Malaria

Torleif Markussen Lunde defended his PhD thesis on Monday August 19, 2013.

The title of the thesis is: «On the dynamics of two efficient malaria vectors of the Afrotropical region: Anopheles gambiae s.s and Anopheles arabiensis».

Abstract

Weather and climate are only some of the factors influencing the dynamics of malaria. With the ongoing debate on the consequences of climate change, there is a need for models which are designed to address these questions. Historically, models have fo- cused on the theoretical principles of eradication, with less emphasis on a changing environment. To estimate the potential impact of climate change on malaria, we need new models which consider a wider range of environmental variables.

In this thesis, we point at some factors which are important to robustly project the influence of climate and weather on malaria. These factors are described using a math- ematical model which focus on the weather sensitive parts of malaria transmission; the mosquitoes and the parasites.

Mosquitoes transmitting malaria belong to the genus Anopheles. There are about 460 known anophelines, where 41 are considered to be dominant vectors of malaria. Each of these species have its own life history, and consequently weather and climate influence each species differently. In Africa, the public health impact of malaria is dev- astating, despite variable transmission. The most efficient mosquitoes are found in this continent: among them Anopehels gambiae sensu stricto and Anopheles arabiensis, which are considered to be of major importance.

In this thesis (Paper I) we describe a dynamical model which include these two species. Based on a literature review, we formulate a model which allows weather to influence each of the two species according to their life history. They compete over puddles, important for reproduction; An. gambiae s.s. mainly feed on humans op- posed to An. arabiensis which feed on cattle and humans; they are allowed to disperse, meaning new ares can be occupied by the species; and as they become older, the daily probability of survival changes. Many of these factors are not important in a short time perspective. But, since climate change is slow process compared to the life of a singe mosquito, there is a need for additional complexity to study how a slowly changing en- vironment influence the population dynamics of these malaria vectors.

To have confidence the model is realistic in the current climate we validated the model in paper II. To date, we constructed the most extensive database on the occur- rence of the two mosquitoes. These data were used to validate the model described in paper I. We concluded the mosquito model produced comparable or better results than existing predictions of the two species under current climate.

An. arabiensis feed on humans and cattle. Since the density and distribution of those are not static, but are changing over time, and the distribution of An. arabiensis is highly dependent on the density of cattle, there is a need to; 1. Document histori- cal changes; 2. Understand how they are influenced by the environment. In paper III we reconstruct the cattle distribution and density in the 1960s, and show how climate variability influence the national cattle holdings. While climate variability has a minor influence in many countries, we also find variations in the climate can explain more than 40% of the national cattle holdings in some countries. The data developed in this paper can be used in the model described in paper I, as well as other studies where cat- tle is an important part of the system.

It has been claimed the optimal temperature for malaria transmission is between 30 to 32◦C, with the potential increasing linearly from 20 to 32◦C. With this claim, any warming in sub-Saharan Africa would potentially cause more malaria. Using the model developed in paper I, we show malaria transmission is most effective around 25◦C, with a decline in efficiency over end below this temperature (Paper IV). This disputes the theory claimed in previous papers. Any projections relating temperature and malaria should be interpreted with care.

The influence of climate change on malaria transmission is still uncertain. With this thesis, we have come a step further in understanding how the environment can alter malaria transmission. However, the future occurrence of malaria is dependent on many other factors, including malaria control measures, access to and usage of treatment, city planning, and immunity.

Analysis of risk factors for malaria in the highland fringe areas of southern Ethiopia. . Using a multistage sampling technique 3,398 people were sampled from 750 households. Results suggest that younger age, lower altitudes, and houses with holes were significant risk factors for malaria infection in this population.

Woyessa A, Deressa W, Ali A, Lindtjørn B. Malaria risk factors in Butajira area, south-central Ethiopia: a multilevel analysis. Malaria Journal 2013, 12:273. 

Abstract (provisional)

Background

The highlands of Ethiopia, situated between 1,500 and 2,500 m above sea level, experienced severe malaria epidemics. Despite the intensive control attempts, underway since 2005 and followed by an initial decline, the disease remained a major public health concern. The aim of this study was to identify malaria risk factors in highland-fringe south-central Ethiopia.

Methods

This study was conducted in six rural kebeles of Butajira area located 130 km south of Addis Ababa, which are part of demographic surveillance site in Meskan and Mareko Districts, Ethiopia. Using a multistage sampling technique 750 households was sampled to obtain the 3,398 people, the estimated sample size for this study. Six repeated cross-sectional surveys were conducted from October 2008 to June 2010. Multilevel, mixed-effects logistic regression models fitted to Plasmodium infection status (positive or negative) and six variables. Both fixed- and random-effects differences in malaria infection were estimated using median odds ratio and interval odds ratio 80%. The odds ratios and 95% confidence intervals were used to estimate the strength of association.

