Abstract
Background
Gametocytes represent the infectious stage of malaria. We sought to characterize the epidemiology of Plasmodium falciparum gametocytemia and determine the prevalence, age-structure, and the viability of a predictive model for detection.
Methods
We collected data from 21 therapeutic efficacy trials of conducted in India during 2009-2010. We estimated the contribution of each age group to the potential reservoir of transmission. We built a predictive model for gametocytemia and calculated the diagnostic utility of different score cut-offs from our risk score.
Results
Gametocytemia was present in 18% (248/1,335) of patients and decreased with age. Adults constituted 43%, school-age children 45%, and children less than five years 12% of the reservoir for potential transmission. Our model retained age, sex, region, and previous antimalarial drug intake as predictors of gametocytemia. The area under the receiver operator characteristic curve was 0.76 (95%CI:0.73,0.78) and a cut-off of 14 or more on a risk score ranging from 0 to 46 provided 91% (95%CI:88,95) sensitivity and 33% (95%CI:31,36) specificity for detecting gametocytemia.
Conclusions
Gametocytemia was common in India and varied by region. Notably, adults contributed substantially to the reservoir for potential transmission. Predictive modeling to generate a clinical algorithm for detecting gametocytemia did not provide sufficient discrimination for targeting interventions.
Keywords: Malaria, Plasmodium falciparum, Disease Reservoirs, Risk, Algorithms, Epidemiology, India
Introduction
Gametocytes are the sexual stage of Plasmodia that render malaria patients infectious to mosquitoes. The proportion of patients infected by P. falciparum with gametocytemia and the duration and density of that gametocytemia vary. Although gametocytes are central to understanding transmission, few studies have tried to demarcate the infectious reservoir (1). 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 (2). However, in low and moderate transmission areas, the proportion of infectious hosts is critical to the maintenance of endemicity (2). Interventions for detecting and treating gametocytemia also differ from those used for asexual parasitemia. So an improved understanding of the epidemiology of gametocytemia opens the possibility of distinct transmission-blocking control strategies. With recent reductions in malaria transmission in many countries and increased focus on elimination worldwide, interest in gametocytemia is gaining traction.
The detection of gametocytes is constrained by their usual low density in peripheral blood. 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 (3). The low density of gametocytes coupled with inadequate laboratory standards and the heavy workloads of technicians makes the detection of gametocytemia difficult in routine settings. Recently, molecular methods that detect gametocyte-stage specific RNA transcripts have been employed in research (4–6) but these may not be feasible for routine public health use. Associations of gametocytemia with easily discerned factors including 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 1.5 million cases in 2010 (7). The reduction of transmission is a priority; most of the country having 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 (8). In addition, lower acquired immunity, more adult malaria, better access to drugs, and mixed species infections alter the epidemiology of gametocytemia in India compared to sub-Saharan Africa (9). Pre-independence Sinton and others studied the crescents of malignant tertian malaria primarily with respect to treatment and spleen size (10). Since 1990, only 4 published studies have described gametocytemia in India but these suffer from small sample sizes and limited area coverage (11–14). Most importantly, no study characterized 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.
Methods
Study sites and population
We utilized data from 22 P. falciparum therapeutic efficacy trials conducted through the National Antimalarial Drug Resistance Monitoring Network of India in 2009 and 2010 (15). The National Vector Borne Disease Control Programme and the National Institute of Malaria Research purposively selected the study sites to represent P. falciparum transmission settings across the country. We could not obtain data from one site (Gadcharoli) because gametocytemia was not recorded in case record forms and slides could not be re-examined. We included all patients eligible for the World Health Organization (WHO) therapeutic efficacy trial protocol: patients with P. falciparum monoinfection, febrile or with a history of fever, asexual parasite density greater than 500/μL and less than 100,000/μL, and willingness to consent to follow-up (16). We excluded pregnant patients and those with signs of severe malaria. The purpose of these trials was to measure the clinical and parasitological response of malaria patients when treated with first-line drugs used in national policy. The study population represents a cross-section of the patients who presented to the local clinic or were recruited through active case detection in nearby communities.
