Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

BACKGROUND: Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS: Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS: Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION: The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

Data and tools to integrate climate and environmental information into public health.

BACKGROUND: During the last 30 years, the development of geographical information systems and satellites for Earth observation has made important progress in the monitoring of the weather, climate, environmental and anthropogenic factors that influence the reduction or the reemergence of vector-borne diseases. Analyses resulting from the combination of geographical information systems (GIS) and remote sensing have improved knowledge of climatic, environmental, and biodiversity factors influencing vector-borne diseases (VBDs) such as malaria, visceral leishmaniasis, dengue, Rift Valley fever, schistosomiasis, Chagas disease and leptospirosis. These knowledge and products developed using remotely sensed data helped and continue to help decision makers to better allocate limited resources in the fight against VBDs. MAIN BODY: Because VBDs are linked to climate and environment, we present here our experience during the last four years working with the projects under the, World Health Organization (WHO)/ The Special Programme for Research and Training in Tropical Diseases (TDR)-International Development Research Centre (IDRC) Research Initiative on VBDs and Climate Change to integrate climate and environmental information into research and decision-making processes. The following sections present the methodology we have developed, which uses remote sensing to monitor climate variability, environmental conditions, and their impacts on the dynamics of infectious diseases. We then show how remotely sensed data can be accessed and evaluated and how they can be integrated into research and decision-making processes for mapping risks, and creating Early Warning Systems, using two examples from the WHO TDR projects based on schistosomiasis analysis in South Africa and Trypanosomiasis in Tanzania. CONCLUSIONS: The tools presented in this article have been successfully used by the projects under the WHO/TDR-IDRC Research Initiative on VBDs and Climate Change. Combined with capacity building, they are an important piece of work which can significantly contribute to the goals of WHO Global Vector Control Response and to the Sustainable Development Goals especially those on health and climate action.

Impact of climate variability on the transmission risk of malaria in northern Côte d'Ivoire.

Since the 1970s, the northern part of Côte d'Ivoire has experienced considerable fluctuation in its meteorology including a general decrease of rainfall and increase of temperature from 1970 to 2000, a slight increase of rainfall since 2000, a severe drought in 2004-2005 and flooding in 2006-2007. Such changing climate patterns might affect the transmission of malaria. The purpose of this study was to analyze climate and environmental parameters associated with malaria transmission in Korhogo, a city in northern Côte d'Ivoire. All data were collected over a 10-year period (2004-2013). Rainfall, temperature and Normalized Difference Vegetation Index (NDVI) were the climate and environmental variables considered. Association between these variables and clinical malaria data was determined, using negative binomial regression models. From 2004 to 2013, there was an increase in the annual average precipitation (1100.3-1376.5 mm) and the average temperature (27.2°C-27.5°C). The NDVI decreased from 0.42 to 0.40. We observed a strong seasonality in these climatic variables, which resembled the seasonality in clinical malaria. An incremental increase of 10 mm of monthly precipitation was, on average, associated with a 1% (95% Confidence interval (CI): 0.7 to 1.2%) and a 1.2% (95% CI: 0.9 to 1.5%) increase in the number of clinical malaria episodes one and two months later respectively. A 1°C increase in average monthly temperature was, on average, associated with a decline of a 3.5% (95% CI: 0.1 to 6.7%) in clinical malaria episodes. A 0.1 unit increase in monthly NDVI was associated with a 7.3% (95% CI: 0.8 to 14.1%) increase in the monthly malaria count. There was a similar increase for the preceding-month lag (6.7% (95% CI: 2.3% to 11.2%)). The study results can be used to establish a malaria early warning system in Korhogo to prepare for outbreaks of malaria, which would increase community resilience no matter the magnitude and pattern of climate change.

Using Rainfall and Temperature Data in the Evaluation of National Malaria Control Programs in Africa.

