MIP analyses of 2013-2014 samples
Following MIPWrangler processing, a 250 bp paired end MiSeq run following a single MIP capture yielded 9 million paired end reads and 4 million UMIs. Sequencing was successful for 514/552 children. The geolocation data indicates that these 514 children live throughout the DRC (Figure 1). Complete pfcrt SNP data was available for 513 children, and 307 had data available across all pfcrt and pfdhps loci of interest.
The results of THE REAL McCOIL analysis estimated an average complexity of infection (COI) of 2 (range = 1-17). Of children with complete genotyping data, one-hundred and eight (35% of the total) had polyclonal infections, compared with 20% of infections that were polyclonal in 2007 (X2 = 7.28, df = 1, p < 0.01). However, this is likely an underestimate of the true number of polyclonal infections as we are only looking at three loci.
Frequency of pfdhps and pfcrt variants over time
The overall proportion of pfdhps mutations remained relatively steady from 2007 to 2013, (80% [95% CI = 72-86%] vs 86% [95% CI = 83-89%], Figure 2). However, the proportions of K540E mutations increased significantly from 17% in 2007 (95% CI = 11-24%) in 2007 to 41% (95% CI = 36-47%) in 2013 (X2 = 25.57, df = 1, p<0.01). A581G mutations also increased significantly between years, from 3% (95% CI = 1-8%) in 2007 to 18% (95% CI = 14-23%) in 2013 (X2 = 15.27, df = 1, p <0.01). Only one individual in 2007 had a single A581G mutation, in all other cases, in both years, A581G was only found in the presence of a K540E mutation. Thus, the proportion of double K540E/A581G mutants also increased significantly across years, from 2% (95% CI = 1-7%) in 2007 to 18% (95% CI = 14-23%) in 2013 (X2 = 19.27, df = 1, p <0.001).
Amongst monoclonal infections, there were similar patterns of allele frequencies over time. The proportions of infections carrying any of the three pfdhps SNPs increased slightly; 62% (95% CI = 51-73%) in 2007 versus 73% (95% CI = 66-79%) in 2013 (X2 = 2.71, df = 1, p = 0.10). However, the proportion of double K540E and A581G mutant parasites increased from 4% (95% CI = 1-8%) in 2007 to 12% (95% CI = 7-17%) in 2013 (X2 = 3.03, df = 1, p = 0.08).
The proportion of pfcrt CVIET haplotypes did not change significantly from 2007 (58% [95% CI = 50-65%] to 2013 (54% [95% CI = 49-58%]; X2 = 0.80, df = 1, p = 0.37). No parasites harbored the SVMNT haplotype. Among monoclonal infections, the proportion of pfcrt CVIET haplotypes also remained steady; 55% (95% CI = 46-63%) in 2007 and 56% (95% CI = 51-61%) in 2013 (X2 = 0.012, df = 1, p = 0.91).
Risk factor analysis
Complete pfdhps and DHS covariate data were available for 492 individuals from both the 2007 and 2013-2014 studies; complete pfcrt and DHS covariate data was available for 675 individuals. Reported antimalarial use was low, with a cluster average of only 12% of pregnant women receiving SP in 2007 and 24% in 2013. In 2007, an average of only 4% of children per cluster reporting a cough or fever received amodiaquine, and only about 1% in 2013. A summary of the cluster and individual level characteristics by pfdhps and pfcrt genotype is available in Table 1.
