The epidemiology of Plasmodium vivax among adults in the Democratic Republic of the Congo

Abstract

Reports of P. vivax infections among Duffy-negative hosts have accumulated throughout sub-Saharan Africa. Despite this growing body of evidence, no nationally representative epidemiological surveys of P. vivax in sub-Saharan Africa have been performed. To overcome this gap in knowledge, we screened over 17,000 adults in the Democratic Republic of the Congo (DRC) for P. vivax using samples from the 2013-2014 Demographic Health Survey. Overall, we found a 2.97% (95% CI: 2.28%, 3.65%) prevalence of P. vivax infections across the DRC. Infections were associated with few risk-factors and demonstrated a relatively flat distribution of prevalence across space with focal regions of relatively higher prevalence in the north and northeast. Mitochondrial genomes suggested that DRC P. vivax were distinct from circulating non-human ape strains and an ancestral European P. vivax strain, and instead may be part of a separate contemporary clade. Our findings suggest P. vivax is diffusely spread across the DRC at a low prevalence, which may be associated with long-term carriage of low parasitemia, frequent relapses, or a general pool of infections with limited forward propagation.

Fig. 1. Overview of study approach and inclusion.

The conceptual diagram (left) describes the study approach to identify “who” was being infected by P. vivax, “where” P. vivax infections were occurring, and the potential source, or “origin”, of these infections. Risk factors identified in the “who” analysis, were considered in the spatial models. Genetic distance analyses were restricted to the mitochondrial genome (mtDNA) and few DRC samples (n = 3). As a result, we limited this analysis to three broad questions: are the DRC samples most similar to (1) non-human apes mitochondrial strains; (2) contemporary P. vivax mitochondrial strains; or (3) an ancestral P. vivax mitochondrial strain. The final data set (right) consisted of 467 P. vivax infections among 15,574 individuals across 489 clusters, prior to the application of the Demographic Health Survey (DHS) sampling weights. Specifically, of the 18,257 Demographic Health Survey (DHS) records that had a dried blood spot, 17,972 samples were successfully shipped to the University of North Carolina for processing. Of these 17,972 samples, a small portion were lost due to barcoding errors, while the remaining 17,934 (99.79%) were successfully linked to the 2013–2014 DRC DHS survey. Of these 17,934 samples, 169 samples failed to amplify human beta-tubulin (i.e. positive control), 1402 individuals had missing geospatial data, 484 individuals were classified as de facto (visitors rather than household members), 535 individuals had zero-weighted DHS sampling weights, and 17 individuals had incomplete data and were excluded from analysis (further details in Supplementary Materials: Study Population and Data Sources). In total, our final dataset consisted of 15,574 individuals across 489 clusters. Similarly, although we identified a total of 579 P. vivax infections, 112 infections were lost during the aforementioned processing steps, which resulted in a final, unweighted count of 467 P. vivax infections.

Fig. 2. Inverse probability weight adjusted prevalence odds ratios for expected malaria risk factors.

Risk factor point estimates and associated 95% confidence intervals are displayed for P. vivax and P. falciparum, respectively. Risk factors associated with P. vivax infection included precipitation and distance from healthcare facilities, while risk factors were associated with P. falciparum infection, included: urbanicity, housing materials, ITN use, altitude, temperature, education, wealth, age, and biological sex. The unadjusted pORs effect estimates and confidence intervals as well as the IPW-pORs are provided in Supplementary Table 7 for reference. Hospital Dist distance to healthcare facilities, Water Dist. distance to water, Trad. traditional, Num. House Members number of household members, ITN insecticide-treated net.

Fig. 3. Non-parametric risk factor analysis and spatial clustering of P. vivax Infections.

