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01.12.2016 | Research article | Ausgabe 1/2016 Open Access

BMC Medicine 1/2016

Gametocyte carriage in uncomplicated Plasmodium falciparum malaria following treatment with artemisinin combination therapy: a systematic review and meta-analysis of individual patient data

Zeitschrift:
BMC Medicine > Ausgabe 1/2016
Autor:
WWARN Gametocyte Study Group
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12916-016-0621-7) contains supplementary material, which is available to authorized users.

Background

Malaria remains a leading cause of morbidity and mortality in endemic countries, with an estimated 584,000 deaths and 198 million clinical cases of malaria globally in 2013 [ 1]. Considerable progress has been made in the last decade in reducing the burden of malaria by wide-scale deployment of insecticide-treated nets and efficacious artemisinin combination therapy (ACT) as first-line antimalarial treatment [ 2]. To maintain these gains and further move towards malaria elimination, a specific focus on malaria reducing interventions is needed [ 3]. The transmission of malaria to mosquitoes depends on mature sexual stage parasites, gametocytes, in the human peripheral blood. Plasmodium falciparum gametocytaemia has been associated with asexual parasite densities, the duration of malaria symptoms, anaemia and immunity [ 4, 5]. A large fraction of gametocyte-positive individuals are asymptomatic and the contribution of this asymptomatic reservoir to onward malaria transmission is considerable in many endemic settings [ 6]. As a consequence, efforts to reduce malaria transmission by antimalarial treatment depend for a large extent on the proportion of malaria-infected individuals that receive treatment [ 7]. Upon initiation of treatment, gametocytes may persist for several weeks after the clearance of asexual parasites with their longevity and infectivity depending on the treatment dispensed [ 8, 9], dosing [ 10] and host immunity [ 5].
ACT is now recommended universally for the treatment of uncomplicated falciparum malaria. Artemisinins are highly effective against the pathogenic asexual parasite stages [ 11] and immature gametocytes [ 12, 13], resulting in a substantial reduction of post-treatment malaria transmission compared to non-artemisinin drugs [ 9, 14, 15]. The wide-scale deployment of ACTs has been associated with substantial reductions in disease burden across a range of endemic settings [ 16, 17]. Nevertheless, the transmission reducing effects of ACT may be incomplete because of limited efficacy of artemisinins against mature gametocytes, permitting residual transmission in the first weeks after treatment [ 9, 15]. Moreover, differences in artemisinin dosing, timing and partner drugs affect their gametocytocidal properties [ 18, 19].
Because gametocytes are only detected in a fraction of patients by microscopy, individual trials are often insufficiently powered to compare gametocytocidal properties between ACTs or disentangle host and parasite factors that influence gametocyte dynamics. To address this, a pooled analysis of individual-level patient data was undertaken in patients before and after treatment with artemether-lumefantrine (AL), artesunate-amodiaquine (AS-AQ), artesunate-mefloquine (AS-MQ), and dihydroartemisinin-piperaquine (DP).

Methods

Data pooling

A search was conducted in PubMed in September 2014 to identify all antimalarial clinical trials published between 1990 and 2014, in which gametocytes were recorded using the search strategy described in the legend of Additional file 1: Table S1. Those who had contributed studies previously to the WorldWide Antimalarial Resistance Network (WWARN) data repository were also invited to participate and asked whether they were aware of any unpublished or ongoing clinical trials involving ACTs, and these additional unpublished studies were also requested. Investigators were invited to participate in this pooled analysis if their studies included (1) uncomplicated P. falciparum malaria (alone or mixed infection with another species); (2) asexual parasite quantification at enrolment; (3) gametocyte quantification or prevalence at enrolment; (4) well described methodology for quantifying asexual parasites and gametocytes; and (5) haemoglobin (or haematocrit) estimation at enrolment.
Individual study protocols were available for all trials included, either from the publication or as a metafile submitted with the raw data. Individual patient data from eligible studies were shared, collated and standardised using a previously described methodology [ 10, 20]. Study reports generated from the formatted datasets were sent back to investigators for validation or clarification. All parasite data were based on microscopic observations.

Statistical analysis

Statistical analyses were carried out using STATA (Version 13.1) according to an a priori Statistical Analysis Plan [ 20]. Briefly, we determined: (1) prevalence of gametocytaemia at enrolment (regardless of subsequent treatment regimen); (2) risk of gametocytaemia in patients presenting with no gametocytaemia on enrolment; and (3) time to clearance of gametocytaemia in patients presenting with gametocytaemia. For the comparison of ACT regimens, the analysis was restricted to individuals with no recurrent asexual parasitaemia recorded during follow-up. Multivariable models with random effects were fitted to adjust for study and site heterogeneity: logistic for outcome (1) and Cox regression (with shared frailty) for outcomes (2) and (3). The effect of the following baseline covariates was examined: age, sex, log asexual parasite density, hyperparasitaemia (asexual parasitaemia > 200,000 parasites per μL), haemoglobin/haematocrit, anaemia (haemoglobin concentration < 10 g/dL), presence of/history of fever, nutritional status (based on weight-for-age z-scores in children < 5 years of age), treatment dose of artemisinin derivative, geographic region and malaria transmission intensity [ 21]. Indicators of parasite clearance time included asexual parasite prevalence and log asexual parasite density on days 1, 2, 3 and the area under the curve of asexual parasite density during days 0–3. Fractional polynomials [ 22] were used to define the nonlinear relationship between age, haemoglobin concentration and asexual parasite density and the risk of gametocytaemia; to maintain stability, these models were fitted to data from patients ≤ 70 years of age, with haemoglobin between 5 and 18 g/dL and with 500–200,000 asexual parasites per μL. Target dosing for the artemisinin components of the ACTs was defined according to WHO guidelines: ≥ 8.4 mg/kg for AL and ≥ 6 mg/kg for AS-AQ, AS-MQ and DP [ 23].
Gametocyte carriage at any time after treatment in patients with no recurrent parasitaemia, patients with recrudescent infections and patients with reinfections were compared using multilevel logistic regression models with random effects for study site and subject.
Methods to detect gametocytes by microscopy differed between trials. The sensitivity of microscopy methods was included in the analyses, by classifying studies into one of four categories, as follows: (1) studies in which slides were specifically read for gametocytes, reviewing at least 100 microscopic high power fields or against ≥ 1000 white blood cells (WBC) (4 studies); (2) microscopists specifically instructed to record gametocytes but slides were primarily read for asexual parasites; ≥ 100 microscopic high power fields per ≥ 1000 WBC were read (26 studies); (3) microscopists were specifically instructed to record gametocytes; 50–99 microscopic high power fields per 500–999 WBC were read (33 studies); (4) microscopists were not specifically instructed to record gametocytes or the number of examined high power fields was < 50 or the number of WBC was < 500 (40 studies). For 18 studies, the information on the sensitivity of the microscopy was not available.
Risk of bias within studies was assessed based on (1) study design (randomization, sequence generation, blinding); (2) methodology for gametocyte detection; and (3) the number and proportion of patients with (a) missing outcomes and (b) missing baseline covariates (age, weight, parasitaemia, temperature, haemoglobin/haematocrit). For the final models, two sets of sensitivity analyses were performed. Firstly, a model was refitted with each study’s data excluded, one at a time, and a coefficient of variation around the parameter estimates calculated. This would identify any influential studies, that is, studies with unusual results (due to variations in methodology, patient population, or other reasons) that affect the overall pooled analysis findings. Secondly, for the outcome measure time to gametocytaemia, the impact of incomplete gametocyte carriage data was investigated by refitting the final multivariable model in a subset of patients with complete weekly data for 28 days.

Ethical approval

All data included in this analysis were obtained in accordance with the laws and ethical approvals applicable to the countries in which the studies were conducted, and were obtained with the knowledge and consent of the individual to which they relate. Data were fully anonymised either before or during the process of uploading to the WWARN repository. Ethical approval to conduct individual participant data pooled analyses was granted to WWARN by the Oxford Tropical Research Ethics Committee (OXTREC-48-09).