Results

Overall, 19,207 individuals were sampled in six surveys (median and inter-quartile range value three). Six of the five variables had about two-fold to eight-fold increase in prevalence of malaria. Furthermore, among these variables, October-November survey seasons of both during 2008 and 2009 were strongly associated with increased prevalence of malaria infection. Children aged below five years (adjusted OR= 3.62) and children aged five to nine years (adj. OR= 3.39), low altitude (adj. OR= 5.22), mid-level altitude (adj. OR= 3.80), houses with holes (adj. OR= 1.59), survey seasons such as October-November 2008 (adj. OR= 7.84), January-February 2009 (adj. OR= 2.33), June-July 2009 (adj. OR=3.83), October-November 2009 (adj. OR= 7.71), and January-February 2010 (adj. OR= 3.05) were associated with increased malaria infection.

The estimates of cluster variances revealed differences in malaria infection. The village-level intercept variance for the individual-level predictor (0.71 [95% CI: 0.28-1.82]; SE=0.34) and final (0.034, [95% CI: 0.002-0.615]; SE=0.05) were lower than that of empty (0.80, [95% CI: 0.32-2.01]; SE=0.21).

Conclusion

Malaria control efforts in highland fringes must prioritize children below ten years in designing transmission reduction of malaria elimination strategy.

Massebo F, Balkew M, Gebre-Michael T, Lindtjorn B. Entomologic Inoculation Rates of Anopheles arabiensis in Southwestern Ethiopia. Am J Trop Med Hyg 2013.

Abstract

We collected anophelines every second week for one year from randomly selected houses in southwestern Ethiopia by using Centers for Disease Control (CDC) light traps, pyrethrum spray catches, and artificial pit shelter constructions to detect circumsporozoite proteins and estimate entomologic inoculation rates (EIRs). Of 3,678 Anopheles arabiensis tested for circumsporozoite proteins, 11 were positive for Plasmodium falciparum and three for P. vivax. The estimated annual P. falciparum EIR of An. arabiensis was 17.1 infectious bites per person per year (95% confidence interval = 7.03–34.6) based on CDC light traps and 0.1 infectious bites per person per year based on pyrethrum spray catches. The P. falciparum EIRs from CDC light traps varied from 0 infectious bites per person per year (in 60% of houses) to 73.2 infectious bites per person per year in the house nearest the breeding sites. Risk of exposure to infectious bites was higher in wet months than dry months, with a peak in April (9.6 infectious bites per person per year), the period of highest mosquito density.

The full text is found here.

This paper reviews the sensitivity and specificity of RDTs for detecting P. falciparum and P. vivax in two different settings ¿ at health centres and in a household survey. The study includes a large number of patients (2,394 participants) and provides useful additional information about the performance of these RDTs.

Woyessa A, Deressa W, Ali A, Lindtjørn B. Evaluation of CareStartTM malaria Pf/Pv combo test for Plasmodium falciparum and Plasmodium vivax malaria diagnosis in Butajira area, south-central Ethiopia. Malaria Journal 2013, 12:218 doi:10.1186/1475-2875-12-218

Abstact:

Malaria is a major public health problem in Ethiopia. Plasmodium falciparum and Plasmodium vivax co-exist and malaria rapid diagnostic test (RDTs) is vital in rendering parasite-confirmed treatment especially in areas where microscopy is not available. CareStartTM Malaria Pf/Pv combo test was evaluated compared to microscopy in Butajira area, south-central Ethiopia. This RDT detects histidine-rich protein-2 (HRP2) found in P. falciparum, and Plasmodium enzyme lactate dehydrogenase (pLDH) for diagnosis of P. vivax 2008-2010. The standard for the reporting of diagnostic accuracy studies was complied. Among 2,394 participants enrolled, 10.9% (n=87) were Plasmodium infected (household survey) and 24.5% (n=392) using microscopy. In the household surveys, the highest positivity was caused by P. vivax (83.9%, n=73), P. falciparum (15.0%, n=13), and the rest due to mixed infections of both (1.1%, n=1). In health facility, P. vivax caused 78.6% (n=308), P. falciparum caused 20.4% (n=80), and the rest caused by mixed infections 1.0% (n=4). RDT missed 9.1% (n=8) in household and 4.3% (n=17) in health facility-based surveys among Plasmodium positive confirmed by microscopy while 3.3% (n=24) in household and 17.2% (n=208) in health facility-based surveys were detected false positive. RDT showed agreement with microscopy in detecting 79 positives in household surveys (n=796) and 375 positives in health centre survey (n=1,598).RDT performance varied in both survey settings, lowest PPV (64.3%) for Plasmodium and P. falciparum (77.2%) in health centres; and Plasmodium (76.7%) and P. falciparum (87.5%) in household surveys. NPV was low in P. vivax in health centres (77.2%) and household (87.5%) surveys. Seasonally varying RDT precision of as low as 14.3% PPV (Dec. 2009), and 38.5% NPV (Nov. 2008) in health centre surveys; and 40-63.6% PPV was observed in household surveys. But the influence of age and parasite density on RDT performance was not ascertained. Establishing quality control of malaria RDT in the health system in areas with low endemic and P. falciparum and P. vivax co-exist is recommendable. CareStartTM RDT might be employed for epidemiological studies that require interpreting the results cautiously. Future RDT field evaluation against microscopy should be PCR corrected.

