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A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations.

ABSTRACT: BACKGROUND:The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces. METHODS:We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID?=?CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression. RESULTS:Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho?=?-?0.989; 95% CI -?0.998, -?0.939; p?

SUBMITTER: Kamau A

PROVIDER: S-EPMC7189714 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations.

Kamau Alice A Mogeni Polycarp P Okiro Emelda A EA Snow Robert W RW Bejon Philip P

BMC medicine 20200429 1

<h4>Background</h4>The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.<h4>Meth ...[more]

PMID: 32345315

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Project description:BackgroundAccording to the World Health Organization (WHO), in 2018, an estimated 228 million malaria cases occurred worldwide with most cases occurring in sub-Saharan Africa. Scale-up of vector control tools coupled with increased access to diagnosis and effective treatment has resulted in a large decline in malaria prevalence in some areas, but other areas have seen little change. Although interventional studies demonstrate that preventing malaria during pregnancy can reduce the rate of low birth weight (i.e. child's birth weight <2500 g), it remains unknown whether natural changes in parasite transmission and malaria burden can improve birth outcomes.MethodsWe conducted an observational study of the effect of changing malaria burden on low birth weight using data from 18,112 births in 19 countries in sub-Saharan African countries during the years 2000-2015. Specifically, we conducted a difference-in-differences study via a pair-of-pairs matching approach using the fact that some sub-Saharan areas experienced sharp drops in malaria prevalence and some experienced little change.ResultsA malaria prevalence decline from a high rate (Plasmodium falciparum parasite rate in children aged 2-up-to-10 (i.e. PfPR2-10) > 0.4) to a low rate (PfPR2-10 < 0.2) is estimated to reduce the rate of low birth weight by 1.48 percentage points (95% confidence interval: 3.70 percentage points reduction, 0.74 percentage points increase), which is a 17% reduction in the low birth weight rate compared to the average (8.6%) in our study population with observed birth weight records (1.48/8.6 ≈ 17%). When focusing on first pregnancies, a decline in malaria prevalence from high to low is estimated to have a greater impact on the low birth weight rate than for all births: 3.73 percentage points (95% confidence interval: 9.11 percentage points reduction, 1.64 percentage points increase).ConclusionsAlthough the confidence intervals cannot rule out the possibility of no effect at the 95% confidence level, the concurrence between our primary analysis, secondary analyses, and sensitivity analyses, and the magnitude of the effect size, contribute to the weight of the evidence suggesting that declining malaria burden can potentially substantially reduce the low birth weight rate at the community level in sub-Saharan Africa, particularly among firstborns. The novel statistical methodology developed in this article-a pair-of-pairs approach to a difference-in-differences study-could be useful for many settings in which different units are observed at different times.FundingRyan A. Simmons is supported by National Center for Advancing Translational Sciences (UL1TR002553). The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Project description:BackgroundOver a decade ago, the Roll Back Malaria Partnership was launched, and since then there has been unprecedented investment in malaria control. We examined the change in malaria transmission intensity during the period 2000-10 in Africa.MethodsWe assembled a geocoded and community Plasmodium falciparum parasite rate standardised to the age group 2-10 years (PfPR2-10) database from across 49 endemic countries and territories in Africa from surveys undertaken since 1980. The data were used within a Bayesian space-time geostatistical framework to predict PfPR2-10 in 2000 and 2010 at a 1 × 1 km spatial resolution. Population distribution maps at the same spatial resolution were used to compute populations at risk by endemicity class and estimate population-adjusted PfPR2-10 (PAPfPR2-10) for each of the 44 countries for which predictions were possible for each year.FindingsBetween 2000 and 2010, the population in hyperendemic (>50% to 75% PfPR2-10) or holoendemic (>75% PfPR2-10) areas decreased from 218·6 million (34·4%) of 635·7 million to 183·5 million (22·5%) of 815·7 million across 44 malaria-endemic countries. 280·1 million (34·3%) people lived in areas of mesoendemic transmission (>10% to 50% PfPR2-10) in 2010 compared with 178·6 million (28·1%) in 2000. Population in areas of unstable or very low transmission (<5% PfPR2-10) increased from 131·7 million people (20·7%) in 2000 to 219·0 million (26·8%) in 2010. An estimated 217·6 million people, or 26·7% of the 2010 population, lived in areas where transmission had reduced by at least one PfPR2-10 endemicity class. 40 countries showed a reduction in national mean PAPfPR2-10. Only ten countries contributed 87·1% of the population living in areas of hyperendemic or holoendemic transmission in 2010.InterpretationSubstantial reductions in malaria transmission have been achieved in endemic countries in Africa over the period 2000-10. However, 57% of the population in 2010 continued to live in areas where transmission remains moderate to intense and global support to sustain and accelerate the reduction of transmission must remain a priority.FundingWellcome Trust.

Project description:BackgroundLimited understanding exists about the interactions between malaria and soil-transmitted helminths (STH), their potential geographical overlap and the factors driving it. This study characterised the geographical and co-clustered distribution patterns of malaria and STH infections among vulnerable populations in sub-Saharan Africa (SSA).Methodology/principal findingsWe obtained continuous estimates of malaria prevalence from the Malaria Atlas Project (MAP) and STH prevalence surveys from the WHO-driven Expanded Special Project for the Elimination of NTDs (ESPEN) from Jan 1, 2000, to Dec 31, 2018. Although, MAP provides datasets on the estimated prevalence of Plasmodium falciparum at 5km x 5km fine-scale resolution, we calculated the population-weighted prevalence of malaria for each implementation unit to ensure that both malaria and STH datasets were on the same spatial resolution. We incorporated survey data from 5,935 implementation units for STH prevalence and conducted the prevalence point estimates before and after 2003. We used the bivariate local indicator of spatial association (LISA analysis) to explore potential co-clustering of both diseases at the implementation unit levels among children aged 2-10 years for P. falciparum and 5-14 years for STH, living in SSA. Our analysis shows that prior to 2003, a greater number of SSA countries had a high prevalence of co-endemicity with P.falciparium and any STH species than during the period from 2003-2018. Similar prevalence and distribution patterns were observed for the co-endemicity involving P.falciparum-hookworm, P.falciparum-Ascaris lumbricoides and P.falciparum-Trichuris trichiura, before and after 2003. We also observed spatial variations in the estimates of the prevalence of P. falciparum-STH co-endemicity and identified hotspots across many countries in SSA with inter-and intra-country variations. High P. falciparum and high hookworm co-endemicity was more prevalent in West and Central Africa, whereas high P. falciparum with high A. lumbricoides and high P. falciparum with high T. trichiura co-endemicity were more predominant in Central Africa, compared to other sub-regions in SSA.Conclusions/significanceWide spatial heterogeneity exists in the prevalence of malaria and STH co-endemicity within the regions and within countries in SSA. The geographical overlap and spatial co-existence of malaria and STH could be exploited to achieve effective control and elimination agendas through the integration of the vertical control programmes designed for malaria and STH into a more comprehensive and sustainable community-based paradigm.

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A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations.

Publications

A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations.

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