Data collection
The data collection methods have been previously described (15). Briefly, clinical and demographic information were recorded from each patient at enrollment. Patients were followed during the first 3 days of treatment (artesunate + sulfadoxine-pyrimethamine, 4mg/kg for three days + 25/1.25mg/kg as per national guidelines) and then at weekly intervals from enrollment until day 28. At each follow-up visit a physical exam was conducted and thick and thin blood smears were prepared. Using routine oil immersion reading at 1,000× on Giemsa-stained thick smears, parasites were counted until 200 white blood cells (WBC) if gametocytes and asexual stages were present or until 1,000 WBC to declare a slide negative for both. A count of 8,000 WBC/μL of blood was assumed to obtain a final density for asexual and sexual parasites. The data were double-entered into the WHO therapeutic efficacy database and blood slides were cross-checked by expert microscopists.
Case and predictor definitions
We defined gametocytemia as the presence of gametocytes in the peripheral blood smear, in any visit between day 0 and day 2 of follow-up, in a patient eligible for a therapeutic efficacy study in 2009 or 2010. The half-life of mature gametocytes is estimated at 4 to 6 days once in peripheral blood (17). Thus, patients with gametocytemia on the first day of follow-up may have been gametocytemic at enrollment. At the least, they would have benefited from a gametocytocidal intervention. Gametocytemia detected later in follow-up however, may have different origins and may not benefit from a gametocytocidal intervention given during treatment. For example, gametocytogenesis from persisting asexual stages, re-infection, or the release of sequestered developing gametocytes could explain any later onset gametocytemia.
We selected predictors associated with gametocytemia in prior literature that could be feasibly identified during routine curative care: age, sex, region, previous antimalarial drug intake, current fever, history of fever, season, and asexual parasite density (1). The recall period for history of fever was the past 48 hours and for taking an antimalarial drug was the past week. We defined current fever as an axillary temperature ≥37.5°C at the time of enrollment. We designated region using geographic clusters associated with different malaria ecotypes: western India as Gujarat, Mumbai, and Rajasthan, central India as Andhra Pradesh, Chhattisgarh, Gadcharoli, Jharkhand, Madhya Pradesh, and Orissa, and northeast India as the Assam, Meghalaya, and West Bengal sites (18,19). We classified season by month of enrollment: monsoon – June-August, post-monsoon – September-November, and winter – December and January.
Data analysis
We included in our analysis all patients who completed 2 follow-up visits after enrollment. None of the predictors, age, sex, region, previous antimalarial drug intake, current fever, history of fever, season, and asexual parasite density, had missing data. Missing gametocytemia data, due to withdrawal and loss to follow-up during the three-day treatment phase, was less than 3% of the overall sample. We calculated the prevalence of gametocytemia among levels of predictors and in the overall population. We also calculated the proportion of parasites that were gametocytes by dividing the enrollment gametocytemia by the sum of the enrollment gametocyte and asexual parasite densities. To calculate the proportion of the reservoir for potential transmission in each age category we first multiplied the geometric mean of the maximum gametocyte density, as well as the geometric mean of the average gametocyte density for sensitivity analysis, during day 0 through day 2 in the age category by the number of gametocytemic individuals in the age category. Second, we summed the gametocyte load of each age category to obtain the population gametocyte load. Third, we divided the gametocyte load of each age category by the total gametocyte load leading to the proportion of the reservoir for potential transmission represented by the age category. Finally, we also calculated the unweighted proportions by age category without including gametocyte density assuming transmission is not density dependent when gametocytemia is microscopically detectable. The use of transmission is qualified with the term “potential” as our approach ignores differences in gametocyte infectivity (due to immunity) and vector biting rates (due to uncovered body surface area, etc.) between the age groups.
