | 0.51, 1.30 | 0.3935 |
| Year | 0.72 | 0.61, 0.86 | 0.0002 | 0.86 | 0.62, 1.19 | 0.3559 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 8.84, p<0.0001. The p values in bold <0.05.
Table 5. Logistic regression model predicting gametocyte positivity using data from Chonyi cohort only.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 2.96 | 2.07, 4.22 | <0.0001 | 3.05 | 1.90, 4.88 | <0.0001 |
| Age group | ||||||
| 5 – 9 years | 1.00 | . | . | 1.00 | . | . |
| 0 – 0.5 years | 0.52 | 0.16, 1.70 | 0.2804 | N/A | N/A | N/A |
| 0.5 – 1 years | 1.55 | 0.74, 3.21 | 0.2433 | 3.43 | 1.55, 7.57 | 0.0023 |
| 1 – 5 years | 1.19 | 0.79, 1.79 | 0.4027 | 1.36 | 0.82, 2.27 | 0.2345 |
| 9 –12 years | 0.72 | 0.43, 1.21 | 0.2168 | 0.94 | 0.51, 1.72 | 0.8390 |
| 12 – 15 years | 0.39 | 0.17, 0.93 | 0.0334 | 0.61 | 0.24, 1.58 | 0.3070 |
| Number of malaria episodesi | 2.01 | 1.55, 2.60 | <0.0001 | 1.49 | 1.06, 2.12 | 0.0234 |
| Number of malaria episodes in the prior survey | 1.61 | 1.18, 2.21 | 0.0029 | 1.29 | 0.93, 1.81 | 0.1290 |
| Gametocyte positive in the prior survey | 2.26 | 1.06, 4.78 | 0.0339 | 1.84 | 0.88, 3.84 | 0.1052 |
| Asexual parasite positive in the prior survey | 1.22 | 0.81, 1.84 | 0.3442 | 0.91 | 0.59, 1.40 | 0.6725 |
| Year | 0.71 | 0.54, 0.93 | 0.0117 | 1.01 | 0.67, 1.54 | 0.9472 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 189.17, p<0.0001. P values in bold <0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Table 6. Logistic regression model predicting gametocyte positivity using data from Junju cohort only.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 20.76 | 9.02, 47.77 | <0.0001 | 18.52 | 6.78, 50.58 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | N/A | N/A | N/A | N/A | N/A | N/A |
| 0.5–1 years | 0.69 | 0.09, 5.15 | 0.7150 | 5.80 | 0.79, 42.69 | 0.0842 |
| 1–5 years | 1.23 | 0.58, 2.59 | 0.5858 | 1.92 | 0.81, 4.57 | 0.1380 |
| 9–12 years | 0.16 | 0.54, 4.15 | 0.0809 | 0.20 | 0.03, 1.50 | 0.3757 |
| 12–15 years | 1.49 | 0.02, 1.25 | 0.4431 | 2.02 | 0.43, 9.61 | 0.1184 |
| Number of malaria episodesi | 0.67 | 0.39, 1.16 | 0.1546 | 0.73 | 0.40, 1.33 | 0.3037 |
| Number of malaria episodes in the prior survey | 0.92 | 0.59, 1.43 | 0.7066 | 1.12 | 0.70, 1.79 | 0.6406 |
| Gametocyte positive in the prior survey | 4.64 | 0.62, 34.84 | 0.1357 | 1.85 | 0.26, 13.19 | 0.5399 |
| Asexual parasite positive in the prior survey | 1.93 | 0.88, 4.23 | 0.0987 | 0.68 | 0.28, 1.61 | 0.3767 |
| Year | 0.86 | 0.75, 0.98 | 0.0273 | 0.96 | 0.79, 1.15 | 0.6349 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 41.06, p<0.0001. P values in bold < 0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Table 7. Logistic regression model predicting gametocyte positivity using data from Ngerenya late cohort only.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 40.39 | 15.78, 103.38 | <0.0001 | 22.07 | 5.70, 85.41 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | N/A | N/A | N/A | N/A | N/A | N/A |
| 0.5–1 years | N/A | N/A | N/A | N/A | N/A | N/A |
| 1–5 years | 2.56 | 0.97, 6.77 | 0.0584 | 2.69 | 0.89, 8.12 | 0.0787 |
| 9–12 years | N/A | N/A | N/A | N/A | N/A | N/A |
| 12–15 years | N/A | N/A | N/A | N/A | N/A | N/A |
| Number of malaria episodesi | 3.25 | 2.21, 4.78 | <0.0001 | 1.30 | 0.60, 2.81 | 0.5018 |
| Number of malaria episodes in the prior survey | 0.65 | 0.13, 3.17 | 0.5963 | 0.19 | 0.02, 1.63 | 0.1308 |
| Gametocyte positive in the prior survey | 18.01 | 4.35, 74.56 | <0.0001 | 9.72 | 0.75, 125.70 | 0.0816 |
| Asexual parasite positive in the prior survey | 3.48 | 1.07, 11.34 | 0.0384 | 0.25 | 0.04, 1.71 | 0.1581 |
| Year | 0.59 | 0.49, 0.71 | <0.0001 | 0.74 | 0.58, 0.94 | 0.0129 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 522.09, p<0.0001 P values in bold < 0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Table 8. Logistic regression model predicting gametocyte positivity with interaction analysis.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 6.70 | 5.38, 8.34 | <0.0001 | 3.25 | 2.09, 5.05 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | 0.53 | 0.22, 1.31 | 0.1683 | 1.60 | 0.38, 6.84 | 0.5226 |
| 0.5–1 years | 1.21 | 0.71, 2.08 | 0.4842 | 2.19 | 1.20, 3.99 | 0.0109 |
| 1–5 years | 1.44 | 1.12, 1.85 | 0.0041 | 1.71 | 1.26, 2.32 | 0.0005 |
| 9–12 years | 0.62 | 0.43, 0.90 | 0.0293 | 0.67 | 0.44, 1.02 | 0.0610 |
| 12–15 years | 0.55 | 0.32, 0.94 | 0.0118 | 0.72 | 0.40, 1.31 | 0.2783 |
| Cohort | ||||||
| Chonyi | 1.00 | . | . | 1.00 | . | . |
| Junju | 0.17 | 0.12, 0.25 | <0.0001 | 0.11 | 0.05, 0.27 | <0.0001 |
| Ngerenya early | 0.98 | 0.77, 1.25 | 0.8991 | 1.02 | 0.67, 1.56 | 0.9149 |
