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

Population Health Metrics

Deborah Balk^1,10  Greg Yetman^1,13  Deepa Pindolia¹  Abdisalan M Noor²  Andrew Nelson⁶  Mark R Montgomery⁵  Livia Montana⁷  Mendelsohn John⁴  Catherine Linard^1,12  Gunter Fink⁷  Audrey Dorelien⁸  Marcia Castro⁷  Clara R Burgert^1,11  Nita Bharti³  Susana Adamo^1,13  Andrew J Tatem⁹ 

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

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

Others  :  806243
DOI  :  10.1186/1478-7954-10-8

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

【 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】

   
2012 Tatem et al.; licensee BioMed Central Ltd

【 图 表 】

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【 参考文献 】

• [1]Tatem A, Campiz N, Gething P, Snow R, Linard C: The effects of spatial population dataset choice on estimates of population at risk of disease. Popul Health Metrics 2011, 9:4. BioMed Central Full Text
• [2]Patil AP, Gething PW, Piel FB, Hay SI: Bayesian geostatistics in health cartography: the perspective of malaria. Trends Parasitol 2011, 27:246-253.
• [3]Riley S: Large-scale spatial-transmission models of infectious disease. Science 2007, 316:1298-1301.
• [4]Molesworth AM, Thomson MC, Connor SJ, Cresswell MP, Morse AP, Shears P, Hart CA, Cuevas LE: Where is the meningitis belt? Defining an area at risk of epidemic meningitis in Africa. Trans R Soc Trop Med Hyg 2002, 96:242-249.
• [5]Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, Kabaria CW, Manh BH, Elyazar IRF, Brooker SJ, et al.: World malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 2009, 6:e1000048.
• [6]Vezzulli L, Pruzzo C, Huq A, Colwell RR: Environmental reservoirs of Vibrio cholerae and their role in cholera. Environ Microbiol Rep 2010, 2:27-33.
• [7]Jones KE, Patel NG, Levy MA, Storeyguard A, Balk D, Gittleman JL, Daszak P: Global trends in emerging infectious diseases. Nature 2008, 451:990-994.
• [8]Viboud C, Bjornstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT: Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 2006, 312:447-451.
• [9]Smith DL, Guerra CA, Snow RW, Hay SI: Standardizing estimates of the Plasmodium falciparum parasite rate. Malar J 2007, 6:131. BioMed Central Full Text
• [10]Egger JR, Coleman PG: Age and clinical dengue illness. Emerg Infect Dis 2007, 13:924-925.
• [11]Miller E, Cradock-Watson JE, Pollock TM: Consequences of confirmed maternal rubella at successive stages of pregnancy. Lancet 1982, 2:781-784.
• [12]Pitzer VE, Viboud C, Simonsen L, Steiner C, Panozzo CA, Alonso WJ, Miller MA, Glass RI, Glasser JW, Parashar UD, Grenfell BT: Demographic variability, vaccination, and the spatiotemporal dynamics of rotavirus epidemics. Science 2009, 325:290-294.
• [13]Talavera A, Perez EM: Is cholera disease associated with poverty? J Infect Dev Ctries 2009, 3:408-411.
• [14]Allison SP: Malnutrition, disease and outcome. Nutrition 2000, 16:590-593.
• [15]Gething PW, Kirui VC, Alegana VA, Okiro EA, Noor AM, Snow RW: Estimating the number of paediatric fevers associated with malaria infection presenting to Africa's public health sector in 2007. PLoS Med 2010, 7:e1000301.
• [16]Soares Magalhaes RJ, Clements ACA: Mapping the risk of anaemia in preschool-age children: the contribution of malnutrition, malaria and helminth infections in West Africa. PLoS Med 2011, 8:e1000438.
• [17]Schur N, Hurlimann E, Garba A, Traore MS, Ndir O, Ratard RC, Tchuente LT, Kristensen TK, Utzinger J, Vounatsou P: Geostatistical model-based estimates of schistosomiasis prevalence among individuals aged <20 years in West Africa. PLoS Negl Trop Dis 2011, 5:e1194.
• [18]Deichmann U, Balk D, Yetman G: Transforming population data for interdisciplinary usages: from census to grid. 2001. Documentation for GPW Version 2 available only at http://sedac.ciesin.columbia.edu/plue/gpw/GPW documentation.pdf webcite
• [19]Balk DL, Deichmann U, Yetman G, Pozzi F, Hay SI, Nelson A: Determining global population distribution: methods, applications and data. Adv Parasitol 2006, 62:119-156.
• [20]Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA: LandScan: a global population database for estimating populations at risk. Photogramm Eng Remote Sens 2000, 66:849-857.