Since 2010, the Roll Back Malaria (RBM) Partnership, including National Malaria Control Programs, donor agencies (e.g., President's Malaria Initiative and Global Fund), and other stakeholders have been evaluating the impact of scaling up malaria control interventions on all-cause under-five mortality in several countries in sub-Saharan Africa. The evaluation framework assesses whether the deployed interventions have had an impact on malaria morbidity and mortality and requires consideration of potential nonintervention influencers of transmission, such as drought/floods or higher temperatures. Herein, we assess the likely effect of climate on the assessment of the impact malaria interventions in 10 priority countries/regions in eastern, western, and southern Africa for the President's Malaria Initiative. We used newly available quality controlled Enhanced National Climate Services rainfall and temperature products as well as global climate products to investigate likely impacts of climate on malaria evaluations and test the assumption that changing the baseline period can significantly impact on the influence of climate in the assessment of interventions. Based on current baseline periods used in national malaria impact assessments, we identify three countries/regions where current evaluations may overestimate the impact of interventions (Tanzania, Zanzibar, Uganda) and three countries where current malaria evaluations may underestimate the impact of interventions (Mali, Senegal and Ethiopia). In four countries (Rwanda, Malawi, Mozambique, and Angola) there was no strong difference in climate suitability for malaria in the pre- and post-intervention period. In part, this may be due to data quality and analysis issues.

An observatory to gather and disseminate information on the health-related effects of environmental and climate change.

This report sought to critically examine proposals, potentials, and challenges of environmental health observatories with an emphasis on climate change processes. A critical review of existing environmental health observatories was performed, examining their purposes, potential audiences, and technological platforms. The implementation of the Brazilian Climate and Health Observatory (C&HO) is described, and two stages are defined: (i) the requirement analysis and negotiation stage that identified the national and regional institutional players and their roles as data producers/users; and (ii) thematic health-related workshops that reviewed water-related diseases, vector-borne diseases, extreme climate events, and health problems derived from forest fires. The C&HO is an example of making information on climate and health available through an Internet site where data from different origins can be accessed on a common platform. Complex queries are made by users and can be executed over multiple sites, geographically distributed, with all technical details hidden from the end user. At this stage of the C&HO prototype, alongside the queries, users can also produce semi-qualitative graphs and maps. A multi-scale approach was developed using the platform by setting up sentinel sites. Building a successful observatory is a participatory process that involves choosing indicators, data sources, information technology, and languages to best reach different audiences, such as researchers, citizens, public health professionals, and decisionmakers.

Modelling the spatial and seasonal distribution of suitable habitats of schistosomiasis intermediate host snails using Maxent in Ndumo area, KwaZulu-Natal Province, South Africa.

BACKGROUND: Schistosomiasis is a snail-borne disease endemic in sub-Saharan Africa transmitted by freshwater snails. The distribution of schistosomiasis coincides with that of the intermediate hosts as determined by climatic and environmental factors. The aim of this paper was to model the spatial and seasonal distribution of suitable habitats for Bulinus globosus and Biomphalaria pfeifferi snail species (intermediate hosts for Schistosoma haematobium and Schistosoma mansoni, respectively) in the Ndumo area of uMkhanyakude district, South Africa. METHODS: Maximum Entropy (Maxent) modelling technique was used to predict the distribution of suitable habitats for B. globosus and B. pfeifferi using presence-only datasets with ≥ 5 and ≤ 12 sampling points in different seasons. Precipitation, maximum and minimum temperatures, Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), pH, slope and Enhanced Vegetation Index (EVI) were the background variables in the Maxent models. The models were validated using the area under the curve (AUC) and omission rate. RESULTS: The predicted suitable habitats for intermediate snail hosts varied with seasons. The AUC for models in all seasons ranged from 0.71 to 1 and the prediction rates were between 0.8 and 0.9. Although B. globosus was found at more localities in the Ndumo area, there was also evidence of cohabiting with B. pfiefferi at some of the locations. NDWI had significant contribution to the models in all seasons. CONCLUSION: The Maxent model is robust in snail habitat suitability modelling even with small dataset of presence-only sampling sites. Application of the methods and design used in this study may be useful in developing a control and management programme for schistosomiasis in the Ndumo area.

