Child mortality and malaria transmission intensity in Africa

Swiss Tropical Institute, Socinstrasse 57, PO Box CH-4002, Basel, Switzerland.
Trends in Parasitology (Impact Factor: 6.2). 04/2001; 17(3):145-9. DOI: 10.1016/S1471-4922(00)01814-6
Source: PubMed


The desirability of controlling malaria transmission in the areas of highest endemicity of Plasmodium falciparum has long been debated. Most recently, it has been claimed that rates of malaria morbidity are no higher in areas of very high transmission in Africa than they are in places with lower inoculation rates. We now review the literature on the relationship of morbidity and mortality to malaria transmission intensity, and have linked published child mortality and malaria transmission rates to examine how age-specific mortality actually varies with the inoculation rate of P. falciparum.

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    • "Discrepant results that might be related to higher levels of indirect mortality attributable to malaria have been observed in various attempts to relate transmission and mortality. In a review article, Smith et al.[20] found an increase in infant mortality rate with increase in entomological inoculation rate (EIR) in Africa. However, Gemperli [22] linked the demographic and health surveys (DHS) and mapping malaria risk in Africa (MARA) databases to assess the effect of malaria transmission on mortality, and found no clear relationship. "
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    ABSTRACT: The precise nature of the relationship between malaria mortality and levels of transmission is unclear. Due to methodological limitations, earlier efforts to assess the linkage have lead to inconclusive results. The malaria transmission intensity and mortality burden across Africa (MTIMBA) project initiated by the INDEPTH Network collected longitudinally entomological data within a number of sites in sub-Saharan Africa to study this relationship. This work linked the MTIMBA entomology database with the routinely collected vital events within the Rufiji Demographic Surveillance System to analyse the transmission-mortality relation in the region. Bayesian Bernoulli spatio-temporal Cox proportional hazards models with village clustering, adjusted for age and insecticide-treated nets (ITNs), were fitted to assess the relation between mortality and malaria transmission measured by entomology inoculation rate (EIR). EIR was predicted at household locations using transmission models and it was incorporated in the model as a covariate with measure of uncertainty. Effects of covariates estimated by the model are reported as hazard ratios (HR) with 95% Bayesian confidence interval (BCI) and spatial and temporal parameters are presented. Separate analysis was carried out for neonates, infants and children 1-4 years of age. No significant relation between all-cause mortality and intensity of malaria transmission was indicated at any age in childhood. However, a strong age effect was shown. Comparing effects of ITN and EIR on mortality at different age categories, a decrease in protective efficacy of ITN was observed (i.e. neonates: HR = 0.65; 95% BCI: 0.39-1.05; infants: HR = 0.72; 95% BCI:0.48-1.07; children 1-4 years: HR = 0.88; 95% BCI: 0.62-1.23) and reduction on the effect of malaria transmission exposure was detected (i.e. neonates: HR = 1.15; 95% BCI:0.95-1.36; infants: HR = 1.13; 95% BCI:0.98-1.25; children 1-4 years: HR = 1.04; 95% BCI:0.89-1.18). A very strong spatial correlation was also observed. These results imply that assessing the malaria transmission-mortality relation involves more than the knowledge on the performance of interventions and control measures. This relation depends on the levels of malaria endemicity and transmission intensity, which varies significantly between different settings. Thus, sub-regions analyses are necessary to validate and assess reproducibility of findings.
    Malaria Journal 03/2014; 13(1):124. DOI:10.1186/1475-2875-13-124 · 3.11 Impact Factor
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    • "In Ghana, malaria transmission has shown a clear variation over time, season and space [16-18]. The relationship between malaria transmission and mortality is still unclear [19,20]. To clarify the relationship between malaria transmission and mortality, the Malaria Transmission Intensity and Mortality Burden Across Africa (MTIMBA) project was established in 10 INDEPTH network sites between 2001 and 2004 [21,22]. "
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    ABSTRACT: Background: The relationship between entomological measures of malaria transmission intensity and mortality remains uncertain. This is partly because transmission is heterogeneous even within small geographical areas. Studying this relationship requires high resolution, spatially structured, longitudinal entomological data. Geostatistical models that have been used to analyse the spatio-temporal heterogeneity have not considered the uncertainty in both sporozoite rate (SR) and mosquito density data. This study analysed data from Kassena-Nankana districts in northern Ghana to obtain small area estimates of malaria transmission rates allowing for this uncertainty. Methods: Independent Bayesian geostatistical models for sporozoite rate and mosquito density were fitted to produce explicit entomological inoculation rate (EIR) estimates for small areas and short time periods, controlling for environmental factors. Results: Mosquitoes were trapped from 2,803 unique locations for three years using mainly CDC light traps. Anopheles gambiae constituted 52%, the rest were Anopheles funestus. Mean biting rates for An. funestus and An. gambiae were 32 and 33 respectively. Most bites occurred in September, the wettest month. The sporozoite rates were higher in the dry periods of the last two years compared with the wet period. The annual EIR varied from 1,132 to 157 infective bites. Monthly EIR varied between zero and 388 infective bites. Spatial correlation for SR was lower than that of mosquito densities. Conclusion: This study confirms the presence of spatio-temporal heterogeneity in malaria transmission within a small geographical area. Spatial variance was stronger than temporal especially in the SR. The estimated EIR will be used in mortality analysis for the area.
    Malaria Journal 02/2013; 12(1):63. DOI:10.1186/1475-2875-12-63 · 3.11 Impact Factor
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    • "The LLIN-IRS combination strategy is mostly recommended for accelerating control in high transmission areas [2,12,38,41,44], where either IRS alone or ITNs alone may not be adequate [41], but where transmission must be reduced to near-undetectable levels to achieve any significant declines in malaria prevalence [41,91-93]. However, ITNs and IRS can also be used together for different other reasons. "
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    ABSTRACT: Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are currently the preferred methods of malaria vector control. In many cases, these methods are used together in the same households, especially to suppress transmission in holoendemic and hyperendemic scenarios. Though widespread, there has been limited evidence suggesting that such co-application confers greater protective benefits than either ITNs or IRS when used alone. Since both methods are insecticide-based and intradomicilliary, this article hypothesises that outcomes of their combination would depend on effects of the candidate active ingredients on mosquitoes that enter or those that attempt to enter houses. It is suggested here that enhanced household level protection can be achieved if the ITNs and IRS have divergent yet complementary properties, e.g. highly deterrent IRS compounds coupled with highly toxic ITNs. To ensure that the problem of insecticide resistance is avoided, the ITNs and IRS products should preferably be of different insecticide classes, e.g. pyrethroid-based nets combined with organophosphate or carbamate based IRS. The overall community benefits would however depend also on other factors such as proportion of people covered by the interventions and the behaviour of vector species. This article concludes by emphasizing the need for basic and operational research, including mathematical modelling to evaluate IRS/ITN combinations in comparison to IRS alone or ITNs alone.
    Malaria Journal 07/2011; 10(1):208. DOI:10.1186/1475-2875-10-208 · 3.11 Impact Factor
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