Dataset Information

Predictive markers of transmission in areas with different malaria endemicity in north-eastern Tanzania based on seroprevalence of antibodies against Plasmodium falciparum

ABSTRACT:

Objective

A community-based cross-sectional study was done to assess Plasmodium falciparum exposure in areas with different malaria endemicity in north-eastern Tanzania using serological markers; PfAMA-1 and PfMSP-119.

Results

Bondo had a higher seroprevalence 36.6% (188) for PfAMA-1 as compared to Hai 13.8% (33), χ2 = 34.66, p < 0.01. Likewise, Bondo had a higher seroprevalence 201(36.6%) for PfMSP-1 as compared to Hai 41 (17.2%), χ2 = 29.62, p < 0.01. Anti-PfAMA-1 titters were higher in malaria positive individuals (n = 47) than in malaria negative individuals (n = 741) (p = 0.07). Anti-PfMSP-1 antibody concentrations were significantly higher in malaria-positive individuals (n = 47) than in malaria-negative individuals (n = 741) (p = 0.003). Antibody response against PfAMA-1 was significantly different between the three age groups; < 5 years, 5 to 15 years and > 15 years in both sites of Bondo and Hai. Likewise, antibody response against PfMSP-119 was significantly different between the three age groups in the two sites (p < 0.001). We also found significant differences in the anti-PfAMA-1and anti-PfMSP-119 antibody concentrations among the three age groups in the two sites (p = 0.004 and 0.005) respectively. Immunological indicators of P. falciparum exposure have proven to be useful in explaining long-term changes in the transmission dynamics, especially in low transmission settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05818-y.

SUBMITTER: Kaaya R

PROVIDER: S-EPMC8557592 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMalaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010.MethodsPlasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006-2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985-2009). After quality control, 2,516 were included into a national database of age-standardized 2-10 year old PfPR data (PfPR(2-10)) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR(2-10) endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface.ResultsWe estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas.ConclusionWhile further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies.

Project description:BackgroundEfficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007.Methods and findingsA total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2-10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2-10 < or = 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2-10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2-10 > or = 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk.ConclusionsHigh levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.

Project description:BackgroundTransmission intensity affects almost all aspects of malaria epidemiology and the impact of malaria on human populations. Maps of transmission intensity are necessary to identify populations at different levels of risk and to evaluate objectively options for disease control. To remain relevant operationally, such maps must be updated frequently. Following the first global effort to map Plasmodium falciparum malaria endemicity in 2007, this paper describes the generation of a new world map for the year 2010. This analysis is extended to provide the first global estimates of two other metrics of transmission intensity for P. falciparum that underpin contemporary questions in malaria control: the entomological inoculation rate (PfEIR) and the basic reproductive number (PfR).MethodsAnnual parasite incidence data for 13,449 administrative units in 43 endemic countries were sourced to define the spatial limits of P. falciparum transmission in 2010 and 22,212 P. falciparum parasite rate (PfPR) surveys were used in a model-based geostatistical (MBG) prediction to create a continuous contemporary surface of malaria endemicity within these limits. A suite of transmission models were developed that link PfPR to PfEIR and PfR and these were fitted to field data. These models were combined with the PfPR map to create new global predictions of PfEIR and PfR. All output maps included measured uncertainty.ResultsAn estimated 1.13 and 1.44 billion people worldwide were at risk of unstable and stable P. falciparum malaria, respectively. The majority of the endemic world was predicted with a median PfEIR of less than one and a median PfRc of less than two. Values of either metric exceeding 10 were almost exclusive to Africa. The uncertainty described in both PfEIR and PfR was substantial in regions of intense transmission.ConclusionsThe year 2010 has a particular significance as an evaluation milestone for malaria global health policy. The maps presented here contribute to a rational basis for control and elimination decisions and can serve as a baseline assessment as the global health community looks ahead to the next series of milestones targeted at 2015.

Project description:BackgroundThe efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.MethodsFirst, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.ResultsMasks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.ConclusionThe availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

Project description:BACKGROUND: Molecular techniques have facilitated the studies on genetic diversity of Plasmodium species particularly from field isolates collected directly from patients. The msp-1 and msp-2 are highly polymorphic markers and the large allelic polymorphism has been reported in the block 2 of the msp-1 gene and the central repetitive domain (block3) of the msp-2 gene. Families differing in nucleotide sequences and in number of repetitive sequences (length variation) were used for genotyping purposes. As limited reports are available on the genetic diversity existing among Plasmodium falciparum population of India, this report evaluates the extent of genetic diversity in the field isolates of P. falciparum in eastern and north-eastern regions of India. METHODS: A study was designed to assess the diversity of msp-1 and msp-2 among the field isolates from India using allele specific nested PCR assays and sequence analysis. Field isolates were collected from five sites distributed in three states namely, Assam, West Bengal and Orissa. RESULTS: P. falciparum isolates of the study sites are highly diverse in respect of length as well as sequence motifs with prevalence of all the reported allelic families of msp-1 and msp-2. Prevalence of identical allelic composition as well as high level of sequence identity of alleles suggest a considerable amount of gene flow between the P. falciparum populations of different states. A comparatively higher proportion of multiclonal isolates as well as multiplicity of infection (MOI) was observed among isolates of highly malarious districts Karbi Anglong (Assam) and Sundergarh (Orissa). In all the five sites, R033 family of msp-1 was observed to be monomorphic with an allele size of 150/160 bp. The observed 80-90% sequence identity of Indian isolates with data of other regions suggests that Indian P. falciparum population is a mixture of different strains. CONCLUSION: The present study shows that the field isolates of eastern and north-eastern regions of India are highly diverse in respect of msp-1 (block 2) and msp-2 (central repeat region, block 3). As expected Indian isolates present a picture of diversity closer to southeast Asia, Papua New Guinea and Latin American countries, regions with low to meso-endemicity of malaria in comparison to African regions of hyper- to holo-endemicity.

Project description:BackgroundThe scaling up of malaria control and renewed calls for malaria eradication have raised interest in defining timelines for changes in malaria endemicity.MethodsThe epidemiological theory for the decline in the Plasmodium falciparum parasite rate (PfPR, the prevalence of infection) following intervention was critically reviewed and where necessary extended to consider superinfection, heterogeneous biting, and aging infections. Timelines for malaria control and elimination under different levels of intervention were then established using a wide range of candidate mathematical models. Analysis focused on the timelines from baseline to 1% and from 1% through the final stages of elimination.ResultsThe Ross-Macdonald model, which ignores superinfection, was used for planning during the Global Malaria Eradication Programme (GMEP). In models that consider superinfection, PfPR takes two to three years longer to reach 1% starting from a hyperendemic baseline, consistent with one of the few large-scale malaria control trials conducted in an African population with hyperendemic malaria. The time to elimination depends fundamentally upon the extent to which malaria transmission is interrupted and the size of the human population modelled. When the PfPR drops below 1%, almost all models predict similar and proportional declines in PfPR in consecutive years from 1% through to elimination and that the waiting time to reduce PfPR from 10% to 1% and from 1% to 0.1% are approximately equal, but the decay rate can increase over time if infections senesce.ConclusionThe theory described herein provides simple "rules of thumb" and likely time horizons for the impact of interventions for control and elimination. Starting from a hyperendemic baseline, the GMEP planning timelines, which were based on the Ross-Macdonald model with completely interrupted transmission, were inappropriate for setting endemicity timelines and they represent the most optimistic scenario for places with lower endemicity. Basic timelines from PfPR of 1% through elimination depend on population size and low-level transmission. These models provide a theoretical basis that can be further tailored to specific control and elimination scenarios.