An observatory to gather and disseminate information on the health-related effects of environmental and climate change / Un observatorio para recopilar y difundir información sobre los efectos del cambio ambiental y climático relacionados con la salud

This report sought to critically examine proposals, potentials, and challenges of environmental health observatories with an emphasis on climate change processes. A critical review of existing environmental health observatories was performed, examining their purposes, potential audiences, and technological platforms. The implementation of the Brazilian Climate and Health Observatory (C&HO) is described, and two stages are defined: (i) the requirement analysis and negotiation stage that identified the national and regional institutional players and their roles as data producers/users; and (ii) thematic health-related workshops that reviewed water-related diseases, vector-borne diseases, extreme climate events, and health problems derived from forest fires. The C&HO is an example of making information on climate and health available through an Internet site where data from different origins can be accessed on a common platform. Complex queries are made by users and can be executed over multiple sites, geographically distributed, with all technical details hidden from the end user. At this stage of the C&HO prototype, alongside the queries, users can also produce semi-qualitative graphs and maps. A multi-scale approach was developed using the platform by setting up sentinel sites. Building a successful observatory is a participatory process that involves choosing indicators, data sources, information technology, and languages to best reach different audiences, such as researchers, citizens, public health professionals, and decisionmakers.

Este informe tiene el propósito de examinar críticamente las propuestas, las posibilidades y los retos de los observatorios de salud ambiental con especial interés en los procesos del cambio climático. Se realizó un examen crítico de los observatorios de salud ambiental existentes, para lo cual se evaluó su finalidad, los posibles destinatarios y las plataformas tecnológicas. En este informe se describe la ejecución del Observatorio Nacional de Clima y Salud del Brasil dividida en dos fases: 1) la fase de análisis y negociación de los requisitos, cuyo fin fue definir las instituciones nacionales y regionales que tendrían a su cargo la producción y el aprovechamiento de los datos; y 2) los talleres celebrados sobre temas de salud acerca de las enfermedades relacionadas con el agua, las enfermedades transmitidas por vectores, los acontecimientos climáticos extremos y los problemas de salud derivados de los incendios forestales. El Observatorio es un ejemplo de plataforma de internet que ofrece información y datos sobre el clima y la salud provenientes de diferentes fuentes. Los usuarios pueden plantear consultas complejas en varios sitios, localizados en distintas zonas geográficas y todos los detalles técnicos se mantienen ocultos para el usuario. En esta fase de prototipo del Observatorio es posible generar gráficos y mapas semicualitativos junto a las consultas. Además, la plataforma permitió generar una estrategia de varias escalas a través del establecimiento de centros centinela. La creación de un observatorio eficaz es un proceso participativo que comprende la elección de los indicadores, las fuentes de los datos, la tecnología de la información y los idiomas que serán empleados con el propósito de llegar a diferentes públicos, como investigadores, ciudadanos, profesionales de la salud pública y encargados de adoptar las decisiones.

Examining the relationship between environmental factors and conflict in pastoralist areas of East Africa.

The eastern Africa region has long been known for recurring drought, prolonged civil war and frequent pastoral conflicts. Several researchers have suggested that environmental factors can trigger conflicts among pastoralist communities, but quantitative support for this hypothesis is lacking. Here we use 29years of georeferenced precipitation and Normalized Difference Vegetation Index (NDVI) data to evaluate long term trends in scarcity of water and forage for livestock, and then ask whether these environmental stressors have any predictive power with respect to the location and timing of 11years of conflict data based on Armed Conflict Location and Event Data Project (ACLED) and Uppsala Conflict Data Program (UCDP). Results indicate that environmental stressors were only partly predictive of conflict events. To better understand the drivers behind conflict, the contribution of other potential stressors to conflict need to be systematically quantified and be taken into consideration.

Molecular detection of leptospiral DNA in environmental water on St. Kitts.

Leptospirosis is an important waterborne zoonotic disease caused by pathogenic Leptospira. The pathogen is maintained in a population due to chronic colonization and shedding from renal tubules of domestic and wild animals. Humans and other animals become infected when they come in contact with urine from infected animals, either directly or through urine-contaminated surface water. In this study, we screened environmental water on the island of St. Kitts by using a TaqMan based real time quantitative polymerase chain reaction (qPCR) targeting a pathogen specific leptospiral gene, lipl32. Our results indicate that around one-fifth of tested water sources have detectable leptospiral DNA.

Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger.

BACKGROUND: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. OBJECTIVES: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. DATA AND METHODS: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. RESULTS: At the national level, a model using early incidence in December and averaged November-December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41). CONCLUSIONS: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.

Land cover change interacts with drought severity to change fire regimes in Western Amazonia.