On May 30th, 2013, Adugna Woyessa defended his PhD thesis «The epidemiology of highland malaria in Ethiopia: a study from Butajira area».

New research uses almost 50 years of data to investigate how climate has affected cattle holdings in Africa since 1961. Such research is important also to understand the distribution of mosquitoes that transmit malaria.

Lunde TM and Lindtjørn B. Cattle and climate in Africa: How climate variability has influenced national cattle holdings from 1961–2008. PeerJ 2013; 1:e55 

The role of cattle in developing countries is as a source of high-quality food, as draft animals, and as a source of manure and fuel. Cattle represent important contribution to household incomes, and in drought prone areas they can act as an insurance against weather risk. So far, no studies have addressed how historical variations in temperature and rainfall have influenced cattle populations in Africa.

The focus of this study is to assess the historical impact of climate variability on national cattle holdings. We reconstruct the cattle density and distribution for two time periods; 1955–1960 and 2000–2005. Based on estimates from FAO and official numbers, we generated a time series of cattle densities from 1961–2008, and compared these data with precipitation and temperature anomalies for the same period.

We show that from 1961–2008 rainfall and temperature have been modulating, and occasionally controlling, the number of cattle in Africa.

This article is a validation of a mathematical model described earlier. Overall, the model gives a realistic representation of seasonal and year-to-year variability in mosquito densities and it can accurately predict the distribution of An. gambiae s.s. and An. arabiensis in sub-Saharan Africa. It may be used for seasonal and long-term predictions of changes in the burden of malaria.

Lunde TM, Balkew M, Korecha D, Gebre-Michael T, Massebo F, Sorteberg A and Lindtjørn B. A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. II. Validation of species distribution and seasonal variations. Malaria Journal 2013, 12:78

Background  The first part of this study aimed to develop a model for Anopheles gambiae s.l. with separate parametrization schemes for Anopheles gambiae s.s. and Anopheles arabiensis. The characterizations were constructed based on literature from the past decades. This part of the study is focusing on the model’s ability to separate the mean state of the two species of the An. gambiae complex in Africa. The model is also evaluated with respect to capturing the temporal variability of An. arabiensis in Ethiopia. Before conclusions and guidance based on models can be made, models need to be validated.

 Methods  The model used in this paper is described in part one (Malaria Journal 2013, 12:28). For the validation of the model, a data base of 5,935 points on the presence of An. gambiae s.s. and An. arabiensis was constructed. An additional 992 points were collected on the presence An. gambiae s.l.. These data were used to assess if the model could recreate the spatial distribution of the two species. The dataset is made available in the public domain. This is followed by a case study from Madagascar where the model’s ability to recreate the relative fraction of each species is investigated. In the last section the model’s ability to reproduce the temporal variability of An. arabiensis in Ethiopia is tested. The model was compared with data from four papers, and one field survey covering two years.

Results  Overall, the model has a realistic representation of seasonal and year to year variability in mosquito densities in Ethiopia. The model is also able to describe the distribution of An. gambiae s.s. and An. arabiensis in sub-Saharan Africa. This implies this model can be used for seasonal and long term predictions of changes in the burden of malaria. Before models can be used to improving human health, or guide which interventions are to be applied where, there is a need to understand the system of interest. Validation is an important part of this process. It is also found that one of the main mechanisms separating An. gambiae s.s. and An. arabiensis is the availability of hosts; humans and cattle. Climate play a secondary, but still important, role.