Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

ISR's Scott Page says diverse teams produce optimal results

Bound, Geronimus, et al. find estimates of decreasing longevity among low-SES whites sensitive to measures and interpretations

Thompson casts doubt on the rehabilitative intentions of prison labor

More News

Highlights

Seefeldt discusses her book Abandoned Families, Wed, March 29, 4 PM, Annenberg Auditorium

U-M participants at PAA Annual Meeting, April 27-29

Heather Ann Thompson wins Bancroft Prize for History for 'Blood in the Water'

Michigan ranks in USN&WR top-10 grad schools for sociology, public health, labor economics, social policy, social psychology

More Highlights

Next Brown Bag

Mon, April 10, 2017, noon:
Elizabeth Bruch

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

Archived Abstract of Former PSC Researcher

Tatem, Andrew, Susana Adamo, Nita Bharti, Clara Burgert, Marcia Castro, Audrey Dorelien, Gunter Fink, Catherine Linard, Mendelsohn John, Livia Montana, Mark Montgomery, Andrew Nelson, Abdisalan Noor, Deepa Pindolia, Greg Yetman, and Deborah Balk. 2012. "Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation." Population Health Metrics, 10(8).

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.

DOI:10.1186/1478-7954-10-8 (Full Text)

Public Access Link

Browse | Search : All Pubs | Next