Results

Characteristics of included studies

In total, 169 published clinical trials were identified that recorded P. falciparum gametocytes at enrolment or during follow-up. Investigators of 117 clinical trials (59,458 patients) agreed to contribute their data. In addition, nine unpublished studies (1,803 patients) were shared, one of which was published subsequently. After exclusion of duplicate studies, studies in returning travellers, multiple infection episodes and participants with protocol violations, 48,840 study participants from 121 individual clinical trials were retained (Fig.  1; full list of studies in Additional file 1: Table S1).

Baseline characteristics

The majority of participants were from Africa (34,377; 70.4 %) or Asia (13,546; 27.7 %) with a minority coming from South America (917; 1.9 %) (Table  1). Most studies involved treatment with an ACT (68.3 % of all participants (33,356/48,840)) with AL being the most commonly used regimen (27.1 %; 13,217/48,840) (Table  2). AS-AQ was given to 17.4 % (8488/48,840) of participants; 50.4 % (4278/8488) of these received a fixed dose combination (FDC), others received a non-fixed dose combination (42.9 %; 3637/8488) or co-blistered AS and AQ (6.8 %; 573/8488). The analyses for AS-AQ were restricted to the FDC regimen (AS-AQ FDC). AS-MQ was administered to 10.6 % (5198/48,840) of participants, in most of the patients (88.1 %; 4580/5198) as a loose combination. The following proportions of patients received less than the recommended dose, AL: 8.3 % (1088/13,086); AS-AQ FDC: 0.1 % (2/4262); AS-MQ: 0.8 % (38/4769) DP: 23.6 % (1488/6315).
Table 1
Demographic and baseline characteristics
 
Africa
Asia
South America
 
n evaluated
n (%) or median (Range)
n evaluated
n (%) or median (Range)
n evaluated
n (%) or median (Range)
Age
           
  < 1 year
34361
2502 (7)
13545
60 (0)
915
0 (0)
 1–4 years
34361
20473 (60)
13545
1377 (10)
915
0 (0)
 5–11 years
34361
6775 (20)
13545
3601 (27)
915
111 (12)
  ≥ 12 years
34361
4611 (13)
13545
8507 (63)
915
804 (88)
Age (years)
34361
3.3 (0–86.7)
13545
15.0 (0–88.0)
915
23.0 (5.0 – 65.0)
Haemoglobin (g/dL)
24771
9.9 (5.0–19.7)
3139
11.1 (5.0–20)
603
12.2 (7.0–17.3)
Haematocrit (%)
5938
32.8 (15.0–49.8)
8076
36.0 (15.0–50.0)
604
37.0 (18.0 – 50.0)
Derived haemoglobin (g/dL)
26806
9.9 (3.6–19.7)
10937
11.6 (3.6–20)
606
12.2 (7.0–17.3)
Anaemia
26806
13313 (50)
10937
3882 (26)
606
48 (8)
Temperature (°C)
33776
37.9 (34.0–41.5)
10828
37.7 (34.0–42.0)
914
37.5 (35.1 – 42.0)
Fever
34199
21213 (62)
10981
6862 (53)
914
438 (48)
History of fever
7244
6826 (94)
2515
2291 (91)
0
. (.)
Parasitaemia (/μL)
34376
20560 (2–250000)
13546
9720 (0–249818)
915
4514 (0–149925)
Hyperparasitaemia
34376
3223 (9)
13546
1908 (14)
915
3 (0)
Mixed infection
34377
0 (0)
13546
903 (7)
917
0 (0)
Sex (male)
33411
17223 (52)
13243
8015 (61)
917
566 (62)
Weight-for-age z-score
           
  < 5 years
21765
–0.89 (–5.93 to 4.69)
1403
–1.58 (–5.88 to 4.71)
0
  < 1 year
2323
–0.68 (–5.93 to 4.69)
56
–0.67 (–4.45 to 4.71)
0
 1–2 years
9708
–0.96 (–5.91 to 4.54)
414
–1.61 (–5.53 to 4.37)
0
 3–4 years
8305
–0.99 (–5.3 to 4.38)
869
–1.69 (–5.88 to 2.62)
0
Underweight
           
  < 5 years
21765
3918 (18)
1403
517 (37)
0
  < 1 year
2323
373 (16)
56
8 (14)
0
 1–2 years
9708
1846 (19)
414
150 (36)
0
 3–4 years
8305
1503 (18)
869
338 (39)
0
Transmission intensity
           
 Low
34105
10063 (30)
13246
11884 (90)
917
917 (100)
 Moderate
34105
10659 (31)
13246
1362 (10)
917
0 (0)
 High
34105
13383 (39)
13246
0 (0)
917
0 (0)
Derived haemoglobin, conversion from haematocrit: haemoglobin = (haematocrit–5.62)/2.60 [ 40]; Anaemia, haemoglobin < 10 g/dL; Fever, temperature > 37.5 °C; Hyperparasitaemia, parasitaemia > 100,000 parasites per μL; Weight-for-age z-score, calculated using “igrowup” package developed by WHO [ 41] in children < 5 years of age; Underweight, weight-for-age z-scores < –2
Table 2
Overview of treatment, artemisinin combination treatment dosing and formulation
 
Treatment
Dosing
 
n evaluated
N (%)
n evaluated
Partner drug dose median (Range)
Artemisinin derivative dose median (Range)
Underdosed n (%)
AL
48840
13217 (27 %)
13086
68.6 (8.9–144.0)
11.4 (1.5–24.0)
1008 (8.3 %)
AS-AQ
48840
8488 (17 %)
8395
31.9 (10.0–91.8)
12.4 (4.0–52.6)
 
AS-AQ formulation:
           
 Co-blistered nFDC
8488
573 (7 %)
573
37.4 (14.8–91.8)
13.5 (4.8–30.0)
 
 FDC
8488
4278 (50 %)
4262
32.4 (14.5–81.0)
12.0 (5.4–30.0)
2 (0.1 %)
 nFDC
8488
3637 (43 %)
3560
30.1 (10.0–60.0)
12.5 (4.0–52.6)
 
AS-MQ
48840
5198 (11 %)
4535
25.0 (4.2–85.0)
12.0 (2.3–62.1)
38 (0.8 %)
DP
48840
6453 (13 %)
6315
53.3 (14.5–182.9)
6.7 (1.8–22.9)
1488 (23.6 %)
Other, including non-ACT
48840
15484 (32 %)
       
AL, Artemether-Lumefantrine; AS-AQ, Artesunate-Amodiaquine; AS-MQ, Artesunate-Mefloquine; DP, Dihydroartemisinin-piperaquine; nFDC, Non-fixed dose combination, FDC, Fixed dose combination; Underdosed defined as ≤ 8.4 mg/kg artemether dose in AL, < 6 mg/kg dose of artesunate or DHA in other regimens [ 19]

Determinants of gametocytaemia at enrolment

Prevalence of gametocytaemia at enrolment was 12.1 % (5887/48,589), and was not significantly influenced by the slide reading method. In Africa, fractional polynomial analysis indicated a gradual decline in the proportion of gametocyte-positive smears with increasing age (Fig.  2); in Asia there was an initial increase in prevalence of gametocytaemia with increasing age in the first 20 years of life, followed by a decline with increasing age thereafter. The differences between African and Asian sites in the association between age and prevalence of gametocytaemia remained apparent when the analysis was restricted to studies with the highest sensitivity of gametocyte detection (≥100 high power fields or ≥ 1,000 WBC examined specifically for gametocytes) and when restricted to children below 5 years of age (Additional file 2: Figure S1). Prevalence of gametocytaemia at enrolment was negatively associated with haemoglobin concentration in all three continents (Table  3; Fig.  2).
Table 3
Risk factors for gametocyte prevalence at enrolment
 
Africa
Asia
South America
Parameter
Nobs/Npos (%)
OR (95 % CI)
P value
Nobs/Npos (%)
OR (95 % CI)
P value
Nobs/Npos (%)
OR (95 % CI)
P value
Univariable model
                 
