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Biomedical subjects

S I Hay

Publications and source records attributed to S I Hay.

At least 19 recordsLinked to original sources

Global environmental data for mapping infectious disease distribution.

This contribution documents the satellite data archives, data processing methods and temporal Fourier analysis (TFA) techniques used to create the remotely sensed datasets on the DVD distributed with this volume. The aim is to provide a detailed reference guide to the genesis of the data, rather than a standard review. These remotely sensed data cover the entire globe at either 1 x 1 or 8 x 8 km spatial resolution. We briefly evaluate the relationships between the 1 x 1 and 8 x 8 km global TFA products to explore their inter-compatibility. The 8 x 8 km TFA surfaces are used in the mapping procedures detailed in the subsequent disease mapping reviews, since the 1 x 1 km products have been validated less widely. Details are also provided on additional, current and planned sensors that should be able to provide continuity with these environmental variable surfaces, as well as other sources of global data that may be used for mapping infectious disease.

Communicable Diseases↗

Determining global population distribution: methods, applications and data.

Evaluating the total numbers of people at risk from infectious disease in the world requires not just tabular population data, but data that are spatially explicit and global in extent at a moderate resolution. This review describes the basic methods for constructing estimates of global population distribution with attention to recent advances in improving both spatial and temporal resolution. To evaluate the optimal resolution for the study of disease, the native resolution of the data inputs as well as that of the resulting outputs are discussed. Assumptions used to produce different population data sets are also described, with their implications for the study of infectious disease. Lastly, the application of these population data sets in studies to assess disease distribution and health impacts is reviewed. The data described in this review are distributed in the accompanying DVD.

Communicable Diseases↗

Defining the global spatial limits of malaria transmission in 2005.

There is no accurate contemporary global map of the distribution of malaria. We show how guidelines formulated to advise travellers on appropriate chemoprophylaxis for areas of reported Plasmodium falciparum and Plasmodium vivax malaria risk can be used to generate crude spatial limits. We first review and amalgamate information on these guidelines to define malaria risk at national and sub-national administrative boundary levels globally. We then adopt an iterative approach to reduce these extents by applying a series of biological limits imposed by altitude, climate and population density to malaria transmission, specific to the local dominant vector species. Global areas of, and population at risk from, P. falciparum and often-neglected P. vivax malaria are presented for 2005 for all malaria endemic countries. These results reveal that more than 3 billion people were at risk of malaria in 2005.

Altitude↗

The global distribution of yellow fever and dengue.

Yellow fever has been subjected to partial control for decades, but there are signs that case numbers are now increasing globally, with the risk of local epidemic outbreaks. Dengue case numbers have also increased dramatically during the past 40 years and different serotypes have invaded new geographical areas. Despite the temporal changes in these closely related diseases, and their enormous public health impact, few attempts have been made to collect a comprehensive dataset of their spatial and temporal distributions. For this review, records of the occurrence of both diseases during the 20th century have been collected together and are used to define their climatic limits using remotely sensed satellite data within a discriminant analytical model framework. The resulting risk maps for these two diseases identify their different environmental requirements, and throw some light on their potential for co-occurrence in Africa and South East Asia.

Africa↗

Global transport networks and infectious disease spread.

Air, sea and land transport networks continue to expand in reach, speed of travel and volume of passengers and goods carried. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Three important consequences of global transport network expansion are infectious disease pandemics, vector invasion events and vector-borne pathogen importation. This review briefly examines some of the important historical examples of these disease and vector movements, such as the global influenza pandemics, the devastating Anopheles gambiae invasion of Brazil and the recent increases in imported Plasmodium falciparum malaria cases. We then outline potential approaches for future studies of disease movement, focussing on vector invasion and vector-borne disease importation. Such approaches allow us to explore the potential implications of international air travel, shipping routes and other methods of transport on global pathogen and vector traffic.

Animals↗

A global assessment of closed forests, deforestation and malaria risk.