| Ngerenya late | 0.08 | 0.05, 0.13 | <0.0001 | 0.07 | 0.04, 0.15 | <0.0001 |
| Number of malaria episodesi | 1.27 | 1.17, 1.38 | <0.0001 | 1.54 | 1.11, 2.12 | 0.0088 |
| Number of malaria episodes in the prior survey | 1.04 | 0.89, 1.21 | 0.6268 | 0.94 | 0.76, 1.18 | 0.6069 |
| Gametocyte positive in the prior survey | 4.58 | 2.95, 7.13 | <0.0001 | 1.91 | 1.20, 3.05 | 0.0067 |
| Asexual parasite positive in the prior survey | 2.16 | 1.68, 2.80 | <0.0001 | 0.81 | 0.61, 1.07 | 0.1364 |
| Asexual parasite positive: Chonyi | 1.00 | . | . | 1.00 | . | . |
| Asexual parasite positive: Junju | 7.02 | 2.84, 17.37 | <0.0001 | 5.12 | 1.99, 13.22 | 0.0007 |
| Asexual parasite positive: Ngerenya early | 1.07 | 0.66, 1.73 | 0.3557 | 0.97 | 0.54, 1.72 | 0.9055 |
| Asexual parasite positive: Ngerenya late | 13.66 | 5.00, 37.31 | <0.0001 | 9.62 | 2.56, 36.16 | 0.0008 |
| Chonyi: Number of malaria episodes | 1.00 | . | . | 1.00 | . | . |
| Junju: Number of malaria episodes | 0.33 | 0.18, 0.61 | 0.0004 | 0.49 | 0.25, 0.93 | 0.0297 |
| Ngerenya early: Number of malaria episodes | 0.92 | 0.67, 1.26 | 0.6055 | 1.11 | 0.76, 1.62 | 0.5965 |
| Ngerenya late: Number of malaria episodes | 1.62 | 1.02, 2.57 | 0.0425 | 1.04 | 0.46, 2.34 | 0.9233 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 22.12, p<0.0001, p values in bold are statistically significant at significance level 0.05.
Owing to differential associations observed in the cohorts between malaria episodes and gametocyte positivity and the difference in follow-up period, particularly for Junju and Chonyi, we divided the dataset into two time-periods, before 2006 and after 2006. This marked the periods before and after the introduction of ACTs. There was a marked decline in gametocyte prevalence in the period after the introduction of ACTs (Figure 10), dropping from approximately 4% to 0.5%. We adjusted for malaria episodes occurring within 28 days of the cross-sectional survey (Table 9 and Table 10). We found that before 2006, the number of malaria episodes were associated with an increased risk of gametocyte positivity (OR 1.38, 95% CI 1.15-1.66, p = 0.0006) while malaria episodes occurring within 28 days of a cross-sectional survey were associated with an approximately threefold increased risk of gametocyte positivity (95% CI 1.85, 4.53, p <0.0001). On the other hand, after 2006 the number of malaria episodes a participant had and malaria episodes occurring within 28 days of a cross-sectional survey ceased to be predictors of gametocyte positivity (OR 0.68, 95% CI 0.39, 1.20, p = 0.1809 and OR 1.46, 95% CI 0.21-10.01, p = 0.7026, respectively).
Enlarge this image.Figure 10. Prevalence of gametocytaemia and asexual parasitaemia before and after artemisinin combination therapy (ACT) introduction in all cohorts.
Bar plot showing the proportion of study participants positive for gametocytes or asexual parasites before and after introduction of ACTs. Prevalence of gametocytaemia was 4% before ACTs and 0.5% after ACTs, while the prevalence of asexual parasitaemia was 27% and 11% respectively.
Table 9. Logistic regression model predicting gametocyte positivity in observations recorded before 2006.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 3.93 | 3.12, 4.95 | <0.0001 | 3.49 | 2.56, 4.78 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | 0.37 | 0.15, 0.91 | 0.0310 | 1.63 | 0.39, 6.84 | 0.5038 |
| 0.5–1 years | 1.06 | 0.60, 1.87 | 08350 | 1.95 | 1.05, 3.62 | 0.0335 |
| 1–5 years | 1.31 | 1.00, 1.70 | 0.0488 | 1.63 | 1.19, 2.25 | 0.0027 |
| 9–12 years | 0.72 | 0.50, 1.06 | 0.0937 | 0.75 | 0.48, 1.15 | 0.1883 |
| 12–15 years | 0.50 | 0.26, 0.96 | 0.0359 | 0.58 | 0.28, 1.20 | 0.1409 |
| Cohort | ||||||
| Chonyi | 1.00 | . | . | 1.00 | . | . |
| Ngerenya early | 0.98 | 0.77, 1.25 | 0.8991 | 1.06 | 0.80, 1.42 | 0.6698 |
| Ngerenya late | 0.20 | 0.12, 0.32 | <0.0001 | 0.31 | 0.19, 0.53 | <0.0001 |
| Number of malaria episodesi | 1.92 | 1.67, 2.20 | <0.0001 | 1.38 | 1.15, 1.66 | 0.0006 |
| Number of malaria episodes in the prior survey | 1.18 | 0.95, 1.47 | 0.1353 | 0.88 | 0.69, 1.12 | 0.3029 |
| Gametocyte positive in the prior survey | 2.76 | 1.75, 4.34 | <0.0001 | 2.01 | 1.23, 3.29 | <0.0001 |
| Asexual parasite positive in the prior survey | 1.40 | 1.07, 1.83 | 0.0146 | 0.86 | 0.63, 1.16 | 0.0132 |
| Malaria episode within 28 days of survey | ||||||
| No | 1.00 | . | . | 1.00 | . | . |
| Yes | 5.60 | 4.03, 7.79 | <0.0001 | 2.89 | 1.85, 4.53 | <0.0001 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 19.73 p<0.0001. P values in bold < 0.05.