Age
                 
  < 1 year
2492/403 (16.2)
2.506 (1.978–3.174)
<0.001
60/12 (20.0)
2.240 (1.124–4.460)
0.022
0/0
   
 1–4 years
20419/2799 (13.7)
2.558 (2.082–3.144)
<0.001
1374/297 (21.6)
2.095 (1.777–2.469)
<0.001
0/0
   
 5–11 years
6715/495 (7.4)
1.523 (1.245–1.864)
<0.001
3587/563 (15.7)
1.535 (1.354–1.740)
<0.001
111/19 (17.1)
0.834 (0.485–1.432)
0.510
 12+ years
4571/179 (3.9)
 
8438/977 (11.6)
 
803/139 (17.3)
 
Age (years)
34197/3876 (11.3)
0.961 (0.952–0.971)
<0.001
13459/1849 (13.7)
0.975 (0.971–0.980)
<0.001
914/158 (17.3)
0.995 (0.983–1.008)
0.453
Derived haemoglobin (g/dL)
26693/3394 (12.7)
0.785 (0.767–0.803)
<0.001
10854/1433 (13.2)
0.664 (0.645–0.683)
<0.001
606/120 (19.8)
0.600 (0.523–0.688)
<0.001
Anaemia
                 
 Yes
13441/2322 (17.3)
2.062 (1.888–2.253)
<0.001
2871/795 (27.7)
4.846 (4.258–5.516)
<0.001
48/22 (45.8)
3.972 (2.162–7.297)
<0.001
 No
13252/1072 (8.1)
Reference
 
7983/638 (8.0)
Reference
 
558/98 (17.6)
Reference
 
Fever
                 
 Yes
21115/1910 (9.1)
0.583 (0.538–0.631)
<0.001
5816/566 (9.7)
0.738 (0.649–0.839)
<0.001
438/54 (12.3)
0.529 (0.367–0.762)
0.001
 No
12925/1950 (15.1)
Reference
 
5107/730 (14.3)
Reference
 
476/105 (22.1)
Reference
 
Sex
                 
 Female
16119/1782 (11.1)
0.976 (0.908–1.048)
0.501
5205/754 (14.5)
1.083 (0.973–1.206)
0.145
350/52 (14.9)
0.756 (0.525–1.089)
0.133
 Male
17128/1963 (11.5)
Reference
 
7952/1085 (13.6)
Reference
 
566/107 (18.9)
Reference
 
Log 10 Parasitaemia (/μL)
34212/3879 (11.3)
0.590 (0.554–0.629)
<0.001
13442/1833 (13.6)
0.726 (0.677–0.779)
<0.001
914/158 (17.3)
0.333 (0.212–0.524)
<0.001
Hyperparasitaemia
                 
 Yes
3217/160 (5.0)
0.389 (0.328–0.460)
<0.001
1892/289 (15.3)
0.441 (0.338–0.575)
<0.001
0/0
   
 No
30995/3719 (12.0)
Reference
 
12568/1743 (13.9)
Reference
 
912/158 (17.3)
   
Mixed infection
                 
 Yes
     
892/106 (11.9)
1.112 (0.880–1.404)
0.374
     
 No
     
13496/1817 (13.5)
Reference
       
Weight-for-age z-score
21701/2996 (13.8)
0.932 (0.901–0.966)
<0.001
1403/305 (21.7)
0.815 (0.723–0.919)
0.001
0/0
   
Underweight
                 
 Yes
3904/651 (16.7)
1.234 (1.113–1.368)
<0.001
517/144 (27.9)
1.404 (1.029–1.915)
0.032
0/0
   
 No
17797/2345 (13.2)
Reference
 
886/161 (18.2)
Reference
 
0/0
   
TIA
                 
 Low
9995/802 (8.0)
0.990 (0.674–1.454)
0.959
11799/1383 (11.7)
0.251 (0.074–0.850)
0.026
871/147 (16.9)
   
 Moderate
10575/1069 (10.1)
1.074 (0.801–1.440)
0.631
1361/438 (32.2)
Reference
 
0/0
   
 High
13371/2000 (15.0)
Reference
 
0/0
   
0/0
   
Multivariable model
26669 / 3389 (12.7)
   
8919 / 929 (10.4)
   
605 /120 (19.8)
   
Age (years)
 
0.984 (0.974–0.994)
0.001
 
0.988 (0.982–0.994)
<0.001
     
Derived haemoglobin (g/dL)
 
0.788 (0.770–0.807)
<0.001
 
0.672 (0.648–0.697)
<0.001
 
0.581 (0.502–0.672)
<0.001
Log 10 Parasitaemia (/μL)
 
0.617 (0.575–0.662)
<0.001
 
0.735 (0.669–0.807)
<0.001
 
0.330 (0.184–0.592)
<0.001
Fever
 
0.633 (0.579–0.691)
<0.001
 
0.811 (0.689–0.954)
0.011
     
Sex (M)
       
1.252 (1.073–1.462)
0.004
 
2.144 (1.331–3.454)
0.002
Logistic univariable and multivariable mixed effects analysis by region with presence of gametocytaemia at enrolment as dependent variable. Nobs, number of observations; Npos, number of positive observations; Weight-for-age z-score, calculated using “igrowup” package developed by WHO [ 41] in children < 5 years of age; Underweight, weight-for-age z-scores < –2; TIA, Transmission intensity areas; Derived haemoglobin, conversion from haematocrit: haemoglobin = (haematocrit–5.62)/2.60 [ 40]; Anaemia, haemoglobin < 10 g/dL; Fever, temperature > 37.5 °C; Hyperparasitaemia, parasitaemia > 100,000 parasites per μL
In Asia, there was a gradual decline in prevalence of gametocytaemia with increasing asexual parasite density across the entire range of asexual parasite densities that were observed (Fig.  2). In Africa, when asexual parasite density exceeded 10,000 parasites/μL, there was a gradual decline in prevalence of gametocytaemia with increasing asexual parasite density. At lower parasite densities the uncertainty around estimates was larger and the association between prevalence of gametocytaemia and the logarithm of asexual parasite density was non-linear (Fig.  2). These differences between African and Asian sites remained apparent when the analysis was restricted to studies with the highest sensitivity of gametocyte detection (Additional file 2: Figure S1).
In all regions, individuals presenting with fever (axillary temperature >37.5 °C or reporting of febrile symptoms) were less likely to present with gametocytaemia and this remained significant after adjusting for covariates in both African (adjusted odds ratio (AOR), 0.63; 95 % CI, 0.58–0.69; P < 0.001) and Asian (AOR, 0.81; 95 % CI, 0.69–0.95; P = 0.011) patients. Male gender was a predictor of prevalence of gametocytaemia at enrolment in studies in Asia (AOR, 1.25; 95 % CI, 1.07–1.46; P = 0.004) and South America (AOR, 2.14; 95 % CI, 1.33–3.45; P = 0.002) but not Africa (Table  3). Children under 5 years of age who were malnourished (weight-for-age z-scores < –2) had a higher prevalence of gametocytaemia at enrolment compared to well-nourished children in Africa (OR, 1.23; 95 % CI, 1.11–1.37; P < 0.001) and in Asia (OR, 1.40; 95 % CI, 1.03–1.92; P = 0.032) but this was not significant in the multivariable analysis (Additional file 3: Table S2).