We used unconditional multivariate logistic regression to build a reference predictive model between the demographic and clinical factors and gametocytemia. To account for the clustering of data in each trial, we estimated cluster robust standard errors with district as the unit (20). We estimated the crude prevalence odds ratio and its 95% confidence interval between each predictor and gametocytemia. We included all predictors associated with gametocytemia (P value < .25 to avoid the exclusion of important variables) in those bivariate analyses. We assessed collinearity between each pair of predictive factors by calculating the odds ratio. We selected among collinear variables (odds ratio > 3) based on their substantive value. We evaluated two-way interactions between all pairs of predictors and retained all product terms with P value < .25.
To simplify field use of the algorithm, we examined reduced models that had similar predictive power and adequate fit compared to the reference model. We used backwards elimination using the Wald test to remove predictors with P value < .10 starting with interaction terms and proceeding to the variable with the highest P value. We assessed the predictive power of models reduced by removing variables or collapsing across categories,through comparison of the area under the receiver operating characteristic (ROC) curve. We evaluated model fit using the Hosmer-Lemeshow test (P value > .1) (21). We then created a scoring system from the logistic model output using the regression coefficient, to preserve the multiplicative nature of the score, for each predictor in the reduced model. We multiplied the regression coefficient by 10 and rounded to the nearest integer to simplify score use (22). A final score for each patient was obtained by summing the individual scores from their predictor values. To determine the utility of the scoring system, we evaluated the sensitivity, specificity, false negatives, false positives and the area under the ROC curve of different score cut-points. We calculated false negatives using the formula (1 – sensitivity) * gametocytemia prevalence * N and false positives as (1 – specificity) * (1 – gametocytemia prevalence) * N. We also calculated the percent of the population correctly classified and the percent of the population that would be treated if scores were used to target gametocytocidal therapy. We imported the final dataset into STATA (v10) and used it for all analyses.
Study power
Assuming a gametocytemia prevalence of at least 10% and α = .25, we estimated more than 95% power in the study to detect risk factors prevalent among at least 7.5% of controls with a prevalence odds ratio of 2 or more.
Ethical clearance
The Scientific Advisory Committee of the National Institute of Malaria Research approved the original trials and the Institutional Review Board of the University of North Carolina approved the secondary analysis study.
Results
During 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, i.e. gametocytes detected in blood films on any day from day 0 through day 2, was 19% (n=248) and 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 enrollment or a history of fever prior to enrollment. Men and patients who reported yes or unknown previous antimalarial intake also had a higher prevalence of gametocytemia. The proportion of malaria patients with gametocytemia varied by region and decreased 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
| Gametocytemia |
Total |
|||
|---|---|---|---|---|
| Characteristic | n | Row % | n | Col % |
| Age (years) | ||||
| <5 | 28 | 27 | 105 | 8 |
| 5-14 | 101 | 20 | 503 | 38 |
| ≥15 | 119 | 16 | 727 | 54 |
| Sex | ||||
| Male | 157 | 20 | 773 | 58 |
| Female | 91 | 16 | 562 | 42 |
| PAI | ||||
| No | 241 | 18 | 1,310 | 98 |
| Yes/unknown | 7 | 28 | 25 | 2 |
| Region | ||||
| Central | 84 | 11 | 731 | 55 |
| Western | 153 | 41 | 371 | 28 |
| Northeast | 11 | 5 | 233 | 17 |
| Fever day 0 | ||||
| Yes | 144 | 16 | 929 | 70 |
| No | 104 | 26 | 406 | 30 |
| Season | ||||
| Monsoon | 34 | 8 | 406 | 30 |
| Post-monsoon | 174 | 28 | 620 | 46 |
| Winter | 40 | 13 | 309 | 23 |
| PD (#/μL) | ||||
| <5,000 | 118 | 25 | 480 | 36 |
| 5,000-49,999 | 117 | 16 | 746 | 56 |
| ≥50,000 | 13 | 12 | 109 | 8 |
| History of fever | ||||
| Yes | 237 | 18 | 1,317 | 99 |
| No | 11 | 61 | 18 | 1 |
Abbreviations: PAI, previous antimalarial intake; PD, asexual parasite density; POR, prevalence odds ratio; CI, confidence interval; Col, column;
The unadjusted prevalence of gametocytemia decreased from 26% (n=103) among ages 1-4 years to 14% (n=96) in those 50 years old or greater (Figure 1). Inversely, the proportion of the total parasite population consisting of gametocytes increased with age from 3% in 1-4 year olds to 8% in ages 50 or more years respectively. The average density, represented by the geometric mean of the maximum gametocytemia and mean gametocytemia during days 0 through day 2 were 117 and 66 gametocytes/μL respectively. The density of gametocytes was higher in children compared to adults (Table 2) which was similar to the trend observed with enrollment asexual parasite density (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 to 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.