Table 10. Logistic regression model predicting gametocyte positivity in observations recorded after 2006.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 38.60 | 16.77, 88.81 | <0.0001 | 23.73 | 7.45, 75.53 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | N/A | N/A | N/A | N/A | N/A | N/A |
| 0.5–1 years | 0.74 | 0.10, 5.56 | 0.7689 | 6.31 | 0.89, 44.60 | 0.0650 |
| 1–5 years | 1.37 | 0.66, 2.89 | 0.3944 | 2.17 | 0.91, 5.20 | 0.0807 |
| 9–12 years | 0.12 | 0.02, 0.94 | 0.0435 | 0.18 | 0.02, 1.44 | 0.1060 |
| 12–15 years | 1.18 | 0.42, 3.26 | 0.7545 | 1.65 | 0.55, 4.93 | 0.3738 |
| Cohort | ||||||
| Junju | 1.00 | . | . | 1.00 | . | . |
| Ngerenya late | 0.04 | 0.01, 0.28 | 0.0013 | 0.16 | 0.02, 1.70 | 0.1293 |
| Number of malaria episodesi | 0.91 | 0.59, 1.39 | 0.6493 | 0.68 | 0.39, 1.20 | 0.1809 |
| Number of malaria episodes in the prior survey | 1.16 | 0.83, 1.62 | 0.3815 | 1.09 | 0.71, 1.67 | 0.6970 |
| Gametocyte positive in the prior survey | 7.58 | 1.01, 56.90 | 0.0489 | 1.86 | 0.27, 13.02 | 0.5321 |
| Asexual parasite positive in the prior survey | 3.44 | 1.58, 7.49 | 0.0019 | 0.65 | 0.27, 1.54 | 0.3249 |
| Malaria episode within 28 days of survey | ||||||
| No | 1.00 | . | . | 1.00 | . | . |
| Yes | 1.01 | 0.14, 7.43 | 0.994 | 1.46 | 0.21, 10.01 | 0.7026 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 41.11, p<0.0001. P values in bold < 0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Additionally, we looked at how gametocyte positivity in individuals who tested positive for asexual parasites varied with age, cohort, malaria episodes and parasitaemia (Supplementary Tables 4 and 528). In this analysis, we found that being under 5 years of age and being gametocyte-positive in the prior survey were associated with an increased odds of gametocyte positivity, while residing in Junju was associated with a decreased odds of gametocyte positivity, consistent with the analysis in Table 2.
We also tested for associations between the genetic factors for sickle cell, α-thalassaemia, and blood group on gametocyte positivity in a subset of individuals for whom genotype data was available (Supplementary Table 628). Heterozygosity (OR 0.92, 95% CI 0.66-1.27, p = 0.6170) and homozygosity (OR 0.70, 95% CI 0.43-1.15, p = 0.1561) for α-thalassaemia did not appear associated with gametocyte positivity. This was also true for sickle cell trait (OR 1.23, 95% CI 0.82-1.85, p = 0. 3082, Table 11). There were only nine children with sickle cell disease, hence we do not present an odds ratio for these children. We found that relative to AB and A blood groups, B and O blood groups were not associated with gametocyte carriage in this cohort (OR 1.34, 95% CI 0.64-2.79, p = 0.4419 and OR 1.03, 95% CI 0.57-1.86, p = 0.9294, respectively) (Table 12).
Table 11. Logistic regression model predicting gametocyte positivity including sickle cell and α-thalassaemia genotype data.
