Comparison of PfHRP-2/pLDH ELISA, qPCR and microscopy for the detection of Plasmodium events and prediction of sick visits during a malaria vaccine study.

BACKGROUND: Compared to expert malaria microscopy, malaria biomarkers such as Plasmodium falciparum histidine rich protein-2 (PfHRP-2), and PCR provide superior analytical sensitivity and specificity for quantifying malaria parasites infections. This study reports on parasite prevalence, sick visits parasite density and species composition by different diagnostic methods during a phase-I malaria vaccine trial. METHODS: Blood samples for microscopy, PfHRP-2 and Plasmodium lactate dehydrogenase (pLDH) ELISAs and real time quantitative PCR (qPCR) were collected during scheduled (n = 298) or sick visits (n = 38) from 30 adults participating in a 112-day vaccine trial. The four methods were used to assess parasite prevalence, as well as parasite density over a 42-day period for patients with clinical episodes. RESULTS: During scheduled visits, qPCR (39.9%, N = 119) and PfHRP-2 ELISA (36.9%, N = 110) detected higher parasite prevalence than pLDH ELISA (16.8%, N = 50) and all methods were more sensitive than microscopy (13.4%, N = 40). All microscopically detected infections contained P. falciparum, as mono-infections (95%) or with P. malariae (5%). By qPCR, 102/119 infections were speciated. P. falciparum predominated either as monoinfections (71.6%), with P. malariae (8.8%), P. ovale (4.9%) or both (3.9%). P. malariae (6.9%) and P. ovale (1.0%) also occurred as co-infections (2.9%). As expected, higher prevalences were detected during sick visits, with prevalences of 65.8% (qPCR), 60.5% (PfHRP-2 ELISA), 21.1% (pLDH ELISA) and 31.6% (microscopy). PfHRP-2 showed biomass build-up that climaxed (1813±3410 ng/mL SD) at clinical episodes. CONCLUSION: PfHRP-2 ELISA and qPCR may be needed for accurately quantifying the malaria parasite burden. In addition, qPCR improves parasite speciation, whilst PfHRP-2 ELISA is a potential predictor for clinical disease caused by P. falciparum. TRIAL REGISTRATION: ClinicalTrials.gov NCT00666380

Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region

Background: With an overall decline of malaria incidence, elimination of malaria is gradually becoming the next target for many of countries affected by the disease. In Kenya the national malaria control strategy is aiming to reach pre-elimination for most parts of the country. However, considerable heterogeneity in prevalence of the disease within the country and especially the remaining high prevalent region of the Lake endemic region is likely to slow progress towards this target. To achieve a sustained control and an eventual elimination, a clear understanding of drivers of ongoing malaria transmission in remaining hotspots is needed. Methods: Data from the 2015 Malaria Indicator Survey (MIS) were analysed for prevalence of malaria parasitaemia in children (6 months to 14 years) of different countries within the highly endemic Lake region. Univariate and multivariate logistic regression analysis were preformed to explore associations between selected risk factors and being parasitaemic. A predictive model was built for the association between malaria and the risk factors with the aim of identifying heterogeneities of the disease at the lower administrative levels. Results: Overall, 604/2253 (27%, 95% CI 21.8-32.2) children were parasitaemic. The highest prevalence was observed in Busia County (37%) and lowest in Bungoma County (18%). Multivariate logistic regression analysis showed that the 10-14 years age group (OR = 3.0, 95% CI 2.3-4.1), households in the poorest socio-economic class (OR = 2.1, 95% CI 1.3-3.3), farming (OR = 1.4, 95% CI 1.2-2.5) and residence in Busia (OR = 4.6, 95% CI 2.1-8.2), Kakamega (OR = 2.6, 95% CI 1.3-5.4), and Migori counties (OR = 4.6 95% CI 2.1-10.3) were associated with higher risk of parasitaemia. Having slept under a long-lasting insecticide-treated bed net (LLIN) was associated with a lower risk (OR = 0.7, 95% CI 0.6-0.9). No association were found between malaria infection and the gender of the child, the household head, and the education status of the household head. Discussion and conclusion: Detailed analysis of malaria prevalence data in a hotspot area can identify new threats and avail opportunities for directing intervention. In the Lake endemic region of Kenya, interventions should be focused more on counties with the highest prevalence, and should target older children as well as children from the lower socio-economic strata. Precisely targeting interventions in remaining hotspots and high-risk populations will likely make impact and accelerate progress towards pre-elimination targets

Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region

Background: With an overall decline of malaria incidence, elimination of malaria is gradually becoming the next target for many of countries affected by the disease. In Kenya the national malaria control strategy is aiming to reach pre-elimination for most parts of the country. However, considerable heterogeneity in prevalence of the disease within the country and especially the remaining high prevalent region of the Lake endemic region is likely to slow progress towards this target. To achieve a sustained control and an eventual elimination, a clear understanding of drivers of ongoing malaria transmission in remaining hotspots is needed. Methods: Data from the 2015 Malaria Indicator Survey (MIS) were analysed for prevalence of malaria parasitaemia in children (6 months to 14 years) of different countries within the highly endemic Lake region. Univariate and multivariate logistic regression analysis were preformed to explore associations between selected risk factors and being parasitaemic. A predictive model was built for the association between malaria and the risk factors with the aim of identifying heterogeneities of the disease at the lower administrative levels. Results: Overall, 604/2253 (27%, 95% CI 21.8-32.2) children were parasitaemic. The highest prevalence was observed in Busia County (37%) and lowest in Bungoma County (18%). Multivariate logistic regression analysis showed that the 10-14 years age group (OR = 3.0, 95% CI 2.3-4.1), households in the poorest socio-economic class (OR = 2.1, 95% CI 1.3-3.3), farming (OR = 1.4, 95% CI 1.2-2.5) and residence in Busia (OR = 4.6, 95% CI 2.1-8.2), Kakamega (OR = 2.6, 95% CI 1.3-5.4), and Migori counties (OR = 4.6 95% CI 2.1-10.3) were associated with higher risk of parasitaemia. Having slept under a long-lasting insecticide-treated bed net (LLIN) was associated with a lower risk (OR = 0.7, 95% CI 0.6-0.9). No association were found between malaria infection and the gender of the child, the household head, and the education status of the household head. Discussion and conclusion: Detailed analysis of malaria prevalence data in a hotspot area can identify new threats and avail opportunities for directing intervention. In the Lake endemic region of Kenya, interventions should be focused more on counties with the highest prevalence, and should target older children as well as children from the lower socio-economic strata. Precisely targeting interventions in remaining hotspots and high-risk populations will likely make impact and accelerate progress towards pre-elimination targets

Trends in malaria prevalence by diagnostic method among the study participants that did not develop clinical malaria during the 112-day study.

<p>At every visit, malaria prevalences are highest when detected by <i>Pf</i>HRP-2 ELISA and qRT-PCR methods and lowest when measured with microscopy and <i>p</i>LDH ELISA.</p

Pair-wise Comparison of malaria diagnostic methods using Generalized Estimating Equations (GEE).

<p>Pair-wise Comparison of malaria diagnostic methods using Generalized Estimating Equations (GEE).</p

Utility of microscopy, qPCR, PfHRP-2 and pLDH ELISAs in predicting clinical episodes.

<p>Parasite dynamics before clinical malaria attack (<b>day 0</b>) as measured by (A) <i>Pf</i>HRP-2, (B) <i>p</i>LDH (C) Microscopy and (D) qPCR, for the 12 participants with microscopically confirmed clinical malaria. Parasite dynamics after clinical attack are also presented for <i>Pf</i>HRP-2 (A). Error bars represent standard error of mean of the parasitemia values at each time point. The arrows indicate the day of treatment. Microscopy, <i>p</i>LDH and qPCR did not detect malaria parasites after the treatment.</p

Species-specific primers and probes for detecting <i>Plasmodium</i> parasites.

<p>Species-specific primers and probes for detecting <i>Plasmodium</i> parasites.</p

Comparison of routine microscopy, <i>p</i>LDH/<i>Pf</i>HRP-2 ELISA and qPCR for a group of study participants who had acute blood smears prepared at sick visits.

<p>Each column (1–38) represents one blood sample with the corresponding microscopy, <i>p</i>LDH/<i>Pf</i>HRP-2 ELISA and qRT-PCR results, ordered by parasite density as determined by microscopy (top graph) and antigen levels (<i>p</i>LDH/<i>Pf</i>HRP-2) or Ct values (qPCR). As the levels of parasitemia decreases, the concordance between the different methods also decreases. <i>Pf</i>HRP-2 and qPCR detect parasites densities way beyond the detection limit of microscopy.</p

The number and percentage of <i>Plasmodium</i> parasite species detected by microscopy and qPCR.

<p>Key: Pf = <i>P. falciparum</i>, Pm = <i>P. malariae</i>, Po = <i>P. ovale</i>.</p

Geographical locations of 18 laboratories with PCR, GeneXpert, and genetic sequencing capacity in Yemen by November 2022.

Geographical locations of 18 laboratories with PCR, GeneXpert, and genetic sequencing capacity in Yemen by November 2022.</p