Fire is becoming a pervasive driver of environmental change in Amazonia and is expected to intensify, given projected reductions in precipitation and forest cover. Understanding of the influence of post-deforestation land cover change on fires in Amazonia is limited, even though fires in cleared lands constitute a threat for ecosystems, agriculture, and human health. We used MODIS satellite data to map burned areas annually between 2001 and 2010. We then combined these maps with land cover and climate information to understand the influence of land cover change in cleared lands and dry-season severity on fire occurrence and spread in a focus area in the Peruvian Amazon. Fire occurrence, quantified as the probability of burning of individual 232-m spatial resolution MODIS pixels, was modeled as a function of the area of land cover types within each pixel, drought severity, and distance to roads. Fire spread, quantified as the number of pixels burned in 3 × 3 pixel windows around each focal burned pixel, was modeled as a function of land cover configuration and area, dry-season severity, and distance to roads. We found that vegetation regrowth and oil palm expansion are significantly correlated with fire occurrence, but that the magnitude and sign of the correlation depend on drought severity, successional stage of regrowing vegetation, and oil palm age. Burning probability increased with the area of nondegraded pastures, fallow, and young oil palm and decreased with larger extents of degraded pastures, secondary forests, and adult oil palm plantations. Drought severity had the strongest influence on fire occurrence, overriding the effectiveness of secondary forests, but not of adult plantations, to reduce fire occurrence in severely dry years. Overall, irregular and scattered land cover patches reduced fire spread but irregular and dispersed fallows and secondary forests increased fire spread during dry years. Results underscore the importance of land cover management for reducing fire proliferation in this landscape. Incentives for promoting natural regeneration and perennial crops in cleared lands might help to reduce fire risk if those areas are protected against burning in early stages of development and during severely dry years.

Long-lasting transition toward sustainable elimination of desert malaria under irrigation development.

In arid areas, people living in the proximity of irrigation infrastructure are potentially exposed to a higher risk of malaria due to changes in ecohydrological conditions that lead to increased vector abundance. However, irrigation provides a pathway to economic prosperity that over longer time scales is expected to counteract these negative effects. A better understanding of this transition between increased malaria risk and regional elimination, in particular whether it is slow or abrupt, is relevant to sustainable development and disease management. By relying on space as a surrogate for stages of time, we investigate this transition in a semidesert region of India where a megairrigation project is underway and expected to cover more than 1,900 million hectares and benefit around 1 million farmers. Based on spatio-temporal epidemiological cases of Plasmodium vivax malaria and land-use irrigation from remote sensing sources, we show that this transition is characterized by an enhanced risk in areas adjacent to the trunk of the irrigation network, despite a forceful and costly insecticide-based control. Moreover, this transition between climate-driven epidemics and sustained low risk has already lasted a decade. Given the magnitude of these projects, these results suggest that increased health costs have to be planned for over a long time horizon. They further highlight the need to integrate assessments of both health and environmental impacts to guide adaptive mitigation strategies. Our results should help to define and track these transitions in other arid parts of the world subjected to similar tradeoffs.

Effects of increase in temperature and open water on transmigration and access to health care by the Nenets reindeer herders in northern Russia.

BACKGROUND: The indigenous Nenets reindeer herders in northern Russia annually migrate several hundred kilometers between summer and winter pastures. In the warming climate, ice-rich permafrost and glaciers are being significantly reduced and will eventually disappear from parts of the Arctic. The emergent changes in hydrological cycles have already led to substantial increases in open water that stays unfrozen for longer periods of time. This environmental change has been reported to compromise the nomadic Nenets' traditional way of life because the presence of new water in the tundra reduces the Nenets' ability to travel by foot, sled, or motor vehicle from the summer transitory tundra campsites in order to access healthcare centers in villages. New water can also impede their access to family and community at other herder camps and in the villages. Although regional and global models predicting hydrologic changes due to climate changes exist, the spatial resolution of these models is too coarse for studying how increases in open water affect health and livelihoods. To anticipate the full health impact of hydrologic changes, the current gap between globally forecasted scenarios and locally forecasted hydrologic scenarios needs to be bridged. OBJECTIVES: We studied the effects of the autumn temperature anomalies and increases in open water on health care access and transmigration of reindeer herders on the Kanin Peninsula. DESIGN: Correlational and time series analyses were completed. METHODS: The study population consisted of 370 full-time, nomadic reindeer herders. We utilized clinical visit records, studied surface temperature anomalies during autumn migrations, and used remotely sensed imagery to detect water bodies. Spearman correlation was used to measure the relationship between temperature anomalies and the annual arrival of the herders at the Nes clinic for preventive and primary care. Piecewise regression was used to model change in mean autumnal temperature anomalies over time. We also created a water body product to detect inter-annual changes in water area. RESULTS: Correlation between arrivals to the Nes clinic and temperature anomalies during the fall transmigration (1979-2011) was r = 0.64, p = 0.0004; 95% CI (0.31; 0.82). Regression analysis estimated that mean temperature anomalies during the fall migration in September-December were stochastically stationary pre-1991 and have been rising significantly (p < 0.001) since then. The rate of change was estimated at +0.1351°C/year, SE = 0.0328, 95% CI (+0.0694, +0.2007). The amount of detected water fluctuated significantly interannually (620-800 km(2)). CONCLUSIONS: Later arrival of freezing temperatures in the autumn followed by the earlier spring thaws and more open water delay transmigration and reduce herders' access to health care. The recently observed delays in arrival to the clinic are likely related to the warming trend and to concomitant hydrologic changes.