• [21]Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ: Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One 2012, 7:e31743.
• [22]United Nations Population Division: World population prospects, 2010 revision . New York: United Nations; 2010.
• [23]Gething PW, Noor AM, Gikandi PW, Ogara EAA, Hay SI, Nixon MS, Snow RW, Atkinson PM: Improving imperfect data from health management information systems in Africa using space-time geostatistics. PLoS Med 2006, 3:e271.
• [24]Health Metrics Network: Statistics save lives: Strengthening country health information systems . Geneva: WHO Health Metrics Network; 2005.
• [25]Murray CJL, Lopez AD, Wibulpolprasert S: Monitoring global health: Time for new solutions. Br Med J 2004, 329:1096-1100.
• [26]Kubiak RJ, Arinaminpathy N, McLean AR: Insights into the evolution and emergence of a novel infectious disease. PLoS Comput Biol 2010, 6:e1000947.
• [27]Brooker S, Hay SI, Bundy DA: Tools from ecology: useful for evaluating infection risk models? Trends Parasitol 2002, 18:70-74.
• [28]Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS: Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 2005, 437:209-214.
• [29]Hay SI, Okiro EA, Gething PW, Patil AP, Tatem AJ, Guerra CA, Snow RW: Estimating the global clinical burden of Plasmodium falciparum malaria in 2007. PLoS Med 2010, 7:e100029.
• [30]World Health Organization: The World Malaria Report . Geneva: World Health Organization; 2008.
• [31]Cibulskis RE, Bell D, Christophel EM, Hii J, Delacollette C, Bakyaita N, Aregawi MW: Estimating trends in the burden of malaria at country level. AmJTrop Med Hyg 2007, 77:133-137.
• [32]Linard C, Tatem AJ: Large-scale spatial population databases in infectious disease research. Int J Heal Geogr 2012, 11:7. BioMed Central Full Text
• [33]Johansson EW, Newby H, Renshaw M, Wardlaw T: Malaria and children. progress in intervention coverage . New York: United Nations Children's Fund (UNICEF)/The Roll Back Malaria Partnership (RBM); 2007.
• [34]Riedel N, Vounatsou P, Miller JM, Gosoniu L, Chizema-Kawesha E, Mukonka V, Steketee RW: Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 2010, 9:37. BioMed Central Full Text
• [35]Guerra CA, Howes RE, Patil AP, Gething PW, Van Boeckel TP, Temperley WH, Kabaria CW, Tatem AJ, Manh BH, Elyazar IRF, et al.: The international limits and population at risk of Plasmodium vivax transmission in 2009. PLoS Negl Trop Dis 2010, 4:e774.
• [36]Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, Hay SI, Snow RW: The limits and intensity of Plasmodium falciparum transmission: implications for malaria control and elimination worldwide. PLoS Med 2008, 5:e38.
• [37]Brooker S, Miguel E, Waswa P, Namunyu R, Moulin S, Guyatt H, Bundy D: The potential of rapid screening methods for Schistosoma mansoni in western Kenya. Ann Trop Med Parasitol 2001, 95:343-351.
• [38]Brooker S, Beasley M, Ndinaromtan M, Madjiouroum EM, Baboguel M, Djenguinabe E, Hay SI, Bundy DA: Use of remote sensing and a geographical information system in a national helminth control programme in Chad. Bull World Health Organ 2002, 80:783-789.
• [39]Kabatereine N, Brooker S, Tukahebwa E, Kazibwe F, Onapa A: Epidemiology and geography of Schistosoma mansoni in Uganda: implications for planning control. Trop Med Int Health 2004, 9:372.
• [40]Clements ACA, Firth S, Dembele R, Garba A, Toure S, Sacko M, Landoure A, Bosque-Oliva E, Barnett AG, Brooker S, Fenwick A: Use of Bayesian geostatistical prediction to estimate local variations in Schistosoma haematobium infection in western Africa. Bull World Health Organ 2009, 87:921-929.
• [41]Brooker SJ, Clements ACA, Hotez PJ, Hay SI, Tatem AJ, Bundy DAP, Snow RW: The co-distribution of Plasmodium falciparum and hookworm among African schoolchildren. Malar J 2006, 5:99. BioMed Central Full Text
• [42]Pullan RL, Gething PW, Smith JL, Mwandawiro CS, Sturrock HJ, Gitonga CW, Hay SI, Brooker S: Spatial modelling of soil-transmitted helminth infections in Kenya: a disease control planning tool. PLoS Negl Trop Dis 2011, 5:e958.
• [43]Brooker S, Clements AC, Bundy DA: Global epidemiology, ecology and control of soil-transmitted helminth infections. Adv Parasitol 2006, 62:221-261.
