Geographic information system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province, Laos

Abstract Background Insecticide-treated nets (ITNs) are a key intervention to control malaria. The intervention coverage varies as a consequence of geographical accessibility to remote villages and limitations of financial and human resources for the intervention. People's adherence to the intervention, i.e., proper use of ITNs, also affects malaria health outcome. The study objective is to explore the impact of the intervention coverage and people's adherence to the intervention on malaria health outcome among targeted villages in various geographic locations. Methods Geographic information system (GIS) maps were developed using the data collected in an active case detection survey in Khammouane province, Laos. The survey was conducted using rapid diagnostic tests (RDTs) and a structured questionnaire at 23 sites in the province from June to July, the rainy season, in 2005. A total of 1,711 villagers from 403 households participated in the survey. Results As indicated on the GIS maps, villages with malaria cases, lower intervention coverage, and lower adherence were identified. Although no malaria case was detected in most villages with the best access to the district center, several cases were detected in the distal villages, where the intervention coverage and adherence to the intervention remained relatively lower. Conclusion Based on the data and maps, it was demonstrated that malaria remained unevenly distributed within districts. Balancing the intervention coverage in the distal villages with the overall coverage and continued promotion of the proper use of ITNs are necessary for a further reduction of malaria cases in the province.</p

Global Distribution of Outbreaks of Water-Associated Infectious Diseases

Water is essential for maintaining life on Earth but can also serve as a media for many pathogenic organisms, causing a high disease burden globally. However, how the global distribution of water-associated infectious pathogens/diseases looks like and how such distribution is related to possible social and environmental factors remain largely unknown. In this study, we compiled a database on distribution, biology, and epidemiology of water-associated infectious diseases and collected data on population density, annual accumulated temperature, surface water areas, average annual precipitation, and per capita GDP at the global scale. From the database we extracted reported outbreak events from 1991 to 2008 and developed models to explore the association between the distribution of these outbreaks and social and environmental factors. A total of1,428 outbreaks had been reported and this number only reflected ‘the tip of the iceberg’ of the much bigger problem. We found that the outbreaks of water-associated infectious diseases are significantly correlated with social and environmental factors and that all regions are affected disproportionately by different categories of diseases. Relative risk maps are generated to show ‘hotspots’ of risks for different diseases. Despite certain limitations, the findings may be instrumental for future studies and prioritizing health resources

Estimating the malaria risk of African mosquito movement by air travel

BACKGROUND: The expansion of global travel has resulted in the importation of African Anopheles mosquitoes, giving rise to cases of local malaria transmission. Here, cases of 'airport malaria' are used to quantify, using a combination of global climate and air traffic volume, where and when are the greatest risks of a Plasmodium falciparum-carrying mosquito being importated by air. This prioritises areas at risk of further airport malaria and possible importation or reemergence of the disease. METHODS: Monthly data on climate at the World's major airports were combined with air traffic information and African malaria seasonality maps to identify, month-by-month, those existing and future air routes at greatest risk of African malaria-carrying mosquito importation and temporary establishment. RESULTS: The location and timing of recorded airport malaria cases proved predictable using a combination of climate and air traffic data. Extending the analysis beyond the current air network architecture enabled identification of the airports and months with greatest climatic similarity to P. falciparum endemic regions of Africa within their principal transmission seasons, and therefore at risk should new aviation routes become operational. CONCLUSION: With the growth of long haul air travel from Africa, the identification of the seasonality and routes of mosquito importation is important in guiding effective aircraft disinsection and vector control. The recent and continued addition of air routes from Africa to more climatically similar regions than Europe will increase movement risks. The approach outlined here is capable of identifying when and where these risks are greatest

Global warming and malaria: knowing the horse before hitching the cart

Speculations on the potential impact of climate change on human health frequently focus on malaria. Predictions are common that in the coming decades, tens – even hundreds – of millions more cases will occur in regions where the disease is already present, and that transmission will extend to higher latitudes and altitudes. Such predictions, sometimes supported by simple models, are persuasive because they are intuitive, but they sidestep factors that are key to the transmission and epidemiology of the disease: the ecology and behaviour of both humans and vectors, and the immunity of the human population. A holistic view of the natural history of the disease, in the context of these factors and in the precise setting where it is transmitted, is the only valid starting point for assessing the likely significance of future changes in climate

Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area. SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

BACKGROUND: Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. METHODOLOGY/PRINCIPAL FINDINGS: In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. CONCLUSIONS/SIGNIFICANCE: The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel

Impacts of 21st century climate change on global air pollution-related premature mortality

Climate change modulates surface concentrations of fine particulate matter (PM2.5) and ozone (O-3), indirectly affecting premature mortality attributed to air pollution. We estimate the change in global premature mortality and years of life lost (YLL) associated with changes in surface O-3 and PM2.5 over the 21st century as a result of climate change. We use a global coupled chemistry-climate model to simulate current and future climate and the effect of changing climate on air quality. Epidemiological concentration-response relationships are applied to estimate resulting changes in premature mortality and YLL. The effect of climate change on air quality is isolated by holding emissions of air pollutants constant while allowing climate to evolve over the 21st century according to a moderate projection of greenhouse gas emissions (A1B scenario). Resulting changes in 21st century climate alone lead to an increase in simulated PM2.5 concentrations globally, and to higher (lower) O-3 concentrations over populated (remote) regions. Global annual premature mortality associated with chronic exposure to PM2.5 increases by approximately 100 thousand deaths (95 % confidence interval, CI, of 66-130 thousand) with corresponding YLL increasing by nearly 900 thousand (95 % CI, 576-1,128 thousand) years. The annual premature mortality due to respiratory disease associated with chronic O-3 exposure increases by +6,300 deaths (95 % CI, 1,600-10,400). This climate penalty indicates that stronger emission controls will be needed in the future to meet current air quality standards and to avoid higher health risks associated with climate change induced worsening of air quality over populated regions.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000326944000011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesMeteorology &amp; Atmospheric SciencesSCI(E)EI17ARTICLE2239-25312