Figure 1.
Prevalence of gametocytemia and the percent of total parasites that were gametocytes by age category of patients from the National Antimalarial Drug Resistance Monitoring System, India, 2009-2010
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
| Maximum |
Mean |
|||||||
|---|---|---|---|---|---|---|---|---|
| Area | Age | N | PG | GD | IR | GD | IR | UW |
| Central | <5 | 64 | 20 | 87 | 0.11 | 46 | 0.11 | 0.15 |
| 5-14 | 301 | 11 | 141 | 0.43 | 77 | 0.45 | 0.39 | |
| ≥15 | 366 | 10 | 130 | 0.46 | 65 | 0.44 | 0.45 | |
| Western | <5 | 13 | 77 | 152 | 0.09 | 24 | 0.04 | 0.07 |
| 5-14 | 136 | 49 | 122 | 0.49 | 47 | 0.50 | 0.43 | |
| ≥15 | 222 | 35 | 88 | 0.42 | 37 | 0.46 | 0.50 | |
| Northeast | <5 | 28 | 18 | 208 | 0.36 | 153 | 0.46 | 0.45 |
| 5-14 | 66 | 3 | 120 | 0.08 | 84 | 0.10 | 0.18 | |
| ≥15 | 139 | 3 | 402 | 0.56 | 182 | 0.44 | 0.36 | |
| Total | <5 | 105 | 27 | 124 | 0.12 | 69 | 0.12 | 0.11 |
| 5-14 | 503 | 20 | 128 | 0.45 | 71 | 0.45 | 0.41 | |
| ≥15 | 727 | 16 | 102 | 0.43 | 58 | 0.43 | 0.48 | |
Abbreviations: %G, prevalence of gametocytemia; GD, geometric mean gametocyte density per microliter; PR, proportion of the reservoir for potential transmission; UW, unweighted PR without accounting for gametocyte density
Age, sex, the age-sex product interaction, region, previous antimalarial intake, fever at enrollment, 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 = .32) (Table S1). Possible risk scores ranged from 0 to 65 although the minimum and maximum observed score were 0 and 45 respectively. The median risk score was 14 (interquartile range: 10, 28). Residing in the western region was the highest scoring predictor with 28 points while age 5-14 years and male sex were the lowest scoring predictors receiving 4 points each. No cut-off point yielded a sensitivity greater than 75% and a specificity lower than 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 2). For comparison, the AUC of the model using all predictors was 0.79 with 2-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 |
Percent treated |
|---|---|---|---|---|---|
| 0 | 1.00 | 0.00 | 0 | 1087 | 100 |
| 4 | 1.00 | 0.03 | 1 | 1059 | 98 |
| 8 | 0.98 | 0.15 | 5 | 924 | 87 |
| 10 | 0.98 | 0.18 | 6 | 888 | 85 |
| 13 | 0.92 | 0.32 | 20 | 738 | 72 |
| 14 | 0.91 | 0.33 | 22 | 723 | 71 |
| 15 | 0.76 | 0.59 | 59 | 441 | 47 |
| 17 | 0.76 | 0.60 | 60 | 435 | 47 |
| 18 | 0.75 | 0.61 | 63 | 427 | 46 |
| 19 | 0.67 | 0.75 | 81 | 277 | 33 |
| 23 | 0.67 | 0.75 | 82 | 270 | 33 |
| 27 | 0.63 | 0.78 | 91 | 242 | 30 |
| 28 | 0.62 | 0.80 | 95 | 219 | 28 |
| 32 | 0.57 | 0.85 | 107 | 165 | 23 |
| 33 | 0.19 | 0.97 | 200 | 32 | 6 |
| 36 | 0.19 | 0.97 | 200 | 31 | 6 |
| 37 | 0.06 | 1.00 | 233 | 5 | 1 |
| 41 | 0.05 | 1.00 | 235 | 3 | 1 |
| 45 | 0.02 | 1.00 | 244 | 2 | 0 |
| 46 | 0.00 | 1.00 | 248 | 0 | 0 |
Abbreviations: FN, false negative; FP, false positive
Figure 2.
