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. 2023 Feb 3;13(1):1998.
doi: 10.1038/s41598-023-28257-0.

Plasmodium falciparum infection prevalence among children aged 6-59 months from independent DHS and HIV surveys: Nigeria, 2018

Adan Oviedo  1 , Ado Abubakar  2 , Perpetua Uhomoibhi  3 , Mark Maire  4 , Uwem Inyang  5 , Bala Audu  3 , Nnaemeka C Iriemenam  6 , Abiodun Ogunniyi  7 , James Ssekitooleko  8 , Jo-Angeline Kalambo  8 , Stacie M Greby  6 , Nwando Mba  7 , Mahesh Swaminathan  6 , Chikwe Ihekweazu  7 , McPaul I Okoye  6 , Eric Rogier #  1 , Laura C Steinhardt #  9
Affiliations

Plasmodium falciparum infection prevalence among children aged 6-59 months from independent DHS and HIV surveys: Nigeria, 2018

Adan Oviedo et al. Sci Rep. .

Abstract

Prevalence estimates are critical for malaria programming efforts but generating these from non-malaria surveys is not standard practice. Malaria prevalence estimates for 6-59-month-old Nigerian children were compared between two national household surveys performed simultaneously in 2018: a Demographic and Health Survey (DHS) and the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). DHS tested via microscopy (n = 8298) and HRP2-based rapid diagnostic test (RDT, n = 11,351), and NAIIS collected dried blood spots (DBS) which were later tested for histidine-rich protein 2 (HRP2) antigen (n = 8029). National Plasmodium falciparum prevalence was 22.6% (95% CI 21.2- 24.1%) via microscopy and 36.2% (34.6- 37.8%) via RDT according to DHS, and HRP2 antigenemia was 38.3% (36.7-39.9%) by NAIIS DBS. Between the two surveys, significant rank-order correlation occurred for state-level malaria prevalence for RDT (Rho = 0.80, p < 0.001) and microscopy (Rho = 0.75, p < 0.001) versus HRP2. RDT versus HRP2 positivity showed 24 states (64.9%) with overlapping 95% confidence intervals from the two independent surveys. P. falciparum prevalence estimates among 6-59-month-olds in Nigeria were highly concordant from two simultaneous, independently conducted household surveys, regardless of malaria test utilized. This provides evidence for the value of post-hoc laboratory HRP2 detection to leverage non-malaria surveys with similar sampling designs to obtain accurate P. falciparum estimates.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Test results as available for Nigerian states and FCT. Geopolitical state boundaries of Nigeria (a) and cartograms illustrating unweighted counts of children with test results for RDT from the DHS (b) and HRP2 bead assay from NAIIS samples (c). Maps were generated using QGIS version 3.12.2 (www.qgis.org).
Figure 2
Figure 2
Malaria antigen prevalence by Nigerian state among 0–59-month-olds from 2018 NAIIS compared to the 2018 DHS. (a) RDT prevalence by DHS compared with HRP2 antigen prevalence from NAIIS samples. (b) Microscopy prevalence by DHS compared with HRP2 antigen prevalence from NAIIS samples.
Figure 3
Figure 3
P. falciparum infection prevalence estimates by Nigerian state among children 6–59 months old. Results by microscopy (a) and RDT (b) from the 2018 DHS and HRP2 bead assay (c) from the 2018 NAIIS. Maps were generated using QGIS version 3.12.2 (www.qgis.org).
Figure 4
Figure 4
Concordance of prevalence rank correlation by Nigerian state. Panels display scatterplot rank of prevalence by state as well as Spearman’s rank order correlation for (a) HRP2 bead assay versus microscopy (Rho = 0.75, p < 0.0001 and (b) HRP2 bead assay versus RDT (Rho = 0.80, p < 0.0001). Higher rank denotes higher prevalence. Reference line is x = y.
Figure 5
Figure 5
HRP2 bead assay versus RDT prevalence estimate agreement by 95% confidence interval (CI) for each Nigerian state. CI agreement is noted only if HRP2 bead assay prevalence point estimates are within respective RDT prevalence confidence intervals by state and vice versa. Otherwise, the state is classified based on the higher prevalence measure. Map was generated using QGIS version 3.12.2 (www.qgis.org).
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