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Gametocytes are the sexual stage of Plasmodia that render the human host infectious to mosquitoes. Although gametocytes are central to understanding transmission, few studies have tried to demarcate the infectious reservoir (Bousema & Drakeley 2011). This may be because the infectious reservoir is not an important determinant of the intensity of transmission relative to high vectorial capacity in high transmission areas (Macdonald 1956). However, in low and moderate transmission areas, the proportion of infectious hosts is critical to the maintenance of endemicity (Macdonald 1956). Interventions for detecting and treating gametocytemia also differ from those used for asexual parasitaemia. So an improved understanding of the epidemiology of gametocytemia opens the possibility of distinct transmission-blocking control strategies.
Gametocyte density is generally lower than that of asexual parasites (typically less than 5% of the total parasite population), and levels below the detection limit of microscopy can infect mosquitoes (Taylor & Read 1997). The low density of gametocytes coupled with inadequate laboratory standards and the heavy workloads of technicians make the detection of gametocytemia difficult in routine settings. Recently, molecular methods that detect gametocyte stage-specific RNA transcripts have been employed in research (Schneider et al. 2004; Mlambo et al. 2008; Karl et al. 2009), but these may not be feasible for routine public health use. Associations of gametocytemia with easily discerned factors such as age, season and symptoms such as fever at the time of presentation could provide an alternative strategy for targeting gametocytocidal interventions. Clinical algorithms for predicting gametocytemia among diagnosed malaria patients could help improve its detection.
The control of malaria is a major challenge for India, which reported 1.1 million cases in 2012 (NVBDCP, National Vector Borne Disease Control Programme). The reduction in transmission is a priority; most of the countries have low or moderate malaria endemicity. The transmission of malaria in India is arguably the most complex in the world given the large geographic area, the presence of both major parasite species, a wide range of ecotypes and vectors and the enormous population (Kumar et al. 2007). In addition, lower acquired immunity, more adult malaria, better access to drugs, and mixed species infections alter the epidemiology of gametocytemia in India compared with sub-Saharan Africa (Singh et al. 2009). Sinton and others studied crescents primarily with respect to treatment and spleen size in the pre-independence era (Sinton 1926). Since 1990, only 4 published studies have described gametocytemia in India, but these had small sample sizes and limited coverage (Rajagopalan et al. 1990; Mohapatra et al. 1992, 1998; Kar et al. 2009). Most importantly, no study characterised the subpopulation with gametocytemia.
The goal of this study was to describe the epidemiology of P. falciparum gametocytemia in India and determine whether a clinical predictive model could improve its detection.
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In 2009 and 2010, 1 372 patients with P. falciparum malaria were recruited into therapeutic efficacy trials of antimalarial drugs. Among these patients, 19 voluntarily withdrew, 3 received outside treatment, 2 contracted other illnesses and 9 were lost to follow-up. After removing 4 patients who were missing gametocytemia data, our complete case population was 1 335. The majority of the study population was, independently, adult, male, from central India, and enrolled in the post-monsoon (Table 1). The proportion of patients with gametocytemia on day 0, day 1 and day 2 was 13% (n = 179), 15% (n = 201) and 15% (n = 203), respectively. Overall, the prevalence of gametocytemia, that is, gametocytes detected in blood films on any day from day 0 through day 2 was 19% (n = 248); this varied in relation to demographic and clinical classifications (Table 1). In the unadjusted bivariate associations, gametocytemia decreased with both increasing age and parasite density categories, while it was lower among those without fever at enrolment or a history of fever prior to enrolment. Men and patients who reported fever or unknown previous antimalarial intake also had a higher prevalence of gametocytemia. The proportion of malaria patients with gametocytemia varied by region and fell along a western to eastern India axis.
Table 1. Prevalence of gametocytemia in relation to demographic and clinical factors of patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010
| n ||Row%|| n ||Col%|
|Fever day 0|
|5 000–49 999||117||16||746||56|
|History of fever|
The unadjusted prevalence of gametocytemia ranged from 26% (n = 103) among ages 1–4 years to 14% (n = 96) in those aged 50 years or older (Figure 2). Inversely, the proportion of the total parasite population consisting of gametocytes increased with age from 3% in 1–4-year olds to 8% in people aged 50 or older. The average densities, represented by the geometric mean of the maximum gametocytemia and mean gametocytemia from day 0 through day 2, were 117 and 66 gametocytes/μl, respectively. The density of gametocytes was higher in children than adults (Table 2), which was similar to the trend observed with asexual parasite density at enrolment (data not shown). In unadjusted analysis, gametocyte densities were similar in western and central India but higher in northeast India in all age categories (Table 2). Adults (age 15 years or more), who were 54% of the study population and among whom 16% carried gametocytes, constituted approximately 44% of the reservoir for potential transmission. School-age children (age 5–15 years), who were 38% of the study population and among whom 20% carried gametocytes, constituted approximately 44% of the reservoir for potential transmission. Young children (age less than 5 years), who were 8% of the study population and among who 27% carried gametocytes, constituted approximately 12% of the reservoir for potential transmission. These estimates did not differ by region except for northeast India where the relative contributions of school-age children and younger children were reversed compared with other regions. These estimates also did not differ whether the maximum or mean gametocyte density was used. Assuming transmission is not gametocyte density dependent, the unweighted contribution for adults towards potential transmission increased in the total population (Table 2).