Gametocytaemia after artemisinin combination therapy

No gametocytaemia at enrolment

Amongst the 18,388 individuals presenting without patent gametocytaemia by microscopy who were treated with an ACT, the Kaplan–Meier estimate of risk of appearance of gametocytaemia within 28 days was 1.9 % (95 % CI, 1.7–2.1) (Fig.  3a). This proportion was similar in African and Asian studies. After controlling for confounding factors, the risk of appearance of gametocytaemia correlated negatively with age, haemoglobin concentration, fever and asexual parasite density at enrolment (Table  4). Appearance of gametocytaemia was lowest after AS-MQ or AL treatment and significantly higher after DP (adjusted hazard ratio (AHR), 2.03; 95 % CI, 1.24–3.32; P = 0.005 compared to AL) and AS-AQ (AHR, 4.01; 95 % CI, 2.40–6.72; P < 0.001 compared to AL) (Fig.  3a, Table  4). A dose of the artemisinin component < 8 mg/kg was associated with an increased chance of appearance of gametocytaemia after treatment with DP (AHR, 2.78; 95 % CI, 1.18–6.55; P = 0.020) but not after treatment with any of the other ACTs (Additional file 4: Table S3). No association was observed for a dose of the artemisinin component < 6 mg/kg either for all treatment combined or for DP alone.
Table 4
Factors associated with the development of gametocytaemia after enrolment in individuals without microscopically detected gametocytes before treatment with artemisinin combination therapy
Parameter
Nobs
Npos
per
Hazard ratio (95 % CI)
P value
Univariable model
         
ACT a
         
 AS-MQ
3082
20
0.6
0.763 (0.392–1.484)
0.425
 DP
3855
93
2.4
2.746 (1.773–4.253)
<0.001
 AS-AQ: FDC
2919
151
5.2
4.094 (2.540–6.600)
<0.001
 AL
8532
97
1.1
Reference
 
Age a
         
  < 1 year
776
23
3.0
2.435 (1.268–4.676)
0.008
 1–4 years
7772
236
3.0
2.780 (1.698–4.552)
<0.001
 5–11 years
4102
58
1.4
1.928 (1.225–3.035)
0.005
 12+ years
5735
44
0.8
Reference
 
Age (years)
18385
361
2.0
0.965 (0.946–0.984)
<0.001
Derived haemoglobin (g/dL)
14357
295
2.1
0.809 (0.758–0.862)
<0.001
Anaemia
         
 Yes
5505
183
3.3
1.824 (1.402–2.373)
<0.001
 No
8852
112
1.3
Reference
 
Fever
         
 Yes
10569
173
1.6
0.594 (0.470–0.749)
<0.001
 No
7244
173
2.4
Reference
 
Sex
         
 Female
8427
160
1.9
0.931 (0.754–1.149)
0.505
 Male
9658
196
2.0
Reference
 
Hyperparasitaemia
         
 Yes
1543
19
1.2
0.615 (0.384–0.984)
0.043
 No
16845
342
2.0
Reference
 
Log 10 parasitaemia (/μL)
18388
361
2.0
0.731 (0.619–0.864)
<0.001
Weight-for-age score
         
Underweight b
8341
257
3.1
0.825 (0.744–0.915)
<0.001
 Yes
1418
80
5.6
1.768 (1.343–2.326)
<0.001
 No
6923
177
2.6
Reference
 
Region
         
 Asia
3895
55
1.4
1.078 (0.356–3.263)
0.894
 South America
615
8
1.3
0.482 (0.035–6.564)
0.584
 Africa
13878
298
2.1
Reference
 
TIA a
         
 Low
8406
64
0.8
0.371 (0.159–0.866)
0.022
 Moderate
5120
129
2.5
0.746 (0.426–1.306)
0.305
 High
4449
161
3.6
Reference
 
Multivariable model
14051
291
2.1
   
ACT:
         
 AS-MQ
     
0.566 ( 0.225–1.420)
0.225
 DP
     
2.029 (1.240–3.317)
0.005
 AS-AQ: FDC
     
4.014 (2.398–6.719)
<0.001
 AL
     
Reference
 
Age
         
  < 1 year
     
1.707 ( 0.778–3.747)
0.269
 1–4 years
     
2.303 (1.208–4.392)
0.011
 5–11 years
     
1.418 (0.795–2.527)
0.237
 12+ years
     
Reference
 
Derived haemoglobin (g/dL)
     
0.828 (0.774–0.886)
<0.001
Fever
     
0.653 (0.503–0.848)
0.001
Log 10 parasitaemia (/μL)
     
0.757 (0.624–0.917)
0.004
Cox regression mixed effects model for time to gametocytaemia
Nobs, Number of observations; Npos, Number of positive observations; Weight-for-age z-score, calculated using “igrowup” package developed by WHO [ 41] in children < 5 years of age; Underweight, weight-for-age z-scores < –2; TIA, Transmission intensity areas. a Proportional hazards assumption not satisfied; b In multivariable analysis: HR, 1.51; 95 % CI, 1.13–2.02; P = 0.005, after adjusting for covariates in the main model

Gametocytaemia at enrolment

A total of 2433 patients treated with an ACT were gametocytaemic at enrolment and had no recurrent infection. Overall, 57.4 % (95 % CI, 55.4–59.4) of these patients cleared gametocytaemia by day 7, 78.4 % (95 % CI, 76.5–80.2) by day 14 and 88.2 % (95 % CI, 86.6–89.6) by day 21. The only independent determinants of gametocyte clearance were initial gametocyte density (AHR, 0.87; 95 % CI, 0.83–0.91; P < 0.001 for log increase in gametocyte density) and the type of ACT given (Additional file 5: Table S4, Fig.  3b). Compared to AL, gametocytaemia clearance was significantly faster with AS-MQ (AHR, 1.26; 95 % CI, 1.00–1.60; P = 0.054) and slower with DP (AHR, 0.74; 95 % CI, 0.63–0.88; P = 0.001) (Fig.  3b). For the AS-AQ FDC, the rate of gametocytaemia clearance was significantly slower compared to that of AS-MQ (HR, 0.64; 95 % CI, 0.48–0.85; P = 0.002), and non-significantly slower compared to AL (HR, 0.80; 95 % CI, 0.63–1.02; P = 0.072). The overall observed proportion of patients who cleared gametocytes by day 7 was 64.4 % for AL, 61.7 % for AS-MQ, 52.3 % for DP, and 47.8 % for AS-AQ, while by day 14 gametocytes were cleared by 85.7 %, 90.2 %, 70.3 %, and 72.1 % of patients, respectively.

Gametocytaemia in relation to asexual parasite clearance time and treatment response

Asexual parasite clearance was rapid for all treatments with 8.8 %, 9.1 %, 6.4 %, and 7.8 % of patients having residual asexual parasites after 2 days treatment with AL, AS-MQ, AS-AQ-FDC, and DP, respectively. On day 3, these figures were 0.8 %, 1.3 %, 0.4 %, and 0.7 %. Residual asexual parasite prevalence on day 1, 2 or 3 was not associated with gametocytaemia clearance or the appearance of gametocytaemia in univariable or multivariable analysis. Individuals who experienced PCR-confirmed treatment failure by day 28 were more likely to be gametocytaemic on any day during follow-up (AOR, 2.12; 95 % CI, 1.08–4.34; P = 0.025) and develop gametocytaemia after day 7 (AOR, 9.05; 95 % CI, 3.74–21.91; P < 0.001) compared to patients with no recorded recurrence and at least 28 days follow-up. Similarly, the increased risk of gametocytaemia on any day during follow-up (AOR, 1.95; 95 % CI, 1.37–2.77; P < 0.001) and of developing gametocytaemia after day 7 (AOR, 3.03; 95 % CI, 1.66–5.54; P < 0.001) was observed in individuals with reinfection (Fig.  4a). This association was not explained by differences in artemisinin dosing. Gametocytaemia clearance in individuals with gametocytaemia prior to treatment was not associated with treatment outcome (Fig.  4b).

Assessment of potential bias

Attrition bias of the included studies is presented in Additional file 6: Table S5. Although many studies were not blinded, the blinding of the independent outcome laboratory assessments (i.e. microscopy readings to measure gametocytaemia and PCR classification of treatment outcome are performed by laboratory staff not directly involved in the study), minimize the risk of bias in outcome assignment. We consider publication bias unlikely since gametocytaemia measurements were a primary outcome in only 2 (out of 121) publications and gametocytaemia results are unlikely to have influenced the decision to publish. Sensitivity analyses showed that exclusion of any of the studies did not change the main conclusions of the analysis (Additional file 7: Table S6). Results for time to gametocytaemia were also confirmed for all covariates except for age when analysis was restricted to individuals with complete weekly data on gametocytaemia (Additional file 8: Table S7 and Additional file 9: Figure S2). The fact that the effect of age was lost may be due to a considerable loss of observations in this sub-analysis that differed by age groups: 12 %, 15 %, 33 %, and 32 % of patients in groups <1 year, 1–4 years, 5–11 years, and ≥12 years of age were not included in the sub-analysis.