A Composition of P. vivax and P. falciparum co-infections. The expected versus observed composition of P. vivax and P. falciparum infections were explored using a multinomial probability likelihood model testing for independent acquisition of each species. To incorporate DHS sample weights, infections were rounded to the nearest integer. The plot shows the expected distribution for individuals without infection (“noinfxn”), P. falciparum infections (“pf”), P. vivax infections (“pv”), and P. falciparum–P. vivax co-infections (“pf/pv”). The blue shading indicates the 95% bootstrapped interval and the red-dotted line indicates the observed number of cases for each infection category. Overall, the observed distribution of each infection composition was explained well by each species occurring independently, which suggests that there were no interspecies infection synergism or antagonism. B Overlap with non-human ape habitats and positive P. vivax clusters. Overall, P. vivax prevalence did not appear to be associated with non-human ape habitat distribution (prevalence displayed as a proportion). This lack of a P. vivax-non-human ape association was recapitulated with permutation testing. Clusters with P. vivax infections are shaded on a blue–red spectrum with respect to the cluster-level prevalence, with the distribution of each non-human ape habitat is indicated in shades of green. C Correlation of P. vivax cluster prevalence with airport distance. The point estimate and 95% confidence interval for each cluster is shown as a point-range. Standard errors for the confidence intervals were calculated with the binomial exact method: to incorporate DHS weights, we rounded to the nearest integer. Airport distance was used as a proxy for the potentiality of P. vivax flight importation risk, or “airport malaria” risk. Overall, there did not appear to be a correlation between this variable and P. vivax prevalence (energy-correlation: 0.07). D Cluster detection of P. vivax infections. P. vivax infections appeared to be clustered in the northern and northeastern regions of the DRC with a small cluster also indicated in the center of the country.

Fig. 4. The distribution of P. vivax infections across the Democratic Republic of the Congo.

For each map, the prevalence (as a proportion) is indicated along a blue–red spectrum (prevalence ranges and color scales differ between A–C). A Each cluster that contained a P. vivax infection is indicated along the blue–red prevalence spectrum, while clusters that lacked P. vivax infections are indicated with white X-marks. The scale of the cluster-mark reflects the cluster sample size (weighted-range: 1.41–269.63). P. vivax infections appeared to be diffusely spread throughout the country with cluster prevalences ranging from 0 to 46.15%. The cluster with the greatest prevalence, located in the Southeast near the Angola border, contained 6/13 infections. However, when DHS sampling weights were considered, this cluster was shrunk considerably. In addition, several surrounding clusters had no infections, resulting in less evidence for a focal region of high prevalence on spatial modeling: as such, its point-prevalence should not be overinterpreted. B The posterior mean prevalence estimates for the province-level model, which show that P. vivax infections appeared to be slightly more common in the north and northeast. C The posterior mean prevalence estimates that were interpolated across the DRC from the cluster-level model indicated a diffuse, low-level of P. vivax prevalence across the country with a few regions of higher prevalence. These focal regions of higher prevalence appeared to be concentrated in the northern and northeastern regions and to a lesser degree in the South. However, when considering the cluster-level model prevalences on a broader scale, ~99% of predicted prevalences are below the national average, which suggests a diffuse pattern of low prevalence across the DRC.

Fig. 5. P. vivax mitochondrial Hamming’s distances and minimum-spanning network.

Comparison of DRC P. vivax with over 700 globally sourced P. vivax isolates yielded 142 within-country unique consensus haplotypes and 102 overall unique consensus haplotypes, as duplicates existed within countries. In the instance of the DRC consensus haplotypes (n = 2/3), one isolate was identical except for ten missing sites. As a result, the two DRC consensus haplotypes were reduced to a single sequence and used in the above figures. A The DRC consensus haplotype is depicted in the center (red asterisk) and orbited by the other 140 consensus haplotypes. Distances from the center are determined by the number of base-pair differences while the relative position along the orbit is arbitrary and clustered only for visualization aid. Isolates from the Americas are colored in shades of blue and include Brazil (BR), Colombia (CO), Mexico (MX), and Peru (PE) Isolates from Asia are indicated in shades of yellow–green and include China (CN), Indonesia (ID), Cambodia (KH), Laos (LA), Myanmar (MM), Malaysia (MY), Papua New Guinea (PG), Thailand (TH), and Vietnam (VN). India (IN) and Sri Lanka (LK) are indicated in shades of orange, while Ethiopia (ET) and Madagascar (MG) are indicated in shades of purple. Finally, the Democratic Republic of the Congo (DRC) is shown in red and non-human apes (NHA) are shown in magenta. The historical sample from the Ebro Delta in Spain dating to 1944 (Ebro1944) is colored in brown. B A representative consensus haplotype from each country and both unique non-human ape sequences were selected as input into the minimum-spanning tree. Overall, there are similarities among the haplotypes from both the Americas and Asia, as well as the DRC consensus haplotype, although to a lesser degree. In terms of base-pair differences (Hamming’s distance), the DRC mitochondrial consensus haplotype differed by three and four bases to the Ebro1944 and the non-human ape consensus haplotypes, respectively. All unique consensus haplotypes with respect to country of origin are provided for comparison and context (Supplementary Fig. 11).