Table 2. The contribution of age groups to the reservoir for potential transmission using the unweighted, maximum, or mean, day 0 to 2 gametocyte density in patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010
Table 3. Adjusted prevalence odds ratios in the reference and final models, regression coefficients and risk scores for predicting gametocytemia in patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010
|Variable||Reference model OR (95%CI) AUC = 0.766||Final model OR (95%CI) AUC = 0.762||Logistic regression coefficient||Risk score|
|Age (years), Sex|
|<5, male||2.34||1.03, 5.28||3.88a||2.13, 7.06||1.36||14|
|<5, female||6.88||3.05, 15.6|| || || || |
|5–14, male||1.30||0.79, 2.14||1.51a||0.96, 2.36||0.41||4|
|5–14, female||2.07||1.18, 3.64|| || || || |
|≥15, male||2.07||1.26, 3.40||1.00a|| || || |
|≥15, female||1.00|| || || || || |
|Male||–|| ||1.49||1.06, 2.10||0.40||4|
|Female|| || ||1.00|| || || |
|No||1.00|| || || || || |
|Yes/unknown||1.69||1.00, 2.87||1.67||0.99, 2.81||0.51||5|
|Central||2.98||1.69, 5.28||2.77||1.63, 4.69||1.02||10|
|Western||16.3||9.44, 28.1||17.1||9.98, 29.3||2.84||28|
|Northeast||1.00|| || || || || |
|Fever day 0|
|Yes||1.30||0.93, 1.81||–|| || || |
|No||1.00|| || || || || |
|<5 000||1.54||0.74, 3.24||–|| || || |
|5 000–49 999||1.440.72, 2.88|| || || || |
|≥50 000||1.00|| || || || || |
Figure 2. Prevalence of gametocytemia and the per cent of total parasites that were gametocytes by age category of patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010.
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Age, sex, the age–sex product interaction, region, previous antimalarial intake, fever at enrolment and parasite density category remained in the reference model (Table 2). In the simplified model, age, sex, region, and previous antimalarial intake alone provided similar predictive ability and model fit (P value = 0.32) (Table S1). Possible risk scores ranged from 0 to 65 although the minimum and maximum scores were 0 and 45. The median risk score was 14 (interquartile range: 10, 28). Residing in the western region was the highest scoring predictor with 28 points; age 5–14 years and male sex were the lowest scoring predictors with 4 points each (Table 3). No cut-off point yielded a sensitivity above 75% and a specificity below 75%. For example, if the goal of a control programme was to treat at least 90% of gametocyte carriers, a risk score of 14 or more provided 91% (95%CI: 88, 95) sensitivity and 33% (95%CI: 31, 36) specificity (Table 4). Applied in our study population of 1 335 patients of whom 248 were gametocytemic, 71% of the population would receive treatment with 22 false negatives and 723 false positives. The area under the ROC curve for predicting gametocytemia was 0.76 (95%CI: 0.73, 0.80) (Figure 3). For comparison, the AUC of the model using all predictors was 0.79 with two-way interactions and 0.82 with all possible interactions.
Table 4. Performance of different risk score cut-offs for detecting gametocytemia in patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010
|Score ≥||Sensitivity||Specificity||Number of FN||Number of FP||Per cent treated|
Figure 3. Receiver operator characteristics curve with risk score cut-points for predicting gametocytemia in patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009–2010.
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We observed a high prevalence of gametocytemia in India, and adults constituted a substantial proportion of the reservoir for potential transmission in our sampled population. While a predictive model for gametocytemia identified several easily screened risk factors, the ability of the clinical algorithm to sensitively and specifically detect gametocytemia was low.
We observed a higher prevalence of gametocytemia than previously reported although there were large, albeit unadjusted, regional differences. Gametocytemia was most common in western India, which is composed of two distinct ecotypes: rural, low-transmission malaria, and urban slum and migrant-labour associated malaria. Both ecotypes could be associated with a high risk of gametocytemia through low immunity and/or poor access to quality care, especially for migrants. Central India and northeast India include higher transmission areas with forest-associated malaria albeit through different vectors. In the northeast, access to care is better and the use of artemisinin combination therapies began earlier, which may explain the lower prevalence of gametocytemia relative to central India (Sonal et al. 2010).