Discussion

We analysed data from nearly 50,000 patients from trials that included measures of gametocytaemia by blood smears. The prevalence of gametocytaemia before and after treatment was greatest in young patients, and those with lower asexual parasite density, anaemia and absence of fever. After treatment with an ACT, the appearance and clearance of gametocytaemia was determined by the type of ACT with AL and AS-MQ being most efficacious in preventing post-treatment gametocyte carriage.
Gametocytaemia is essential for onward transmission of malaria infections to mosquitoes. Understanding factors that influence gametocytaemia prior to treatment and the gametocytocidal properties of antimalarial drugs is of great relevance for interventions that aim to reduce malaria transmission. Mature P. falciparum gametocytes first appear in the human bloodstream 7 to 15 days after the initial wave of their asexual parasite progenitors. This long maturation process and the impacts of human and parasite factors associated with gametocyte production [ 5] result in considerable variation in the proportion of malaria patients harbouring gametocytes upon presentation with clinical illness. We observed that the same host characteristics influenced gametocytaemia before and after treatment. The prevalence of gametocytaemia was higher in patients with anaemia and without concurrent fever [ 4, 24]. Reduced haemoglobin concentrations are often a consequence of prolonged duration of infections or recurrent malaria episodes [ 25, 26], both of which have been associated with increased gametocyte production [ 4]. Anaemia may also be an independent predictor of gametocytaemia [ 4, 27] since low haemoglobin concentrations and reticulocytosis directly stimulate gametocyte production [ 28, 29]. The association between asexual parasite density at enrolment and gametocytaemia was different in Asian and African settings. In Asian studies, the prevalence of gametocytaemia showed a gradual negative association with asexual parasite density [ 4], whilst in Africa, this negative association was only apparent at asexual parasite densities above 5,000 parasites/μL. These setting-dependent patterns may explain previous inconsistent reports on the association between asexual parasite densities and gametocytaemia [ 4, 27, 30, 31]. These three predictors of gametocytaemia (anaemia, lower asexual parasite density and absence of fever) may all reflect chronic infections that, because of their longer duration, may be more likely to present with gametocytaemia. Host immunity and the likelihood of super-infections vary significantly with transmission intensity and both influence asexual parasite densities and gametocyte dynamics. Age is a useful surrogate of immunity. In African studies, there was a gradual decrease in the prevalence of gametocytaemia with increasing age, while in Asia, the prevalence of gametocytaemia increased until approximately 20 years of age followed by a general decline thereafter. Further studies are needed to determine whether this pattern is explained by host-factors or by age or occupation-associated malaria exposure in Asian settings.
Patients presenting with gametocytaemia cleared their gametocytaemia rapidly following ACT, with 57 % of patients being gametocyte-free by day 7 and 88 % by day 21. The rate of gametocytaemia clearance varied significantly with the ACT regimen. Differential effects of ACT on post-treatment gametocytaemia have been reported previously, but with contradicting results [ 3234]. Our large meta-analysis revealed that both the appearance and duration of gametocytaemia were 2-fold and 25 % lower, respectively, in AL- compared to DP-treated patients. In individuals treated with DP, a lower treatment dose was associated with an increased appearance of gametocytaemia after treatment. We previously demonstrated that treatment failure is also associated with DP dosing [ 10] and the World Health Organization recently increased the dose recommendation for DP to ensure a minimum of 7.5 mg/kg total dose of dihydroartemisinin in children < 25 kg [ 35]. The appearance of gametocytaemia after AS-AQ FDC was markedly more prevalent than after either AL or AS-MQ. Furthermore, gametocytaemia clearance was slower after AS-AQ FDC compared to AS-MQ. This striking difference of AS-AQ FDC compared to AL and AS-MQ could not be explained by differences in total artemisinin dosing or treatment outcome. These differential effects of ACTs may relate to the frequency of artemisinin dosing or to the activity of the non-artemisinin partner drug. In vitro drug screening assays indicate similar activity of lumefantrine and amodiaquine against mature gametocytes [ 36], whilst developing gametocytes appear more susceptible to mefloquine and lumefantrine than to amodiaquine [ 37]. This would suggest that the maturation of developing gametocytes after initiation of treatment differs between ACT regimens, and this has consequences for post-treatment gametocytaemia.
Contrary to previous studies [ 38, 39], we found no association between the rate of asexual parasite clearance and gametocytaemia during follow-up. For chloroquine and sulphadoxine-pyrimethamine treatment, post-treatment gametocytaemia and malaria transmission to mosquitoes have been proposed as early parasitological indicators of reduced drug sensitivity [ 40, 41]. In our study, >98 % of all patients cleared their infections by day 2 post-initiation of treatment. Patients subsequently failing treatment were at 15-fold greater risk of gametocytaemia than those successfully treated, and this was similar for both PCR confirmed recrudescent and new infections. The timing of gametocytaemia coincided with the recurrent asexual parasitaemia. Since the earliest developmental stages of gametocytes are sequestered for 6–8 days in the bone marrow [ 42], this suggests that gametocyte production started before reappearing asexual parasites were detected by microscopy. The strong association of gametocytaemia with recrudescent infections and new infections warns against a simplistic comparison of treatment regimens based on gametocytaemia shortly after treatment. Initial treatment efficacy and post-treatment prophylaxis that postpones new infection, and therefore de novo gametocyte production, are important determinants of the impact of ACT regimens on malaria transmission.
Whilst our analysis focuses on peripheral gametocytaemia, it is important to acknowledge that this is a surrogate marker of malaria transmission potential. The infectivity of persisting or appearing gametocytes may be affected by the type of antimalarial treatment [ 9]. Antimalarial drugs may also influence gametocyte sex-ratio [ 43], which is an important determinant of transmission success, although there is currently no evidence for a differential effect of ACT regimens on male and female gametocytes. The only available study that directly determined infectiousness to mosquitoes after ACT regimens compared in this study supports our findings, reporting a two-fold higher mosquito infection rate after DP compared to AL [ 18], which is consistent with our finding of significantly higher risk of gametocyte appearance after DP (AHR, 2.03; 95 % CI, 1.24–3.34; P = 0.005 compared to AL). Gametocyte densities commonly fluctuate around the microscopic threshold for detection and the use of molecular gametocyte detection tools would have uncovered a higher proportion of gametocyte carriers [ 5] at densities capable of contributing to onward malaria transmission [ 44]. The addition of a single low primaquine dose to ACT can substantially reduce the duration of low density gametocytaemia after treatment [ 45] and prevent transmission to mosquitoes [ 46, 47] but primaquine is currently not routinely added to ACTs for treatment of uncomplicated malaria. Importantly, although the gametocytocidal properties of first-line ACTs may influence community-wide transmission [ 16, 48], this effect may be modest if transmission is largely driven by asymptomatic individuals who do not seek treatment. The inclusion of these asymptomatically infected individuals in treatment campaigns may have a much larger impact on malaria transmission than the choice of ACT for first-line treatment [ 6, 7].
Our analysis was purposefully restricted to microscopic findings on gametocytaemia, for which most data are available. Although this approach will have missed some gametocyte carriers, this would not affect the comparison of treatment arms. Studies where microscopy, molecular gametocyte data and infectivity results are available indicate that these methods lead to the same conclusions on the comparative effects of antimalarials on post-treatment gametocyte dynamics and infectivity [ 15, 18].

Conclusions

In conclusion, we identified independent risk factors for the prevalence of gametocytaemia in patients with uncomplicated falciparum malaria in studies conducted on three continents. AS-MQ and AL are superior ACT options in preventing gametocytes shortly after treatment compared to DP or AS-AQ. We hypothesize that this difference is due to the non-artemisinin partner drug defining post-treatment gametocyte dynamics.