Climate information for public health: the role of the IRI climate data library in an integrated knowledge system.

Public health professionals are increasingly concerned about the potential impact of climate variability and change on health outcomes. Protecting public health from the vagaries of climate requires new working relationships between the public health sector and the providers of climate data and information. The Climate Information for Public Health Action initiative at the International Research Institute for Climate and Society (IRI) is designed to increase the public health community's capacity to understand, use and demand appropriate climate data and climate information to mitigate the public health impacts of the climate. Significant challenges to building the capacity of health professionals to use climate information in research and decision-making include the difficulties experienced by many in accessing relevant and timely quality controlled data and information in formats that can be readily incorporated into specific analysis with other data sources. We present here the capacities of the IRI climate data library and show how we have used it to build an integrated knowledge system in the support of the use of climate and environmental information in climate-sensitive decision-making with respect to health. Initiated as an aid facilitating exploratory data analysis for climate scientists, the IRI climate data library has emerged as a powerful tool for interdisciplinary researchers focused on topics related to climate impacts on society, including health.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa.

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

Rapid increase in ownership and use of long-lasting insecticidal nets and decrease in prevalence of malaria in three regional States of ethiopia (2006-2007).

Following recent large scale-up of malaria control interventions in Ethiopia, this study aimed to compare ownership and use of long-lasting insecticidal nets (LLIN), and the change in malaria prevalence using two population-based household surveys in three regions of the country. Each survey used multistage cluster random sampling with 25 households per cluster. Household net ownership tripled from 19.6% in 2006 to 68.4% in 2007, with mean LLIN per household increasing from 0.3 to 1.2. Net use overall more than doubled from 15.3% to 34.5%, but in households owning LLIN, use declined from 71.7% to 48.3%. Parasitemia declined from 4.1% to 0.4%. Large scale-up of net ownership over a short period of time was possible. However, a large increase in net ownership was not necessarily mirrored directly by increased net use. Better targeting of nets to malaria-risk areas and sustained behavioural change communication are needed to increase and maintain net use.

Individual, household and environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia.

We assessed malaria infection in relation to age, altitude, rainfall, socio-economic factors and coverage of control measures in a representative sample of 11437 people in Amhara, Oromia and SNNP regions of Ethiopia in December 2006-January 2007. Surveys were conducted in 224 randomly selected clusters of 25 households (overall sample of 27884 people in 5708 households). In 11538 blood slides examined from alternate households (83% of those eligible), malaria prevalence in people of all ages was 4.1% (95% CI 3.4-4.9), with 56.5% of infections being Plasmodium falciparum. At least one mosquito net or one long-lasting insecticidal net (LLIN) was present in 37.0% (95% CI 31.1-43.3) and 19.6% (95% CI 15.5-24.5) of households, respectively. In multivariate analysis (n=11437; 82% of those eligible), significant protective factors were: number of LLINs per household (odds ratio [OR] (per additional net)=0.60; 95% CI 0.40-0.89), living at higher altitude (OR (per 100 m)=0.95; 95% CI 0.90-1.00) and household wealth (OR (per unit increase in asset index)=0.79; 95% CI 0.66-0.94). Malaria prevalence was positively associated with peak monthly rainfall in the year before the survey (OR (per additional 10 mm rain)=1.10; 95% CI 1.03-1.18). People living above 2000 m and people of all ages are still at significant risk of malaria infection.

