Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Population Health Metrics

Review

Catherine Linard¹  Greg Yetman²  Susana Adamo²  Clara R Burgert³  Andrew J Tatem⁴  Deepa Pindolia⁵  Marcia Castro⁶  Livia Montana⁶  Gunter Fink⁶  Nita Bharti⁷  Abdisalan M Noor⁸  Audrey Dorelien⁹  Mendelsohn John^1,10  Deborah Balk^1,11  Andrew Nelson^1,12  Mark R Montgomery^1,13 

[1] Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium;Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Brussels, Belgium;Center for International Earth Science Information Network (CIESIN), Columbia University, New York, USA;Demographic and Health Surveys,ICF International, International Health and Development Division, Washington DC, USA;Department of Geography, University of Florida, Gainesville, USA;Emerging Pathogens Institute, University of Florida, Gainesville, USA;Fogarty International Center, National Institutes of Health, Bethesda, USA;Department of Geography, University of Florida, Gainesville, USA;Emerging Pathogens Institute, University of Florida, Gainesville, USA;Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI Wellcome Trust Research Programme, University of Oxford, Nairobi, Kenya;Department of Global Health and Population, Harvard School of Public Health, Boston, USA;Ecology and Evolutionary Biology, Princeton University, Princeton, USA;Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI Wellcome Trust Research Programme, University of Oxford, Nairobi, Kenya;Office of Population Research and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, USA;Research and Information Services of Namibia, Research and Information Services of Namibia, Windhoek, Namibia;School of Public Affairs, Baruch College, City University, New York, USA;The International Rice Research Institute, The International Rice Research Institute, Los Banos, Philippines;The Population Council, The Population Council, New York, USA;Department of Economics, Stony Brook University, New York, USA;

关键词: Population;    Epidemiology;    Demography;    Disease mapping;   

DOI  :  10.1186/1478-7954-10-8

 received in 2011-10-28, accepted in 2012-04-27,  发布年份 2012

来源: Springer

【 摘 要 】

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

【 授权许可】

CC BY   
© Tatem et al.; licensee BioMed Central Ltd 2012