Acknowledgments

We thank the patients and all the staff who participated in these clinical trials at all the sites and the WorldWide Antimalarial Resistance Network (WWARN) team for technical and administrative support. We would also like to thank Sigma-Tau for sharing data and Hasifa Bukirwa, Marco Corsi, Oumar Faye, Bouasy Hongvanthong, Anand Joshi, Maniphone Khanthavong Moussa Kone, Elfaith Malik, Ushma Mehta, Albert Same-Ekobo, Bhwana Sharma, and Roger CK Tine for their contributions to the studies used in the pooled analysis. WWARN is funded by a Bill and Melinda Gates Foundation grant. The funder did not participate in developing the protocol or writing the paper. Teun Bousema is supported by a fellowship from the European Research Council (ERC-2014-StG 639776).
The members of the WWARN Gametocytes Study Group are the authors of this paper:
Salim Abdulla, Ifakara Health Institute, Dar es Salaam, Tanzania; Jane Achan, Uganda Malaria Surveillance Project, Kampala, Uganda and Medical Research Council Unit, Fajara, The Gambia; Ishag Adam, Faculty of Medicine, University of Khartoum, Khartoum, Sudan; Bereket H Alemayehu, ICAP at Mailman School of Public Health, Columbia University, New York, USA; Richard Allan, The MENTOR Initiative, Crawley, UK; Elizabeth N Allen, Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa; Anupkumar R Anvikar, National Institute of Malaria Research, New Delhi, India; Emmanuel Arinaitwe, Infectious Diseases Research Collaboration, Kampala, Uganda; Elizabeth A Ashley, Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand and Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Puji Budi Setia Asih, Eijkman Institute for Molecular Biology; Ghulam Rahim Awab, The Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand and Ministry of Public Health, Islamic Republic of Afghanistan, Kabul, Afghanistan; Karen I Barnes, WWARN, Oxford, UK and Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa; Quique Bassat, Centro de Investigação em Saude de Manhiça, Manhiça, Mozambique and ISGlobal, Centre de Recerca en Salut Internacional de Barcelona (CRESIB), Hospital Clinic, Universitat de Barcelona, Barcelona, Spain; Elisabeth Baudin, Epicentre, Paris, France; Anders Björkman, Department of Microbiology Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Francois Bompart, Sanofi, Access to Medicines, Paris, France; Maryline Bonnet, Epicentre, Mbarara, Uganda and Institute de Recherche pour le Developpement UMI233, INSERM U1175, Université de Montpellier, Montelier, France; Steffen Borrmann, Kenya Medical Research Institute/Wellcome Trust Research Programme, Kilifi, Kenya and Institute for Tropical Medicine, University of Tübingen, Germany; Teun Bousema, Department of Infection and Immunity, London School of Hygiene & Tropical Medicine (LSHTM), London, UK and Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, Njimegen, The Netherlands; Verena I Carrara, Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand and the Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand; Fabio Cenci, Sigma Tau, Rome, Italy; Francesco Checchi, Epicentre, Paris, France; Michel Cot, IRD, Mother and Child Health in the Tropics Research Unit, Université Paris Descartes, Paris, France; Prabin Dahal, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Umberto D’Alessandro, Medical Research Council Unit, Fajara, The Gambia, LSHTM, London, UK and Institute of Tropical Medicine, Antwerp, Belgium; Philippe Deloron, Institut de Recherche pour le Développement, Mother and Child Faced with Tropical Infections Research Unit, Paris, France and PRES Paris Sorbonne Cité, Université Paris Descartes, Paris, France; Abdoulaye Djimde, Malaria Research and Training Center, Department of Epidemiology of Parasitic Diseases, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Bamako, Mali; Arjen Dondorp, The Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Grant Dorsey, Department of Medicine, University of California San Francisco, San Francisco, USA; Ogobara K Doumbo, Malaria Research and Training Center, Department of Epidemiology of Parasitic Diseases, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Bamako, Mali; Chris J Drakeley, Department of Infection and Immunity, LSHTM, London, UK; Stephan Duparc, Medicines for Malaria Venture, Geneva, Switzerland; Emmanuelle Espie, Epicentre, Paris, France; Abul Faiz, Dev Care Foundation, Bangladesh; Catherine O Falade, Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Ibadan, Nigeria; Caterina Fanello, The Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand; Jean‐François Faucher, Institut de Recherche pour le Développement (IRD), Mother and Child Health in the Tropics Research Unit, Paris, France and Faculté de Pharmacie, Université Paris Descartes, Paris, France and Department of Infectious Diseases, Besançon University Medical Center, Besançon, France; Babacar Faye, Department of Medical Parasitology, Medical Faculty, Université Cheikh Anta Diop, Dakar, Senegal; Scott Filler, The Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland; Bakary Fofana, Malaria Research and Training Center, Department of Epidemiology of Parasitic Diseases, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Bamako, Mali; Carole Fogg, University of Portsmouth, Portsmouth, UK; Adama Gansane, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso; Oumar Gaye, Department of Medical Parasitology, Medical Faculty, Université Cheikh Anta Diop, Dakar, Senegal; Blaise Genton, Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland and Division of Infectious Diseases and Department of Ambulatory Care and Community Medicine, University Hospital, Lausanne, Switzerland; Peter W Gething, Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK; Raquel Gonzalez, Centro de Investigação em Saude de Manhiça, Manhiça, Mozambique and ISGlobal, Barcelona Ctr. Int Health Res. (CRESIB), Hospital Clínic, Universitat de Barcelona, Spain; Francesco Grandesso, Epicentre, Paris, France; Brian Greenwood, Department of Diseases Control, LSHTM, London, UK; Anastasia Grivoyannis, University of Washington, USA; Philippe J Guerin, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Kamal Hamed, Novartis Pharmaceuticals Corporation, East Hanover, USA; Christoph Hatz, Medical Department, Swiss Tropical Institute, Basel, Switzerland; Simon I Hay, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK and Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA; Eva Maria Hodel, Swiss Tropical Institute and Public Health Institute, Basel, Switzerland and Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK; Georgina S Humphreys, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Jimee Hwang, U.S. Centers for Disease Control and Prevention, Atlanta, USA and Global Health Group, University of California San Francisco, San Francisco, USA; Bart Janssens, Médecins Sans Frontières, Operational Centre Brussels, Brussels, Belgium; Daddi Jima, Federal Ministry of Health, Addis Ababa, Ethiopia; Elizabeth Juma, Kenya Medical Research Institute, Nairobi, Kenya; S Patrick Kachur, U.S. Centers for Disease Control and Prevention, Atlanta, USA; Piet Kager, Academic Medical Centre, Amsterdam, Netherlands; Moses R Kamya, Makerere University College of Health Sciences, Kampala, Uganda; Melissa Kapulu, Kenya Medical Research Institute/Wellcome Trust Research Programme, Kilifi, Kenya and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Corine Karema, Malaria & Other Parasitic Diseases Division, RBC, Ministry of Health, Kigali, Rwanda; Kassoum Kayentao, Malaria Research and Training Centre, Department of Epidemiology of Parasitologic Diseases, Faculty of Medicine, Pharmacy and Dentistry, University of Bamako, Mali; Jean R Kiechel, Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland; Poul-Erik Kofoed, Projecto de Saúde de Bandim, Bissau, Guinea-Bissau and Health Services Research Unit, Lillebaelt Hospital/IRS University of Southern Denmark, Vejle, Denmark and Department of Paediatrics, Kolding Hospital, Kolding, Denmark; Valerie