Effectiveness of malaria control during changing climate conditions in Eritrea, 1998-2003.

OBJECTIVE: To assess the effectiveness of impregnated mosquito nets, indoor residual spraying and larval control relative to the impacts of climate variability in the decline of malaria cases in Eritrea. METHODS: Monthly data on clinical malaria cases by subzoba (district) in three zobas (zones) of Eritrea for 1998-2003 were used in Poisson regression models to determine whether there is statistical evidence for reduction in cases by DDT, malathion, impregnated nets and larval control used over the period, while analysing the effects of satellite-derived climate variables in the same geographic areas. RESULTS: Both indoor residual spraying (with DDT or malathion) and impregnated nets were independently and significantly negatively associated with reduction in malaria cases, as was larval control in one zoba. Malaria cases were significantly positively related to differences in current and previous months' vegetation (NDVI) anomalies. The relationship to rainfall differences 2 and 3 months previously was also significant, but the direction of the effect varied by zoba. Standardized regression coefficients indicated a greater effect of climate in the zoba with less intense malaria transmission. CONCLUSION: The results support the view that both indoor residual spraying and impregnated nets have been independently effective against malaria, and that larval control was also effective in one area. Thus climate, while significant, is not the only explanation for the recent decline in malaria cases in Eritrea. If appropriate statistical approaches are used, routine surveillance data from cases attending health facilities can be useful for assessing control programme success and providing estimates of the effectiveness of individual control measures. Effectiveness estimates suitable for use in cost-effectiveness analysis have been obtained.

Malaria stratification, climate, and epidemic early warning in Eritrea.

Eritrea has a successful malaria control program, but it is still susceptible to devastating malaria epidemics. Monthly data on clinical malaria cases from 242 health facilities in 58 subzobas (districts) of Eritrea from 1996 to 2003 were used in a novel stratification process using principal component analysis and nonhierarchical clustering to define five areas with distinct malaria intensity and seasonality patterns, to guide future interventions and development of an epidemic early warning system. Relationships between monthly clinical malaria incidence by subzoba and monthly climate data from several sources, and with seasonal climate forecasts, were investigated. Remotely sensed climate data were averaged over the same subzoba geographic administrative units as the malaria cases. Although correlation was good between malaria anomalies and actual rainfall from ground stations (lagged by 2 months), the stations did not have sufficiently even coverage to be widely useful. Satellite derived rainfall from the Climate Prediction Center Merged Analysis of Precipitation was correlated with malaria incidence anomalies, with a lead time of 2-3 months. NDVI anomalies were highly correlated with malaria incidence anomalies, particularly in the semi-arid north of the country and along the northern Red Sea coast, which is a highly epidemic-prone area. Eritrea has 2 distinct rainy seasons in different parts of the country. The seasonal forecasting skill from Global Circulation Models for the June/July/August season was low except for the Eastern border. For the coastal October/November/December season, forecasting skill was good only during the 1997-1998 El Niño event. For epidemic control, shorter-range warning based on remotely sensed rainfall estimates and an enhanced epidemic early-detection system based on data derived for this study are needed.

Perspectives on using remotely-sensed imagery in predictive veterinary epidemiology and global early warning systems.

Recent disease epidemics and their spread around the world have illustrated the weaknesses of disease surveillance and early warning systems (EWS), both at national and international levels. These diseases continuously threaten the livestock sector on a worldwide basis, some with major public health impact. EWS and accurate forecasting of new outbreaks of epidemic livestock diseases that may also affect wildlife, and the capacity for spread of such diseases to new areas is an essential pre-requisite to their effective containment and control. Because both the geographical and seasonal distribution of many infectious diseases are linked to climate, the possibility of using climaterelated environmental factors as predictive indicators, in association with regular disease surveillance activities, has proven to be relevant when establishing EWS for climate-related diseases. This article reviews the growing importance of using geographical information systems in predictive veterinary epidemiology and its integration into EWS, with a special focus on Rift Valley fever. It shows that, once fully validated in a country or region, this technology appears highly valuable and could play an increasing role in forecasting major epidemics, providing lead time to national veterinary services to take action to mitigate the impact of the disease in a cost-effective manner.