Global environmental change is expected to affect profoundly the transmission of the parasites that cause human malaria. Amongst the anthropogenic drivers of change, deforestation is arguably the most conspicuous, and its rate is projected to increase in the coming decades. The canonical epidemiological understanding is that deforestation increases malaria risk in Africa and the Americas and diminishes it in South-east Asia. Partial support for this position is provided here, through a systematic review of the published literature on deforestation, malaria and the relevant vector bionomics. By using recently updated boundaries for the spatial limits of malaria and remotely-sensed estimates of tree cover, it has been possible to determine the population at risk of malaria in closed forest, at least for those malaria-endemic countries that lie within the main blocks of tropical forest. Closed forests within areas of malaria risk cover approximately 1.5 million km2 in the Amazon region, 1.4 million km2 in Central Africa, 1.2 million km2 in the Western Pacific, and 0.7 million km2 in South-east Asia. The corresponding human populations at risk of malaria within these forests total 11.7 million, 18.7 million, 35.1 million and 70.1 million, respectively. By coupling these numbers with the country-specific rates of deforestation, it has been possible to rank malaria-endemic countries according to their potential for change in the population at risk of malaria, as the result of deforestation. The on-going research aimed at evaluating these relationships more quantitatively, through the Malaria Atlas Project (MAP), is highlighted.

Africa, Central↗

The entomological inoculation rate and Plasmodium falciparum infection in African children.

Malaria is an important cause of global morbidity and mortality. The fact that some people are bitten more often than others has a large effect on the relationship between risk factors and prevalence of vector-borne diseases. Here we develop a mathematical framework that allows us to estimate the heterogeneity of infection rates from the relationship between rates of infectious bites and community prevalence. We apply this framework to a large, published data set that combines malaria measurements from more than 90 communities. We find strong evidence that heterogeneous biting or heterogeneous susceptibility to infection are important and pervasive factors determining the prevalence of infection: 20% of people receive 80% of all infections. We also find that individual infections last about six months on average, per infectious bite, and children who clear infections are not immune to new infections. The results have important implications for public health interventions: the success of malaria control will depend heavily on whether efforts are targeted at those who are most at risk of infection.

Adolescent↗

The influence of urbanisation on measures of Plasmodium falciparum infection prevalence in East Africa.

There is a growing interest in the effects of urbanisation in Africa on Plasmodium falciparum risks and disease outcomes. We undertook a review of published and unpublished literature to identify parasite survey data from communities in East Africa. Data were selected to represent the most reliable and contemporary estimates of infection prevalence and were categorised by urban or rural status using a number of approaches. We identified 329 spatially distinct surveys undertaken since 1980 in the sub-region of which 37 were undertaken in urban settlements and 292 in rural settlements. Overall rural settlements reported significantly higher parasite prevalence among children aged 0-14 than urban settlements (on average 10% higher infection rates; p<0.05). No urban settlements recorded parasite prevalence in excess of 75%. In areas of East Africa where climatic conditions are likely to support higher parasite transmission, the rural-urban difference was most marked. There was a significant trend towards documenting higher classes of parasite prevalence in rural compared to urban settlements (p<0.05) and the mean difference between rural and urban samples was 18% (p<0.001). These results further highlight the need to better define urban extents in Africa in order to capture the non-climatic determinants of infection and disease risk and provide a more informed approach to describing the burden of disease across the continent.

Adolescent↗

Modelling malaria risk in East Africa at high-spatial resolution.

OBJECTIVES: Malaria risk maps have re-emerged as an important tool for appropriately targeting the limited resources available for malaria control. In Sub-Saharan Africa empirically derived maps using standardized criteria are few and this paper considers the development of a model of malaria risk for East Africa. METHODS: Statistical techniques were applied to high spatial resolution remotely sensed, human settlement and land-use data to predict the intensity of malaria transmission as defined according to the childhood parasite ratio (PR) in East Africa. Discriminant analysis was used to train environmental and human settlement predictor variables to distinguish between four classes of PR risk shown to relate to disease outcomes in the region. RESULTS: Independent empirical estimates of the PR were identified from Kenya, Tanzania and Uganda (n = 330). Surrogate markers of climate recorded on-board earth orbiting satellites, population settlement, elevation and water bodies all contributed significantly to the predictive models of malaria transmission intensity in the sub-region. The accuracy of the model was increased by stratifying East Africa into two ecological zones. In addition, the inclusion of urbanization as a predictor of malaria prevalence, whilst reducing formal accuracy statistics, nevertheless improved the consistency of the predictive map with expert opinion malaria maps. The overall accuracy achieved with ecological zone and urban stratification was 62% with surrogates of precipitation and temperature being among the most discriminating predictors of the PR. CONCLUSIONS: It is possible to achieve a high degree of predictive accuracy for Plasmodium falciparum parasite prevalence in East Africa using high-spatial resolution environmental data. However, discrepancies were evident from mapped outputs from the models which were largely due to poor coverage of malaria training data and the comparable spatial resolution of predictor data. These deficiencies will only be addressed by more random, intensive small areas studies of empirical estimates of PR.