The reservoir for potential transmission in our study population was distributed throughout the age spectrum. Traditionally, children were thought to be the primary reservoir for transmission (Drakeley et al. 2006). Young and school-age children did contribute to the reservoir for potential transmission disproportionate to their population because of their higher prevalence of gametocytemia and higher gametocyte densities. Still, adults constituted nearly half of the potential reservoir of infection simply due to their larger population. In adults, a higher proportion of the parasite population was gametocytes although this was largely due to a smaller denominator as the asexual parasite density decreased with age (data not shown). The contribution of adults in malaria transmission may be higher than the potential reservoir estimated by us when accounting for other factors such as their larger surface area for biting (Port et al. 1980). These results underscore the need to examine absolute measures of frequency rather than relative measures to inform public health conclusions. While children may be at higher risk of infection and may individually contribute more towards transmission, most malaria patients in India and other low to moderate transmission settings are adults. Many malaria intervention strategies, such as school-based screening or preferential net distribution in mass campaigns, target children. Given the vulnerability of children, this focus is not inappropriate, but without addressing the burden in adults, we cannot maximise malaria control.
Four variables (region, age, sex and previous antimalarial drug intake) predicted gametocytemia in our model. Previous intake of non-gametocytocidal antimalarial drugs is thought to induce ‘stress’ on the parasite, which activates gametocytogenesis (Butcher 1997). Age, sex and region would, presumably, be associated with gametocytemia through one of two mechanisms: (i) immunity, primarily determined by transmission intensity and the exposure of specific risk groups, and (ii) treatment access or treatment seeking behaviour as gametocytemia increases with longer durations of infection (Doolan et al. 2009). Parasite density and fever at the time of enrolment, which were removed from the final model, are also distal effects of, rather than proximal markers of, the aforementioned mechanisms, which may explain their inability to predict gametocytemia in our model.
The use of a predictive model to detect gametocytemia generated an algorithm that ranked a positive case selected at random from our study population higher than a negative case selected at random 76% of the time (AUC). No cut-off point yielded an acceptable sensitivity (>95%) and specificity (>90%) according to criteria developed for malaria rapid diagnostic tests (Bell & Peeling 2006; WHO, Publications on rapid diagnostic tests). As an illustration, if we selected 90% sensitivity or more as a desirable criterion, we could only achieve 33% specificity. We also did not validate our algorithm on independent data, and hence, its performance in our study population could be considered a best-case scenario. Other strategies for selecting a predictive model are unlikely to produce better clinical algorithms with the available data as the AUC of the final model was close to the AUC of the saturated model. Alternative data, however, could produce better clinical algorithms if other easily measured predictors existed. While our performance would not suffice for a disease diagnostic, one could argue the direct costs of using an algorithmic approach are non-existent; so any reduction in false positives is a benefit compared with universal treatment. However, substantial indirect costs may exist. Considerations of implementing any clinical algorithm must account for the operational challenges in individual level targeting including the costs of training, the time required for patient assessment and increased programme complexity. Poor prospects for future improvement in model performance coupled with the likelihood of considerable indirect costs of implementation suggest that a clinical predictive approach for targeting gametocytemia is not viable.
Our study had several limitations. We used microscopy for the measurement of gametocytemia, which is less sensitive than molecular techniques. However, in studies comparing the two methods, the latter increased the magnitude of gametocytemia but did not alter its age structure, circulation time and other trends (Bousema et al. 2010; Ouédraogo et al. 2010). Interpreting the functional relevance of submicroscopic gametocytemia is also difficult. While submicroscopic density infections can infect mosquitoes, the probability of infection, the proportion of mosquitoes infected and the density of infection in mosquitoes are positively correlated with gametocyte density (Schneider et al. 2007; Ouédraogo et al. 2009). Next, we completed enrolment at each site over 1–2 months, which restricted the analysis of seasonal trends of gametocytemia. Our population cross-section was also not representative of the population at risk. It was representative of the population encountered by the control programme through active and passive case detection. Thus, we could not assess the contribution of asymptomatic carriers to transmission in thus study but, at present, there may also be no valid means to do so (Laishram et al. 2012). We also were not able to assess gametocytemia in patients with mixed infections. Interspecies dynamics may alter gametocytogenesis; for example, epidemiological evidence for P. falciparum gametocytemia as an indicator for occult mixed infection exists (Lin et al. 2011). Finally, we used the presence of gametocytes in peripheral blood as a proxy for infectiousness. In reality, infectivity is modified by a number of factors; it can be assessed most directly through membrane-feeding experiments, but these are labour intensive and would not be possible in a large survey needed for generalisable results (Awono-Ambene et al. 2001).
In a population of P. falciparum patients from a national network of sentinel sites, we conclude gametocytemia was common, adults were an important component of the reservoir for potential transmission, and clinical algorithms based on predictive modelling were not effective for the detection of gametocytemia. Due to the wide age distribution of gametocytemia, and the difficulty of targeting using clinical prediction, we recommend universal application, if any, of gametocytocidal interventions among confirmed malaria patients. Future research on gametocytemia should prioritise the measurement of the asymptomatic reservoir, conduct longitudinal assessments and validate gametocytemia as an indicator for treatment access.