| Covariate | Univariable analysis | Multivariable analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 8.24 | 6.34, 10.71 | <0.0001 | 5.94 | 4.15, 8.48 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | 0.56 | 0.18, 1.79 | 0.3287 | 2.93 | 0.64, 13.46 | 0.1671 |
| 0.5–1 years | 1.15 | 0.56, 2.36 | 0.7091 | 1.60 | 0.68, 3.78 | 0.2813 |
| 1–5 years | 1.59 | 1.17, 2.17 | 0.0030 | 1.65 | 1.17, 2.33 | 0.0044 |
| 9–12 years | 0.56 | 0.34, 0.91 | 0.0195 | 0.47 | 0.28, 0.81 | 0.0059 |
| 12–15 years | 0.37 | 0.17, 0.81 | 0.0127 | 0.37 | 0.15, 0.90 | 0.0292 |
| Cohort | ||||||
| Chonyi | 1.00 | . | . | 1.00 | . | . |
| Junju | 0.19 | 0.12, 0.29 | <0.0001 | 0.21 | 0.12, 0.35 | <0.0001 |
| Ngerenya early | 1.12 | 0.80, 1.56 | 0.5084 | 1.15 | 0.78, 1.69 | 0.4870 |
| Ngerenya late | 0.09 | 0.05, 0.15 | <0.0001 | 0.19 | 0.10, 0.34 | <0.0001 |
| Number of malaria episodesi | 1.23 | 1.12, 1.35 | <0.0001 | 1.30 | 1.12, 1.50 | 0.0004 |
| Number of malaria episodes in the prior survey | 1.02 | 0.85, 1.22 | 0.8374 | 0.88 | 0.68, 1.15 | 0.3507 |
| Gametocyte positive in the prior survey | 5.65 | 3.36, 9.50 | <0.0001 | 1.99 | 1.11, 3.56 | 0.0205 |
| Asexual parasite positive in the prior survey | 2.22 | 1.62, 3.04 | <0.0001 | 0.85 | 0.59, 1.22 | 0.3853 |
| Sickle cell genotype | ||||||
| Normal | 1.00 | . | . | 1.00 | . | . |
| Heterozygous | 0.99 | 0.66, 1.48 | 0.9586 | 1.23 | 0.82, 1.85 | 0.3082 |
| Homozygous | N/A | N/A | N/A | N/A | N/A | N/A |
| α-thalassaemia genotype | ||||||
| Normal | 1.00 | . | . | 1.00 | . | . |
| Heterozygous | 0.90 | 0.66, 1.22 | 0.4856 | 0.92 | 0.66, 1.27 | 0.6170 |
| Homozygous | 0.62 | 0.40, 0.97 | 0.0351 | 0.70 | 0.43, 1.15 | 0.1561 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 56.98, p<0.0001. P values in bold < 0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Table 12. Logistic regression model predicting gametocyte positivity including sickle cell, α-thalassaemia and blood group genotype data.
| Covariate | Univariable Analysis | Multivariable Analysis | ||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| Asexual parasite positive | 12.14 | 7.65, 19.27 | <0.0001 | 10.97 | 6.11, 19.69 | <0.0001 |
| Age group | ||||||
| 5–9 years | 1.00 | . | . | 1.00 | . | . |
| 0–0.5 years | 1.06 | 0.14, 8.01 | 0.9584 | 3.18 | 0.26, 38.94 | 0.3657 |
| 0.5–1 years | 1.80 | 0.52, 6.29 | 0.3541 | 1.14 | 0.22, 6.01 | 0.8740 |
| 1–5 years | 3.11 | 1.69, 5.73 | 0.0003 | 1.85 | 0.91, 3.76 | 0.0895 |
| 9–12 years | 0.14 | 0.02, 1.11 | 0.0623 | 0.15 | 0.02, 1.14 | 0.0662 |
| 12–15 years | 1.07 | 0.35, 3.26 | 0.9106 | 1.07 | 0.34, 3.42 | 0.9029 |
| Cohort | ||||||
| Junju | 1.00 | . | . | 1.00 | . | . |
| Ngerenya early | 8.51 | 5.00, 14.49 | <0.0001 | 5.62 | 2.86, 11.04 | <0.0001 |
| Ngerenya late | 0.36 | 0.18, 0.75 | 0.0058 | 0.64 | 0.30, 1.34 | 0.2387 |
| Number of malaria episodesi | 1.22 | 1.06, 1.41 | 0.0072 | 1.09 | 0.86, 1.38 | 0.4660 |
| Number of malaria episodes in the prior survey | 1.06 | 0.81, 1.37 | 0.6787 | 0.85 | 0.55, 1.30 | 0.4494 |
| Gametocyte positive in the prior survey | 9.01 | 3.40, 23.88 | <0.0001 | 2.38 | 0.81, 6.98 | 0.1142 |
| Asexual parasite positive in the prior survey | 2.00 | 1.08, 3.69 | 0.0272 | 0.64 | 0.32, 1.30 | 0.2189 |
| Sickle cell genotype | ||||||
| Normal | 1.00 | . | . | 1.00 | . | . |
| Heterozygous | 0.86 | 0.42, 1.77 | 0.6827 | 1.07 | 0.51, 2.24 | 0.8560 |
| Homozygous | N/A | N/A | N/A | N/A | N/A | N/A |
| α-thalassaemia genotype | ||||||
| Normal | 1.00 | . | . | 1.00 | . | . |
| Heterozygous | 1.12 | 0.65, 1.93 | 0.6865 | 0.99 | 0.57, 1.73 | 0.9811 |
| Homozygous | 0.45 | 0.17, 1.22 | 0.1165 | 0.45 | 0.16, 1.26 | 0.1299 |
| ABO blood group | ||||||
| A and AB | 1.00 | . | . | 1.00 | . | . |
| B | 1.22 | 0.58, 2.57 | 0.5970 | 1.34 | 0.64, 2.79 | 0.4419 |
| O | 1.06 | 0.60, 1.88 | 0.8446 | 1.03 | 0.57, 1.86 | 0.9294 |
i the ‘number of malaria episodes’ is defined as the sum of the number of malaria episodes occurring in the period leading up to a cross-sectional survey. Robust standard error estimation; Wald test F statistic - 31.67, p<0.0001. P values in bold <0.05. N/A - sample size insufficient for estimate (i.e. n<5).
Discussion
The analysis aimed to describe gametocyte prevalence and distribution over time and varying transmission intensities, and to identify factors associated with gametocyte carriage in three cohorts of children in Kilifi maintained for 3, 12 and 19 years, respectively, in which individual follow-up ran to a maximum of 15 years of age. Identification of these factors could then possibly serve as predictive features that would allow for targeted application of transmission-reducing interventions11. The three cohorts were in sublocations within Kilifi County (i.e. Ngerenya, Chonyi and Junju) that represent a low, a mid to low, and a high transmission setting. For the purposes of this analysis, however, Ngerenya was subdivided into Ngerenya early (a period of moderate to high transmission) and Ngerenya late (a period of low transmission).