Lameyre, Sanofi, Access to Medicines, Paris, France; Sue J Lee, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Bertrand Lell, Institute for Tropical Medicine, University of Tubingen, Tubingen, Germany and Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon; Nines Lima, Médecins Sans Frontières – Operational Centre Barcelona Athens, Barcelona, Spain; Kevin Marsh, Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK and Kenya Medical Research Institute/Wellcome Trust Research Programme, Kilifi, Kenya; Andreas Mårtensson, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden and Department of Women’s and Children’s Health, International Maternal and Child Health (IMCH), Upssala University, Uppsala, Sweden; Achille Massougbodji, Centre d’Etudes et de Recherche sur le Paludisme Associé à la Grossesse et à l’Enfant (CERPAGE), Faculté des Sciences de la Santé (FSS), Université d’Abomey-Calavi, Cotonou, Bénin; Mayfong Mayxay, Lao-Oxford-Mahosot Hospital, Wellcome Trust Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR and Faculty of Postgraduate Studies, University of Health Sciences, Vientiane, Lao PDR and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Rose McGready, Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Hervé Menan, Department of Parasitology, Faculty of Pharmacy, University of Cocody, Abidjan, Côte d'Ivoire; Clara Menendez, Barcelona Institute for Global Health (ISGlobal) Hospital Clinic, University of Barcelona, Spain and CISM, Manhiça Health Research Center, Manhiça, Mozambique; Petra Mens, Royal Tropical Institute, KIT Biomedical Research, Amsterdam, The Netherlands and Division of Infectious Diseases, Center for Tropical Medicine & Travel Medicine, Academic Medical Center, University of Amsterdam, The Netherlands; Martin Meremikwu, Department of Paediatrics, University of Calabar and Nigeria Institute of Tropical Diseases Research & Prevention, Calabar, Nigeria; Frank P Mockenhaupt, Institute of Tropical Medicine and International Health, Charite-Universitatsmedizin Berlin, Germany; Clarissa Moreira, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Carolyn Nabasumba, Epicentre, Paris, France and Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda; Michael Nambozi, Tropical Diseases Research Centre, Ndola, Zambia; Jean-Louis Ndiaye, Parasitology and Mycology Laboratory, Medical Faculty, Université Cheikh Anta Diop, Dakar, Senegal; Paul N Newton, Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Mahosot Hospital, Vientiane, Lao PDR and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Billy E Ngasala, Department of Parasitology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania and Malaria Research, Infectious Disease Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden; Francois Nosten, Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Christian Nsanzabana, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Andre Toure Offianan, Malariology Department, Institut Pasteur, Abidjan, Côte d'Ivoire; Mary Oguike, Department of Immunology & Infection, LSHTM, London, UK; Bernhards R Ogutu, Kenya Medical Research Institute/United States Army Medical Research Unit, Kisumu, Kenya; Piero Olliaro, UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (WHO/TDR), Geneva, Switzerland; Sabah A Omar, International Centre for Insect Physiology and Ecology (ICIPE), Mbita Point, Kenya; Lyda Osorio, Facultad de Salud, Universidad del Valle, Cali, Colombia; Seth Owusu-Agyei, Kintampo Health Research Centre, Kintampo, Ghana; Louis K Penali, WWARN, Dakar, Senegal; Mbaye Pene, Department of Medical Parasitology, Medical Faculty, Université Cheikh Anta Diop, Dakar, Senegal; Judy Peshu, Kenya Medical Research Institute/Wellcome Trust Research Programme, Kilifi, Kenya; Patrice Piola, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Zul Premji, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania; Ric N Price, Menzies School of Health Research and Charles Darwin University, Darwin, Australia and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK and WWARN, Oxford, UK; Michael Ramharter, Department of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University Vienna, Austria and Institut für Tropenmedizin, Universität Tübingen, Germany and Centre de Recherches Medicales de Lambaréné, Gabon; Lars Rombo, Infectious Diseases Unit, Department of Medicine Solna, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden; Cally Roper, LSHTM, London, UK; Philip J Rosenthal, Department of Medicine, University of California San Francisco, San Francisco, USA; Issaka Sagara, Malaria Research and Training Center, Department of Epidemiology of Parasitic Diseases, Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako, Bamako, Mali; Patrick Sawa, Human Health Division, International Centre for Insect Physiology and Ecology, Mbita, Kenya; Henk DFH Schallig, Royal Tropical Institute, KIT Biomedical Research, Amsterdam, The Netherlands; Birgit Schramm, Epicentre, Paris, France; Seif A Shekalaghe, Ifakara Health Institute, Bagamoyo, Tanzania; Carol H Sibley, WWARN, Oxford, UK and Department of Genome Sciences, University of Washington, Seattle, USA; Sodiomon Sirima, Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso; Frank Smithuis, Myanmar Oxford Clinical Research Unit (MOCRU), Yangon, Myanmar and Medical Action Myanmar, Yangon, Myanmar; Doudou Sow, Service de Parasitologie, Medical Faculty, Université Cheikh Anta Diop, Dakar, Sénégal; Sarah G Staedke, Department of Clinical Research, LSHTM, London, UK and Infectious Disease Research Collaboration, Kampala, Uganda; Kasia Stepniewska, WWARN, Oxford, UK and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Inge Sutanto, Department of Parasitology, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia; Colin J Sutherland , Department of Immunology & Infection, LSHTM, London, UK; Todd D Swarthout, Médecins Sans Frontières, London, UK; Din Syafruddin, Eijkman Institute for Molecular Biology, Jakarta, Indonesia; Khadime Sylla, Service de Parasitologie-Mycologie Médicale, Université Cheikh Anta Diop, Dakar, Senegal; Ambrose O Talisuna, University of Oxford/KEMRI/Wellcome Trust Research Programme, Nairobi, Kenya; Walter R Taylor, Institute for Biomechanics, Department of Health Science and Technology, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland; Emmanuel A Temu, The MENTOR Initiative, Crawley, UK and Swiss Tropical and Public Health Institute, Basel, Switzerland and University of Basel, Basel, Switzerland; Feiko Ter Kuile, Liverpool School of Tropical Medicine, Liverpool, UK and Kenya Medical Research Institute (KEMRI), Centre for Global Health Research, Kisumu, Kenya; Halidou Tinto, Institut de Recherce en Sciences de la Sante, Bobo Dioulasso, Burkina Faso and Centre Muraz, Bobo Dioulasso, Burkina Faso; Emiliana Tjitra, National Institute of Health Research and Development, Ministry of Health, Jakarta, Indonesia; Johan Ursing, Projecto de Saúde de Bandim, Indepth Network, Bissau, Guinea-Bissau and Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Neena Valecha, National Institute of Malaria Research, New Delhi, India; Ingrid van den Broek, Médecins Sans Frontières, London, UK and Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Michel van Herp, Médecins Sans Frontières, Operational Centre Brussels, Brussels, Belgium; Michele van Vugt, Division of Infectious Diseases, Center for Tropical Medicine & Travel Medicine, Academic Medical Center, University of Amsterdam, The Netherlands; Stephen A Ward, Department of Parasitology, Liverpool School of Tropical Medicine, Liverpool, UK; Nicholas J White, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Peter A Winstanley, School of Clinical Sciences, University of Liverpool, Liverpool, UK; Charles J Woodrow, The Mahidol Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand and Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Adoke Yeka, Uganda Malaria Surveillance Project, Kampala, Uganda; Julien Zwang, Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland.