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
Discussion
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 are 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 (19).
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 (23). 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 small denominator as the asexual parasite density decreased with age (data not shown). These results underscore the need to examine absolute measures of frequency rather than relative measures to inform public health conclusions. 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 (24).
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 (25). Age, sex, and region would, presumably, be associated with gametocytemia through one of two mechanisms: 1) immunity, primarily determined by transmission intensity and the exposure of specific risk-groups and 2) treatment access or treatment seeking behavior as gametocytemia increases with longer durations of infection (26). Parasite density and fever at the time of enrollment, 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 which 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-point yielded an acceptable sensitivity (>95%) and specificity (>90%) according to criteria developed for malaria rapid diagnostic tests (27,28). 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. Alternative 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 of false-positives is a benefit compared to 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. The poor prospects for future improvement in model performance coupled with the likelihood of considerable indirect costs of implementation suggests 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 (17,29). Interpreting the functional relevance of submicroscopic gametocytemia is also difficult. While sub-microscopic density infections can infect mosquitoes, the probability of infection, the proportion of mosquitoes infected, and the density of infection in mosquitoes is positively correlated with gametocyte density (30,31). Next, we completed enrollment at each site over 1 to 2 months which restricted the analysis of seasonal trends of gametocytemia. Our population cross-section was also not representative of the population at-risk. However, it is representative of the population encountered by the control programme through active and passive case detection. Thus, we did not assess the contribution of asymptomatic carriers to transmission but, at present, there may be no valid means to do so (32). The population was appropriate for our goal - to observe the population amenable to current and future interventions. Finally, we used the presence of gametocytes in peripheral blood as a proxy for infectiousness. In reality, infectiousness 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 generalizable results.
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 modeling 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 prioritize the measurement of the asymptomatic reservoir, conduct longitudinal assessments, and validate gametocytemia as an indicator for treatment access.
Supplementary Material
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 | 1.00 a | |||
| ≥15, female | 1.00 | |||||
| Sex | ||||||
| Male | -- | 1.49 | 1.06, 2.10 | 0.40 | 4 | |
| Female | 1.00 | |||||
| PAI | ||||||
| No | 1.00 | |||||
| Yes/unknown | 1.69 | 1.00, 2.87 | 1.67 | 0.99, 2.81 | 0.51 | 5 |
| Region | ||||||
| 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 | |||||
| PD (#/μL) | ||||||
| <5,000 | 1.54 | 0.74, 3.24 | -- | |||
| 5,000-49,999 | 1.44 | 0.72, 2.88 | ||||
| ≥50,000 | 1.00 | |||||
for the entire age category
Abbreviations: PAI, previous antimalarial intake; PD, asexual parasite density; OR, odds ratio; CI, confidence interval
Acknowledgements
We are grateful to the Director General, Indian Council of Medical Research for his support of this work. NKS was supported by a fellowship from the Paul and Daisy Soros Foundation and National Institutes of Health Medical Scientist Training Program grant GM008719.
NKS, SRM, NV, and NM designed the study and BS, AA, NV, NM collected the original data. NKS, CP, JJ, AS, SRM, PMD, and NM analysed the data and NKS wrote the first draft of the manuscript and all authors wrote the final report.
Footnotes
Conflict of interest:
None declared
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