In the Ngerenya early, Chonyi, and Ngerenya late cohorts, a trend towards lower gametocyte and asexual parasite prevalence over time was observed, but there was no clear trend in Junju. Malaria transmission has been on the decline on the Kenyan coast since 1998, as evidenced by a decrease in parasite prevalence and paediatric malaria admission cases. A resurgence in malaria transmission has been described, however, following a nadir in 2009/201033,34. Heterogeneity of transmission with hotspots of malaria has been described35, and transmission has persisted in Junju, whilst in Ngerenya transmission has remained either non-existent or low in some parts. Previous literature has demonstrated reductions in the prevalence of gametocytaemia with increasing age in higher-transmission areas11,17,19,31,36,37, consistent with the pattern we here describe in the higher transmission cohorts. Similarly, results obtained in previous studies are confirmed by this analysis, that the likelihood of gametocytaemia increases in the presence of asymptomatic asexual parasitaemia6,17,31. Furthermore, it is expected that the prevalence of gametocytaemia would fall in the community as the prevalence of asymptomatic parasitaemia falls38. Moreover, prior episodes of clinical malaria are a well-documented source of gametocytaemia6,11. The findings in Junju are therefore unexpected: gametocyte prevalence was disproportionately lower compared with the prevalence of asexual parasitaemia (Figure 4), and malaria episodes occurring in the period leading up to a cross-sectional survey were modestly protective rather than a risk factor for gametocytaemia (Table 6). We speculate that a change in anti-malarial drug policy might have caused this variation in effect.
Based on national guidelines, treatment of malaria in Kenya was with CQ from the 1970s to 1999 before being replaced by SP that was used until late 2006 when it was replaced by ACTs33. Both CQ and SP have been associated with increased gametocyte carriage post-treatment14,36,39 and were in use in Ngerenya early and Chonyi for the treatment of malaria, possibly leading to a higher than expected gametocyte prevalence. On the other hand, in Junju, treatment for malaria during the period of follow-up included in this study was with ACTs, in particular the combination of artemether-lumefantrine, that has been described to reduce post-treatment gametocyte carriage4,6,40, which may also explain the much lower gametocyte prevalence in Junju, and furthermore also explain the lack of association between prior episodes of clinical malaria and subsequent gametocytaemia.
Antimalarials have been used in various ways to control malaria. Mass drug administration has been effective in clearing gametocytes and reducing subsequent transmission intensity41. Screen and treat has been proposed to avoid treating uninfected participants in mass drug administration, but has not been efficacious in field trials42. In this study, we demonstrate that providing ACTs to the children with acute febrile malaria was associated with a cohort-wide reduction in the prevalence of gametocytaemia, and further evidence linking this effect to ACTs is the expected association between prior episodes of acute febrile malaria and gametocyte carriage in cohorts prior to ACT use, but the absence of this association after ACTs were introduced.
We acknowledge that Junju monitoring data is only available post ACT introduction and therefore we cannot analyse parasite prevalence pre- and post-ACT introduction in this cohort. Additionally, while monitoring data for Ngerenya spans pre- and post-ACT introduction, parasite prevalence is infrequent in Ngerenya after 2006 and thus we do not have enough power to detect the changing association between drug regimen and gametocyte prevalence. However, as Junju and Chonyi are located close to each other (Figure 1) and have similar populations22, and malaria parasites are likely to be mixed over this geographical space43, it seems more likely that ACT use explains the changing epidemiological patterns rather than an ecological difference between the settings.
Gametocyte carriage depended more strongly on asexual parasite positivity at lower transmission intensities (Junju and Ngerenya late) than at higher transmission intensities (Ngerenya early and Chonyi). When gametocytes are seen in the absence of asexual parasitaemia by microscopy, it may be that asexual parasitaemia is present but below the threshold detectable microscopically44. We hypothesise that since immunity to malaria develops more slowly at lower transmission settings45, individuals will tend to have higher parasite densities that are more readily detected by microscopy and linked with an increased likelihood of gametocytaemia6,17,31.
There was an indication for certain individuals being at a greater risk of gametocyte carriage, as being gametocyte-positive in the previous year predisposed a subject to gametocyte carriage. This has been described in Senegal, where Grange et al. (2015) identified hotspots of gametocyte carriage and these were associated with active malaria transmission11. However, gametocytaemia appeared to follow a binomial distribution in contrast to the negative binomial distribution seen for asexual parasitaemia, suggestive that gametocyte carriage is more evenly distributed in the population compared with asexual parasite carriage, where certain individuals are at considerably greater susceptibility to [re-]infection due to host-related factors25 (Figure 8 and Figure 9).
Genetic polymorphisms known to be protective against severe malaria, such as B and O blood groups, sickle cell trait and α-thalassemia, were associated with increased gametocyte carriage in previous studies11,12,25,46,47. However, in our dataset we were not able to replicate this finding.
A limitation of this study is that we did not study sub-microscopic infection. Sub-microscopic gametocyte carriage is common in malaria endemic areas and has been shown to contribute to malaria transmission48,49. We have previously shown that 45–75% of all mosquito infections result from parasite levels below the detection threshold of microscopy49. Interestingly, this previous study by Goncalves et al.49 and other studies16,18 showed that gametocyte prevalence was highest in 5–15-year-olds in comparison to their younger and older counterparts. This may reflect the lower parasite densities in these children owing to a more developed immune system that would be undetectable microscopically. This also indicates that transmission-reducing interventions may need to target more than just <5-year-olds to be effective. Therefore, employing the use of high-quality research-grade microscopy or quantitative PCR would be most beneficial in epidemiological studies aimed at identifying the infectious reservoir50.