• [44]Brooker S, Hotez PJ, Bundy DA: Hookworm-related anaemia among pregnant women: a systematic review. PLoS Negl Trop Dis 2008, 2:e291.
• [45]Dellicour S, Tatem AJ, Guerra CA, Snow RW, ter Kuile FO: Quantifying the number of pregnancies at risk of malaria in 2007: a demographic study. PLoS Med 2010, 7:e1000221.
• [46]van Eijk A, Hill J, Alegana V, Kirui V, Gething P, ter Kuile F, Snow R: Coverage of malaria protection in pregnant women in sub-Saharan Africa: a synthesis and analysis of national survey data. Lancet Infect Dis 2011, 11:190-207.
• [47]Fischer E, Pahan D, Chowdhury S, Richardus J: The spatial distribution of leprosy cases during 15 years of a leprosy control program in Bangladesh: an observational study. BMC Infect Dis 2008, 8:126. BioMed Central Full Text
• [48]Kalipeni E, Zulu LC: HIV and AIDS in Africa: a geographic analysis at multiple spatial scales. GeoJournal 2010.
• [49]Chao DL, Halloran ME, Longini IM Jr: Vaccination strategies for epidemic cholera in Haiti with implications for the developing world. Proc Natl Acad Sci U S A 2011, 108:7081-7085.
• [50]Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS: Strategies for mitigating an influenza pandemic. Nature 2006, 442:448-452.
• [51]Rakowski F, Gruziel M, Bieniasz-Krywiec L, Radomski JP: Influenza epidemic spread simulation for Poland - a large scale, individual based model study. Physica A: Statistical Mechanics and its Applications 2010, 389:3149-3165.
• [52]Rao DM, Chernyakhovsky A, Rao V: Modeling and analysis of global epidemiology of avian influenza. Environ Model Softw 2009, 24:124-134.
• [53]Balcan D, Colizza V, Goncalves B, Hu H, Ramasco JJ, Vespignani A: Multiscale mobility networks and the spatial spreading of infectious diseases. Proc Natl Acad Sci 2009, 106:21484-21489.
• [54]Dye C: Health and urban living. Science 2008, 319:766-769.
• [55]United Nations Population Division: World urbanization prospects, 2009 revision. New York: United Nations; 2009.
• [56]Tatem AJ, Hay SI: Measuring urbanization pattern and extent for malaria research: a review of remote sensing approaches. J Urban Health 2004, 81:363-376.
• [57]Tatem AJ, Noor AM, Hay SI: Assessing the accuracy of satellite derived global and national urban maps in Kenya. Remote Sens Environ 2005, 96:87-97.
• [58]Schneider A, Friedl MA, Potere D: Mapping global urban areas using MODIS 500-m data: New methods and datasets based on 'urban ecoregions'. Remote Sens Environ 2010, 114:1733-1746.
• [59]Balk D, Montgomery M, McGranahan G, Kim D, Mara V, Todd M, Buettner T, Dorelien A: Mapping urban settlements and the risks of climate change in Africa, Asia and South America . In Population dynamics and climate change. Edited by Martine G, Guzman J-M, McGranahan G, Schensul D, Tacoli C. New York: UNPD; 2009:88-103.
• [60]Kim D: Econometric modeling of city population growth in developing countries . New York: State University of; 2011.
• [61]Gemperli A, Vounatsou P, Kleinschmidt I, Bagayoko M, Lengeler C, Smith T: Spatial patterns of infant mortality in Mali: the effect of malaria endemicity. Am J Epidemiol 2004, 159:64-72.
• [62]Chin B, Montana L, Basagana X: Spatial modeling of geographic inequalities in child mortality across Nepal. Health Place 2011, 17:929-936.
• [63]Elbers C, Lanjouw J, Lanjouw P: Micro-level estimation of poverty and inequality. Econometrica 2003, 71:355-386.
• [64]Prothero RM: Population movements and tropical health. Global Change and Human Health 2002, 3:20-32.
• [65]Stoddard S, Morrison A, Vazquez-Prokopec G, Paz-Soldan V, Kochel T, Kitron U, Elder J, Scott T: The role of human movement in the transmission of vector-borne pathogens. PLoS Negl Trop Dis 2010, 3:e481.
• [66]Tatem AJ, Smith DL: International population movements and regional Plasmodium falciparum malaria elimination strategies. Proc Natl Acad Sci 2010, 107:12222-12227.
• [67]Paz-Soldan V, Stoddard S, Vazquez-Prokopec G, Morrison A, Elder J, Kitron U, Kochel T, Scott T: Assessing and Maximizing the Acceptability of GPS Device Use for Studying the Role of Human Movement in Dengue Virus Transmission in Iquitos, Peru. AmJTrop Med Hyg 2010, 82:723-730.
• [68]Tatem A, Qiu Y, Smith D, Sabot O, Ali A, Moonen B: The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents. Malar J 2009, 8:287. BioMed Central Full Text