Animals↗

The accuracy of human population maps for public health application.

OBJECTIVES: Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. METHODS: The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. RESULTS: We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. CONCLUSIONS: Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.

Climate↗

Creating spatially defined databases for equitable health service planning in low-income countries: the example of Kenya.

Equity is an important criterion in evaluating health system performance. Developing a framework for equitable and effective resource allocation for health depends upon knowledge of service providers and their location in relation to the population they should serve. The last available map of health service providers in Kenya was developed in 1959. We have built a health service provider database from a variety of traditional government and opportunistic non-government sources and positioned spatially these facilities using global positioning systems, hand-drawn maps, topographical maps and other sources. Of 6674 identified service providers, 3355 (50%) were private sector, employer-provided or specialist facilities and only 39% were registered in the Kenyan Ministry of Health database during 2001. Of 3319 public service facilities supported by the Ministry of Health, missions, not-for-profit organizations and local authorities, 84% were registered on a Ministry of Health database and we were able to acquire co-ordinates for 92% of these. The ratio of public health services to population changed from 1:26,000 in 1959 to 1:9300 in 1999-2002. There were 82% of the population within 5 km of a public health facility and resident in 20% of the country. Our efforts to recreate a comprehensive, spatially defined list of health service providers has identified a number of weaknesses in existing national health management information systems, which with an increased commitment and minimal costs can be redressed. This will enable geographic information systems to exploit more fully facility-based morbidity data, population distribution and health access models to target resources and monitor the ability of health sector reforms to achieve equity in service provision.

Developing Countries↗

Measuring urbanization pattern and extent for malaria research: a review of remote sensing approaches.

Within the next 30 years, the proportion of urban dwellers will rise from under half to two thirds of the world's population. Such a shift will entail massive public health consequences, and most of this urban transition will occur in low-income regions of the world. Urban populations face very different health risks compared to those in rural areas, particularly in terms of malaria. To target effective and relevant public health interventions, the need for clear, consistent definitions of what determines urban areas and urban communities is paramount. Decision makers are increasingly seeing remote sensing as a cost-effective solution to monitoring urbanization at a range of spatial scales. This review focuses on the progress made within the field of remote sensing on mapping, monitoring, and modeling urban environments and examines existing challenges, drawbacks, and future prospects. We conclude by exploring some of the particular relevance of these issues to malaria and note that they are of more general relevance to all those interested in urban public health.

Africa↗

Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya.

Distance is a crucial feature of health service use and yet its application and utility to health care planning have not been well explored, particularly in the light of large-scale international and national efforts such as Roll Back Malaria. We have developed a high-resolution map of population-to-service access in four districts of Kenya. Theoretical physical access, based upon national targets, developed as part of the Kenyan health sector reform agenda, was compared with actual health service usage data among 1668 paediatric patients attending 81 sampled government health facilities. Actual and theoretical use were highly correlated. Patients in the larger districts of Kwale and Makueni, where access to government health facilities was relatively poor, travelled greater mean distances than those in Greater Kisii and Bondo. More than 60% of the patients in the four districts attended health facilities within a 5-km range. Interpolated physical access surfaces across districts highlighted areas of poor access and large differences between urban and rural settings. Users from rural communities travelled greater distances to health facilities than those in urban communities. The implications of planning and monitoring equitable delivery of clinical services at national and international levels are discussed.

Child↗

Malaria early warning in Kenya.

Kenya displays large spatiotemporal diversity in its climate and ecology. It follows that malaria transmission will reflect this environmental heterogeneity in both space and time. In this article, we discuss how such heterogeneity, and its epidemiological consequences, should be considered in the development of early warning systems for malaria epidemics.

Disease Outbreaks↗