Another limitation of our study is that the microscopy protocol used in these cohorts is primarily for assessing asexual parasite carriage and therefore the number of blood films examined varied depending on the asexual parasite density. More fields were examined for asexual parasite negative blood films, which increased the chances of detecting gametocytes in these asexual parasite negative blood films. We observed, however, that more gametocytes were detected in blood films from individuals who were asexual parasite-positive, and furthermore that more gametocytes were detected at higher asexual parasite densities. Furthermore, any bias resulting from missing low-density gametocytaemia would be consistent across age, time, and other factors since the protocol has not been varied during the period of study, and we adjusted for asexual parasites in the multivariable models, and noted associations with gametocytaemia as reported in previous studies. We therefore do not believe this bias was responsible for the associations between covariates and risk of gametocytaemia seen in our study.
Conclusion
In summary, our analyses have confirmed the importance of age, transmission intensity and previous malaria episodes as predictors of microscopically detectable gametocyte carriage. The analyses including three different cohorts over 19 years of follow-up and varying transmission intensities allow a clear demonstration of the independence and interactions of these factors. These could serve as potential indicators of populations that contribute disproportionately to the infectious reservoir and where malaria transmission-blocking interventions could be prioritised. However, to improve characterisation of the infectious reservoir, epidemiological studies combining molecular tools for parasite detection together with assays to measure infectiousness to mosquitoes across all age groups and varied transmission settings will be required.
Our data also suggest that the introduction of ACTs, particularly the highly effective artemether/lumefantrine, may have had a substantial effect on gametocyte carriage among a cohort of children followed up actively both weekly and at cross-sectional surveys, disrupting the link between malaria episodes and subsequent gametocyte carriage. Based on the impact on gametocyte carriage, we infer a role for ACTs targeted used in febrile malaria cases as potentially impacting malaria transmission.
Data availability
Underlying data
Harvard Dataverse: Kilifi Malaria Longitudinal Cohort cross-sectional survey and weekly follow-up surveillance data for the estimation of parasite prevalence and factors associated with gametocyte carriage. https://doi.org/10.7910/DVN/18QB3V28.
This project contains the following underlying data:
The above raw data that support the findings of this manuscript are under restricted access and available through the KEMRI-Wellcome Trust Research Programme Data Governance Committee if the use of the data is complaint with the consent provided by the participants. Details of the criteria can be found in the KEMRI-Wellcome data sharing guidelines (https://kemri-wellcome.org/about-us/#ChildVerticalTab_15). Requests for the data can be made to the Data Governance Committee ([email protected]) through the corresponding author.
Extended data
Harvard Dataverse: Kilifi Malaria Longitudinal Cohort cross-sectional survey and weekly follow-up surveillance data for the estimation of parasite prevalence and factors associated with gametocyte carriage. https://doi.org/10.7910/DVN/18QB3V28.
This project contains the following extended data:
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Grant information
The cohorts were supported by a grant from the Wellcome Trust (092741). MKM is funded via an IDeAL PhD Studentship awarded by the KEMRI-Wellcome Trust Research Programme through funding from the DELTAS Africa programme via the Wellcome Trust (107769) and TNW was supported by grants from the Wellcome Trust (091758 and 203077).
The funders had no role in the design, collection, analysis and interpretation of results, manuscript writing or submission for publication.
Acknowledgements
We thank the study participants, microscopy and lab personnel and field workers. This work was supported through the DELTAS Africa Initiative [DEL-15-003]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust and the UK government. The views expressed in this publication are those of the author(s) and not necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK government. This paper is published with permission from the Director of KEMRI.
1. Snow RW, Sartorius B, Kyalo D, et al.: The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900. Nature. 2017; 550(7677): 515–8.
2. Bhattarai A, Ali AS, Kachur SP, et al.: Impact of artemisinin-based combination therapy and insecticide-treated nets on malaria burden in Zanzibar. PLoS Med. 2007; 4(11): e309.
3. Norris LC, Norris DE: Efficacy of long-lasting insecticidal nets in use in Macha, Zambia, against the local Anopheles arabiensis population. Malar J. 2011; 10: 254.
4. Barnes KI, Chanda P, Ab Barnabas G: Impact of the large-scale deployment of artemether/lumefantrine on the malaria disease burden in Africa: case studies of South Africa, Zambia and Ethiopia. Malar J. 2009; 8 Suppl 1: S8.
5. Eichner M, Diebner HH, Molineaux L, et al.: Genesis, sequestration and survival of Plasmodium falciparum gametocytes: parameter estimates from fitting a model to malariatherapy data. Trans R Soc Trop Med Hyg. 2001; 95(5): 497–501.
6. Nacher M, Singhasivanon P, Silachamroon U, et al.: Decreased hemoglobin concentrations, hyperparasitemia, and severe malaria are associated with increased Plasmodium falciparum gametocyte carriage. J Parasitol. 2002; 88(1): 97–101.
7. Buckling A, Read AF: The effect of partial host immunity on the transmission of malaria parasites. Proc Biol Sci. 2001; 268(1483): 2325–30.
8. Regev-Rudzki N, Wilson DW, Carvalho TG, et al.: Cell-cell communication between malaria-infected red blood cells via exosome-like vesicles. Cell. 2013; 153(5): 1120–33.