Authors’ contributions

TB, KSt, GSH, CJD, CHS, PJG and RNP conceived and designed the experiments. KSt and GSH analyzed the pooled individual patient data. GSH, CNs and PDa performed literature search. GSH, PDa and CMo data management. SA, JA, IA, BHA, RA, ENA, ARA, EA, EAA, PBSA, GRA, KIB, QB, EB, AB, FB, MB, SB, TB, VIC, FC, FCh, MC, UdA, PDe, ADj, ADo, GD, OKD, CJD, SD, EE, AF, COF, CFa, JFF, BFa, SF, BFo, CFo, AGa, OG, BGe, PWG, RG, FG, BGr, AGr, PJG, KH, CH, SIH, EMH, JH, BJ, DJ, EJ, SPK, PK, MRK, MK, CK, KK, JRK, PEK, VL, SJL, BL, NL, KM, AMar, AMas, MMa, RM, HM, CMe, PM, MMe, FPM, CNa, MN, JLN, PNN, BEN, FN, CNs, ATO, MO, BRO, PO, SAO, LO, SOA, LKP, MP, JP, PP, ZP, RNP, MR, LR, CR, PJR, ISa, PS, HDFHS, BS, SAS, SS, FS, DSo, SGS, KSt, ISu, CJS, TDS, DSy, KSy, AOT, WRT, EAT, FTK, HT, ET, JU, NV, IvdB, MvH, MvV, SAW, NJW, PAW, CJW, AY and JZ performed the original experiments. PWG and SIH provided transmission intensity estimates. TB, KSt, GSH, CJD, CHS, PJG and RNP wrote the first draft of the manuscript. SA, JA, IA, BHA, RA, ENA, ARA, EA, EAA, PBSA, GRA, KIB, QB, EB, AB, FB, MB, SB, TB, VIC, FC, FCh, MC, PDa, UdA, PDe, ADj, ADo, GD, OKD, CJD, SD, EE, AF, COF, CFa, JFF, BFa, SF, BFo, CFo, AGa, OG, BGe, PWG, RG, FG, BGr, AGr, PJG, KH, CH, SIH, EMH, GSH, JH, BJ, DJ, EJ, SPK, PK, MRK, MK, CK, KK, JRK, PEK, VL, SJL, BL, NL, KM, AMar, AMas, MMa, RM, HM, CMe, PM, MMe, FPM, CMo, CNa, MN, JLN, PNN, BEN, FN, CNs, ATO, MO, BRO, PO, SAO, LO, SOA, LKP, MP, JP, PP, ZP, RNP, MR, LR, CR, PJR, ISa, PS, HDFHS, BS, SAS, CHS, SS, FS, Dso, SGS, KSt, ISu, CJS, TDS, Dsy, KSy, AOT, WRT, EAT, FTK, HT, ET, JU, NV, IvdB, MvH, MvV, SAW, NJW, PAW, CJW, AY and JZ International Committee of Medical Journal Editors (ICMJE) criteria for authorship read and met. All authors contributed to the writing of the manuscript and agree with manuscript results and conclusions. All authors read and approved the final manuscript.

Competing interests

FB and VL are employees of Sanofi. SD is an employee of Medicines for Malaria Venture, Geneva, Switzerland. KH is an employee of Novartis Pharmaceuticals, East Hanover, NJ, USA. FS is an employee of Sigma Tau. The remaining authors declare that no competing interests exist.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.
Zusatzmaterial
Additional file 1: Table S1. Overview of all included studies. 1 The sensitivity of microscopy methods was classified into one of four categories: 1 = studies in which slides were specifically read for gametocytes, reviewing at least 100 microscopic high power fields or against ≥ 1000 white blood cells (WBC); 2 = microscopists specifically instructed to record gametocytes but slides were primarily read for asexual parasites; ≥ 100 microscopic high power fields per ≥ 1000 WBC were read; 3 = microscopists were specifically instructed to record gametocytes; 50–99 microscopic high power fields per 500–999 WBC were read; 4 = microscopists were not specifically instructed to record gametocytes or the number of examined high power fields was < 50 or the number of WBC was < 500. 2 All treatment combinations are loose, unless stated. FDC, fixed dose combination. AS, Artesunate; MQ, Mefloquine; AL, Artemether-lumefantrine; DP, Dihydrartemisinin-piperaquine; SP, Sulphadoxine-pyrimethamine; AQcb, AQ co-blisterered loose combination; HL, Halofantrine; QN, Quinine; AM, Artemether, AV, Atovaquone; PG, Proguanil; CQ, Chloroquine; CDA, Chlorproguanil-dapsone-artesunate; Tet, Tetracycline; CL, Clindamycine. Search strategy: Published prospective trials were identified by the application of the key terms ((malaria OR plasmod*) AND (amodiaquine OR atovaquone OR artemisinin OR arteether OR artesunate OR artemether OR artemotil OR azithromycin OR artekin OR chloroquine OR chlorproguanil OR cycloguanil OR clindamycin OR coartem OR dapsone OR dihydroartemisinin OR duo-cotecxin OR doxycycline OR halofantrine OR lumefantrine OR lariam OR malarone OR mefloquine OR naphthoquine OR naphthoquinone OR piperaquine OR primaquine OR proguanil OR pyrimethamine OR pyronaridine OR quinidine OR quinine OR riamet OR sulphadoxine OR tetracycline OR tafenoquine)) though the PubMed library. Studies on prevention, prophylaxis, review, animal studies or patients with severe malaria were excluded. (DOCX 155 kb)
Additional file 3: Table S2. Independent risk factors for the prevalence of gametocytaemia at enrolment in children aged 1–5 years. Logistic multivariable analysis by region with prevalence of gametocytaemia at enrolment as dependent variable. Nobs, Number of observations; Npos, Number of positive observations. The relationship between gametocyte prevalence at enrolment and age is statistically significant (P < 0.001) although not linear, see Additional file 2: Figure S1. Malnutrition (underweight) was not an independent predictor (AOR, 1.11; 95 % CI, 0.99–1.26; P = 0.083) in Africa and (AOR, 0.92; 95 % CI, 0.47–0.83; P = 0.823) in Asia, after adjustment for age, haemoglobin, parasitaemia and fever (after polynomial transformations, as presented in Additional file 2: Figure S1). (DOC 28 kb)
Additional file 4: Table S3. The effect of treatment dosing on the appearance of gametocytaemia in participants without microscopically detected gametocytaemia before treatment (time to gametocytaemia) and clearance of gametocytaemia in participants with gametocytaemia at enrolment (time to clearance). All analyses of time to clearance are adjusted for log of the initial gametocyte density. Nobs, Number of observations; Npos, Number of positive observations. Under-dosed defined as ≤ 8.4 mg/kg artemether dose in AL, < 6 mg/kg dose of artesunate or DHA in other regimens [19]. In the multivariate model estimates are adjusted for other covariates, for time to gametocytaemia: covariates identified in the full final model presented in Table 4; for time to clearance: ACT, since no other covariates other than ACT were identified in the final model there were no multivariate models fitted within each ACT. ND, No data, HR could not be estimated as there were no patients with gametocytaemia in the under-dose/low-dose group. (DOC 86 kb)
Additional file 5: Table S4. Factors associated with the clearance of gametocytaemia after enrolment in individuals who were gametocytaemic before treatment with artemisinin combination therapy. Nobs, Number of observations; N cleared, Number of patients with day of clearance of gametocytaemia recorded. Derived haemoglobin, conversion from haematocrit: haemoglobin = (haematocrit – 5.62)/2.60 [40]; Anaemia, haemoglobin < 10 g/dL; Fever, temperature > 37.5 °C; Hyperparasitaemia, parasitaemia > 100,000 parasites per μL; weight-for-age z-score, calculated using “igrowup” package developed by WHO [41] in children < 5 years of age; Underweight, weight-for-age z-scores < –2. Proportional hazards assumption not satisfied for transmission intensity areas, Region, and artemisinin combination therapy. (DOC 59 kb)
Additional file 6: Table S5. Risk of bias in individual studies included in the analysis. ACT, Artemisinin combination therapy. 1 For trials with non-ACTs, data were only analysed for gametocytaemia on enrolment and regimens, arms, randomization, concealment of treatment, sequence generation and treatment blinding are given as not applicable (NA). 2 Includes exclusions due to study design (i.e. travellers, repeated episodes). 3 Evaluated in all patients except for exclusions due to study design or protocol violations. 4 Evaluated on all included patients treated with ACT and without gametocytaemia on enrolment. 5 Proportion of patients with time to gametocyte data available but incomplete day 28 follow-up. 6 Evaluated on all included patients with gametocytaemia on enrolment treated with ACT. 7 The sensitivity of microscopy methods was classified into one of four categories: 1 = studies in which slides were specifically read for gametocytes, reviewing at least 100 microscopic high power fields or against ≥ 1000 white blood cells (WBC); 2 = microscopists specifically instructed to record gametocytes but slides were primarily read for asexual parasites ; ≥ 100 microscopic high power fields per ≥1000 WBC were read; 3 = microscopists were specifically instructed to record gametocytes; 50–99 microscopic high power fields per 500–999 WBC were read; 4 = microscopists were not specifically instructed to record gametocytes or the number of examined high power fields was < 50 or the number of WBC was < 500. 8No data, no patients with sufficient gametocyte follow-up data that could be included in the analysis. (PDF 408 kb)
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