The relationship between facility-based malaria test positivity rate and community-based parasite prevalence

Kamau, Alice; Mtanje, Grace; Mataza, Christine; Malla, Lucas; Bejon, Philip; Snow, Robert W.

doi:10.1371/journal.pone.0240058

Cited by 12 publications

(13 citation statements)

References 53 publications

(72 reference statements)

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“…1). The study area has been described in detail elsewhere [22,23]. Briefly, malaria transmission is perennial but relatively higher during the long (April-June) and short (October-December) rains with an infection prevalence of approximately 10% detectable among residents of all ages [22].…”

Section: Study Areamentioning

confidence: 99%

Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

Kamau

Mtanje

Mataza

et al. 2021

Malar J

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Background The over-distributed pattern of malaria transmission has led to attempts to define malaria “hotspots” that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. Methods Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff’s spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. Results Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. Conclusion Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

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Section: Study Areamentioning

confidence: 99%

Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

Kamau

Mtanje

Mataza

et al. 2021

Malar J

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“…The carriage of sub-patent infections might be higher among afebrile individuals, as observed in pregnant women at rst ANC visit (9.2%), who tend to carry asymptomatic low-density infections [22]. Overall, the low malaria positivity rates in Monrovia compared to estimates from other African countries [23,24] might be due to the relatively lower risk of malaria infection among the population residing in Monrovia compared to the rural areas in Liberia. Positivity rate is higher among individuals reporting joint pain, vomiting, chills and shivers and weakness.…”

Section: Discussionmentioning

confidence: 92%

No Evidence of False-negative P. Falciparum Rapid Diagnostic Results in Monrovia, Liberia

King¹,

George

Cisteró

et al. 2021

Preprint

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Background: Malaria diagnosis relies mainly on the use of rapid diagnostic tests (RDTs). The majority of commercial RDTs used in Africa detect the Plasmodium falciparum histidine-rich protein 2 (PfHRP2). pfhrp2/3 gene deletions can therefore lead to false-negative RDT results. This study aimed to evaluate the frequency of PCR-confirmed, false-negative P. falciparum RDT results in Monrovia, Liberia.Methods: We used PfHRP2-based RDT (Paracheck Pf®) and microscopy results from 1038 individuals with fever or history of fever (n=951) and pregnant women at first antenatal care (ANC) visit (n=87) enrolled in the Saint Joseph Catholic Hospital (Monrovia) from March to July 2019 to assess the frequency of false-negative RDT results. True false negatives were confirmed by detecting the presence of P. falciparum DNA by quantitative PCR in samples from individuals with discrepant RDT and microscopy resultsResults: One hundred and eighty-six (19.6%) and 200 (21.0%) of the 951 febrile participants had a P. falciparum positive result by RDT and microscopy, respectively. Positivity rate increased with age and the reporting of joint pain, chills and shivers, vomiting and weakness, and increased with the presence of coughs and nausea. The positivity rate at first ANC visit was 5.7% (n=5) and 8% (n=7) by RDT and microscopy, respectively. Out of 207 Plasmodium infections detected by microscopy, 22 (11%) were negative by RDT. qPCR confirmed absence of P. falciparum DNA in the sixteen RDT-negative but microscopy-positive samples which were available for molecular testing. Conclusion: There is no qPCR-confirmed evidence of false-negative RDT results due to pfhrp2/pfhrp3 deletions in this study conducted in Monrovia (Liberia). This indicates the appropriate performance of PfHRP2-based RDTs for the diagnosis of malaria in Liberia. Nevertheless, active surveillance for the emergence of PfHRP2 deletions is required.

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“…This requires further investigation over large regions and possible interaction with malaria co-infections. However, the immediate application of such a hybrid approach is dependent upon a better understanding of the relationship between TPR to PR at varying endemicity, which is not always linear [41,42].…”

Section: Discussionmentioning

confidence: 99%

“…A threshold of > 70% population weighted TPR represented high burden sub-counties (or the 10% sub-counties with highest TPR), while < 30% represented sub-counties with low malaria risk. In previous studied 30% TPR was associated with low malaria prevalence estimated from community survey data [ 41 , 42 ]. Thus, areas where was closer in value to 100%, indicated the likelihood of location to be above the threshold .…”

Section: Methodsmentioning

confidence: 99%

Malaria micro-stratification using routine surveillance data in Western Kenya

Alegana

Macharia

Ikahu-Muchangi

et al. 2021

Malar J

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Background There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods Routine data from health facilities (n = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability < 30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels.

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The relationship between facility-based malaria test positivity rate and community-based parasite prevalence

Cited by 12 publications

References 53 publications

Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

No Evidence of False-negative P. Falciparum Rapid Diagnostic Results in Monrovia, Liberia

Malaria micro-stratification using routine surveillance data in Western Kenya

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