9. Mantel PY, Hoang AN, Goldowitz I, et al.: Malaria-infected erythrocyte-derived microvesicles mediate cellular communication within the parasite population and with the host immune system. Cell Host Microbe. 2013; 13(5): 521–34.
10. Williams TN, Mwangi TW, Wambua S, et al.: Sickle cell trait and the risk of Plasmodium falciparum malaria and other childhood diseases. J Infect Dis. 2005; 192(1): 178–86.
11. Grange L, Loucoubar C, Telle O, et al.: Risk factors for Plasmodium falciparum gametocyte positivity in a longitudinal cohort. PLoS One. 2015; 10(4): e0123102.
12. Lawaly YR, Sakuntabhai A, Marrama L, et al.: Heritability of the human infectious reservoir of malaria parasites. PLoS One. 2010; 5(6): e11358.
13. Barnes KI, White NJ: Population biology and antimalarial resistance: The transmission of antimalarial drug resistance in Plasmodium falciparum. Acta Trop. 2005; 94(3): 230–40.
14. Tjitra E, Suprianto S, Anstey NM: Higher gametocyte prevalence following failure of treatment of Plasmodium falciparum malaria with sulfadoxine-pyrimethamine and the combination of chloroquine plus sulfadoxine-pyrimethamine: implications for progression of anti-folate resistance. Trans R Soc Trop Med Hyg. 2002; 96(4): 434–7.
15. World Health Organization: Treatment of Severe Malaria. Guidel Treat Malar. 2015; 71–88.
16. Zhou Z, Mitchell RM, Kariuki S, et al.: Assessment of submicroscopic infections and gametocyte carriage of Plasmodium falciparum during peak malaria transmission season in a community-based cross-sectional survey in western Kenya, 2012. Malar J. 2016; 15(1): 421.
17. Koepfli C, Robinson LJ, Rarau P, et al.: Blood-Stage Parasitaemia and Age Determine Plasmodium falciparum and P. vivax Gametocytaemia in Papua New Guinea. PLoS One. 2015; 10(5): e0126747.
18. Coalson JE, Walldorf JA, Cohee LM, et al.: High prevalence of Plasmodium falciparum gametocyte infections in school-age children using molecular detection: patterns and predictors of risk from a cross-sectional study in southern Malawi. Malar J. 2016; 15(1): 527.
19. Akim NI, Drakeley C, Kingo T, et al.: Dynamics of P. falciparum gametocytemia in symptomatic patients in an area of intense perennial transmission in Tanzania. Am J Trop Med Hyg. 2000; 63(3–4): 199–203.
20. Andagalu B, Mativo J, Kamau E, et al.: Longitudinal study on Plasmodium falciparum gametocyte carriage following artemether-lumefantrine administration in a cohort of children aged 12-47 months living in Western Kenya, a high transmission area. Malar J. 2014; 13: 265.
21. Mwangi TW, Ross A, Snow RW, et al.: Case definitions of clinical malaria under different transmission conditions in Kilifi District, Kenya. J Infect Dis. 2005; 191(11): 1932–9.
22. Mwangi TW: Clinical epidemiology of malaria under differing levels of transmission. PhD thesis, The Open University; 2003.
23. Bejon P, Mwacharo J, Kai O, et al.: A phase 2b randomised trial of the candidate malaria vaccines FP9 ME-TRAP and MVA ME-TRAP among children in Kenya. PLoS Clin Trials. 2006; 1(6): e29.
24. Mwangangi JM, Mbogo CM, Muturi EJ, et al.: Spatial distribution and habitat characterisation of Anopheles larvae along the Kenyan coast. J Vector Borne Dis. 2007; 44(1): 44–51.
25. Ndungu FM, Marsh K, Fegan G, et al.: Identifying children with excess malaria episodes after adjusting for variation in exposure: identification from a longitudinal study using statistical count models. BMC Med. 2015; 13: 183.
26. O’Meara WP, Mwangi TW, Williams TN, et al.: Relationship between exposure, clinical malaria, and age in an area of changing transmission intensity. Am J Trop Med Hyg. 2008; 79(2): 185–91.
27. Bejon P, Mwacharo J, Kai O, et al.: The induction and persistence of T cell IFN-gamma responses after vaccination or natural exposure is suppressed by Plasmodium falciparum. J Immunol. 2007; 179(6): 4193–201.
28. Muthui MK, Mogeni P, Mwai K, et al.: Kilifi Malaria Longitudinal Cohort cross-sectional survey and weekly-follow-up surveillance data for the estimation of parasite prevalence and factors associated with gametocyte carriage. Harvard Dataverse; V2. http://www.doi.org/10.7910/DVN/18QB3V
29. Chong SS, Boehm CD, Higgs DR, et al.: Single-tube multiplex-PCR screen for common deletional determinants of alpha-thalassemia. Blood. 2000; 95(1): 360–2.
30. Waterfall CM, Cobb BD: Single tube genotyping of sickle cell anaemia using PCR-based SNP analysis. Nucleic Acids Res. 2001; 29(23): E119.
31. Bousema JT, Gouagna LC, Drakeley CJ, et al.: Plasmodium falciparum gametocyte carriage in asymptomatic children in western Kenya. Malar J. 2004; 3: 18.
32. RStudio Team: RStudio: Integrated Development for R. Boston, MA: RStudio Inc.; 2016.
33. Snow RW, Kibuchi E, Karuri SW, et al.: Changing Malaria Prevalence on the Kenyan Coast since 1974: Climate, Drugs and Vector Control. Keating JA, editor. PLoS One. 2015; 10(6): e0128792.
34. Mogeni P, Williams TN, Fegan G, et al.: Age, Spatial, and Temporal Variations in Hospital Admissions with Malaria in Kilifi County, Kenya: A 25-Year Longitudinal Observational Study. PLoS Med. 2016; 13(6): e1002047.
