BIBLIOGRAPHY

Publications, working papers, and other research using data resources from IPUMS.

Full Citation

Title: Mapping Populations at Risk: Improving Spatial Demographic data for Infectious Disease Modeling and Metric Derivation

Citation Type: Journal Article

Publication Year: 2012

Abstract: 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 isdriving 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 affectedpopulations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during nationalcensuses 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 thosemost at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.

User Submitted?: No

Authors: Adamo, Susana; Burgert, Clara; Tatem, Andreq; Castro, Marcia; Bharti, Nita; Dorelien, Audrey

Periodical (Full): Population Health Metrics

Issue: 8

Volume: 10

Pages: 6-30

Data Collections: IPUMS USA, IPUMS International

Topics: Health

Countries:

IPUMS NHGIS NAPP IHIS ATUS Terrapop