Table 1: Individual and cluster level characteristics of all study participants, stratified by Pfdhps and Pfcrt genotype
|
Pfdhps
|
Pfcrt
|
|
Wildtype
(N = 81)
|
Any pfdhps mutation (N = 434)
|
P-value***
|
Wildtype
(N = 306)
|
CVIET haplotype
(N = 369)
|
P-value***
|
Malaria prevalence (SD)
|
59.3 (20.4)
|
58.9 (21.8)
|
0.872
|
60.04 (21.74)
|
57.42 (22.24)
|
0.125
|
Anti-malarial use during pregnancy* (SD)
|
16.7 (16.6)
|
22.2 (18.1)
|
0.011
|
2.0 (6.2)
|
1.9 (6.0)
|
0.955
|
Anti-malarial use amongst children (SD)**
|
3.0 (6.4)
|
2.0 (5.6)
|
0.126
|
1.7 (4.4)
|
1.6 (4.0)
|
0.745
|
Mean DHS Cluster size (SD)
|
17.9 (18.0)
|
19.3 (18.8)
|
0.652
|
17.8 (19.07)
|
20.0 (22.14)
|
0.266
|
% without education (SD)
|
32.7 (23.48)
|
23.1 (21.5)
|
<0.001
|
28.8 (24.41)
|
22.0 (20.16)
|
<0.001
|
% in lowest wealth category (SD)
|
30.2 (23.0)
|
21.4 (22.5)
|
0.001
|
27.2 (22.5)
|
20.5 (22.5)
|
<0.001
|
Number living in urban area (%)
|
28 (34.6)
|
154 (35.4)
|
0.975
|
90 (29.4)
|
135 (36.6)
|
0.059
|
Individual covariates:
|
Number female (%)
|
41 (50.6)
|
228 (52.5)
|
0.845
|
153 (50.0)
|
192 (52.0)
|
0.654
|
Median Individual Wealth Index (IQR)
|
2 (1-3)
|
3 (1-4)
|
0.015
|
2 (1-3)
|
3 (2-4)
|
<0.001
|
*Percentage of pregnant women reporting drug use; SP use is described by pfdhps status and chloroquine use by pfcrt status
** Percentage of children with a cough or fever that received SP or chloroquine; SP use is described by pfdhps status and chloroquine use by pfcrt status
*** p-values for tests conducted for comparisons between wildtype and mutant groups (Chi-squared tests for categorical data and t-tests for continuous data)
The mixed-effects model identified several risk factors for pfdhps mutations and the pfcrt CVIET haplotype (Table 2). Increasing cluster-level use of SP was a risk factor for carrying a K540E mutation (PR = 1.14, 95% CI = 1.09 – 1.20, p <0.01) as was increasing cluster prevalence of P. falciparum infections (PR = 1.11, 95% CI = 1.06 – 1.17, p = 0.02). The results from the pfcrt model indicated an inverse relationship between the prevalence of mutations and the proportion of uneducated individuals (PR = 0.92, 95% CI = 0.90 – 0.95, p < 0.01). Education may be a proxy for access to medications.
Table 2: Risk factors identified from final backwards selection multivariate risk factor model.
Covariate
|
Prevalence Ratio (95% CI)
|
p-value
|
|
Pfdhps K540E
|
|
10% increase in malaria prevalence
|
1.11 (1.06 – 1.17)
|
0.024
|
10% increase in cluster SP use*
|
1.14 (1.09 – 1.20)
|
<0.01
|
|
Pfcrt CVIET
|
|
10% increase in lowest education category
|
0.92 (0.90 – 0.95)
|
< 0.01
|
* reported SP use amongst pregnant women
Increasing cluster level SP use amongst pregnant women and malaria prevalence were both identified as risk factors for carrying the K540E mutation (including those with the A581G mutation also), while education was the only risk factor identified for carrying the CVIET haplotype.
Results from the secondary univariate models matched those from the multivariate models (Supplemental Table 2). Like the multivariate model, the univariate models did not identify any risk factors for carrying any pfdhps mutation. The univariate models of K540E identified both increasing SP use and increasing cluster P.f. prevalence as risk factors, though the p-value for prevalence was not significant at the 5% level. Like the multivariate model, the univariate models of pfcrt identified only increasing cluster level education as a risk factor for the CVIET haplotype. Similarly, increasing cluster level proportion of poor individuals showed a protective effect against the CVIET haplotype, though the association had a p-value that was not significant at the 5% level. Full results for the univariate models are available in Supplemental Table 1.
Spatial-temporal prediction maps:
The prediction maps generated from the logistic Gaussian model indicate that the allele frequency distribution of the A437G mutation shifted range slightly between 2007 and 2013, decreasing in the east and west of the country but increasing in the south (Figure 3). The results also demonstrate the geographic spread of both the K540E and A581G mutations from east to west, showing both an increase in the frequency of each mutation and a geographic expansion, indicated by the shift in the 10% contour lines (marked in black). Pfcrt results demonstrate that there has been no significant change in the spatial distribution of the CVIET haplotype between 2007 and 2013; the prevalence of the haplotype is highest across the central part of DRC. The wide 95% credible intervals on posterior parameter weights indicate that there is large uncertainty as to which components are driving the signal (Supplementary Figure 1). Similarly, the posterior error maps show that there is large uncertainty in the predicted allele frequency at most points in space (Supplementary Figure 2). Hence, it is important to recognize that the maps in Figure 3 show only the average prediction, and there are alternative maps that are plausible under the posterior distribution. However the general patterns described above, such as the east-west expansion of K540E and A581G mutations, remain consistent over the majority of posterior draws, and therefore are well-supported in spite of uncertainty in any specific prediction.