35. Bejon P, Williams TN, Nyundo C, et al.: A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots. eLife. 2014; 3: e02130.
36. Stepniewska K, Price RN, Sutherland CJ, et al.: Plasmodium falciparum gametocyte dynamics in areas of different malaria endemicity. Malar J. 2008; 7: 249.
37. Boudin C, Lyannaz J, Bosseno MF, et al.: Epidemiology of Plasmodium falciparum in a rice field and a savanna area in Burkina Faso: seasonal fluctuations of gametocytaemia and malarial infectivity. Ann Trop Med Parasitol. 1991; 85(4): 377–85.
38. Poirot E, Skarbinski J, Sinclair D, et al.: Mass drug administration for malaria. Cochrane Database Syst Rev. 2013; (12): CD008846.
39. Sokhina C, Trape JF, Robert V: Gametocytaemia in Senegalese children with uncomplicated falciparum malaria treated with chloroquine, amodiaquine or sulfadoxine + pyrimethamine. Parasite. 2001; 8(3): 243–50.
40. WWARN Gametocyte Study Group: Gametocyte carriage in uncomplicated Plasmodium falciparum malaria following treatment with artemisinin combination therapy: a systematic review and meta-analysis of individual patient data. BMC Med. 2016; 14: 79.
41. Newby G, Hwang J, Koita K, et al.: Review of mass drug administration for malaria and its operational challenges. Am J Trop Med Hyg. 2015; 93(1): 125–34.
42. von Seidlein L: The failure of screening and treating as a malaria elimination strategy. PLoS Med. 2014; 11(1): e1001595.
43. Omedo I, Mogeni P, Bousema T, et al.: Micro-epidemiological structuring of Plasmodium falciparum parasite populations in regions with varying transmission intensities in Africa [version 2; peer review: 4 approved]. Wellcome Open Res. 2017; 2: 10.
44. Wu L, van den Hoogen LL, Slater H, et al.: Comparison of diagnostics for the detection of asymptomatic Plasmodium falciparum infections to inform control and elimination strategies. Nature. 2015; 528(7580): S86–93.
45. Langhorne J, Ndungu FM, Sponaas AM, et al.: Immunity to malaria: more questions than answers. Nat Immunol. 2008; 9(7): 725–32.
46. Robert V, Tchuinkam T, Mulder B, et al.: Effect of the Sickle Cell Trait Status of Gametocyte Carriers of Plasmodium falciparum on Infectivity to Anophelines. Am J Trop Med Hyg. 1996; 54(2): 111–3.
47. Gouagna LC, Bancone G, Yao F, et al.: Genetic variation in human HBB is associated with Plasmodium falciparum transmission. Nat Genet. 2010; 42(4): 328–31.
48. Ouédraogo AL, Bousema T, Schneider P, et al.: Substantial contribution of submicroscopical Plasmodium falciparum gametocyte carriage to the infectious reservoir in an area of seasonal transmission. PLoS One. 2009; 4(12): e8410.
49. Gonçalves BP, Kapulu MC, Sawa P, et al.: Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity. Nat Commun. 2017; 8(1): 1133.
50. Hofmann NE, Gruenberg M, Nate E, et al.: Assessment of ultra-sensitive malaria diagnosis versus standard molecular diagnostics for malaria elimination: an in-depth molecular community cross-sectional study. Lancet Infect Dis. 2018; 18(10): 1108–1116.
Michelle K. Muthui 1, Polycarp Mogeni 1,2, [...] Kennedy Mwai 1,3, Christopher Nyundo 1, Alex Macharia1, Thomas N. Williams 1,4, George Nyangweso1, Juliana Wambua1, Daniel Mwanga1, Kevin Marsh 1,5, Philip Bejon 1,5, Melissa C. Kapulu 1,5
1 Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, 230-80108, Kenya
2 African Health Research Institute, Durban, Congella, 4013, Private bag X7, South Africa
3 Epidemiology and Biostatistics Division, School of Public Health, University of the Witwatersrand, Johannesburg, Parktown, 2193, 27 St Andrews Road, South Africa
4 Department of Medicine, Imperial College London, St Mary's Campus, London, W21NY, UK
5 Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7FZ, UK
Michelle K. Muthui
Roles: Formal Analysis, Visualization, Writing – Original Draft Preparation
Polycarp Mogeni
Roles: Formal Analysis, Methodology, Writing – Review & Editing
Kennedy Mwai
Roles: Formal Analysis, Writing – Review & Editing
Christopher Nyundo
Roles: Visualization, Writing – Review & Editing
Alex Macharia
Roles: Investigation, Writing – Review & Editing
Thomas N. Williams
Roles: Funding Acquisition, Investigation, Writing – Review & Editing
George Nyangweso
Roles: Data Curation, Writing – Review & Editing
Juliana Wambua
Roles: Data Curation, Writing – Review & Editing
Daniel Mwanga
Roles: Data Curation, Writing – Review & Editing
Kevin Marsh
Roles: Funding Acquisition, Methodology, Project Administration, Writing – Review & Editing
Philip Bejon
Roles: Formal Analysis, Methodology, Supervision, Writing – Review & Editing
Melissa C. Kapulu
Roles: Conceptualization, Supervision, Writing – Review & Editing
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Mogeni, Polycarp
; Kennedy Mwai
; Nyundo, Christopher
; Macharia, Alex; Williams, Thomas N
; Nyangweso, George; Wambua, Juliana; Mwanga, Daniel; Marsh, Kevin
; Bejon, Philip
; Kapulu, Melissa C