Limits...
The effects of spatial population dataset choice on estimates of population at risk of disease.

Tatem AJ, Campiz N, Gething PW, Snow RW, Linard C - Popul Health Metr (2011)

Bottom Line: Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling.In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography, University of Florida, Gainesville, USA. andy.tatem@gmail.com.

ABSTRACT

Background: The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods: The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results: The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions: Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

No MeSH data available.


Related in: MedlinePlus

The spatial distribution of P. falciparum malaria PfPR2-10 predictions stratified by endemicity class. They are categorized as low risk PfPR2-10 <5%, light red; intermediate risk PfPR2-10 = 5% to 40%, medium red; and high risk PfPR2-10 >40%, dark red. The map shows the class to which PfPR2-10 has the highest predicted probability of membership. The rest of the land area was defined as unstable risk (medium gray areas, where PfAPI = 0.1 per 1,000 pa) or no risk (light gray).
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Figure 3: The spatial distribution of P. falciparum malaria PfPR2-10 predictions stratified by endemicity class. They are categorized as low risk PfPR2-10 <5%, light red; intermediate risk PfPR2-10 = 5% to 40%, medium red; and high risk PfPR2-10 >40%, dark red. The map shows the class to which PfPR2-10 has the highest predicted probability of membership. The rest of the land area was defined as unstable risk (medium gray areas, where PfAPI = 0.1 per 1,000 pa) or no risk (light gray).

Mentions: The Malaria Atlas Project has recently published revised global limits of unstable and stable P. falciparum infection risk [24] and a modeled, mapped distribution of the intensity of P. falciparum within the stable margins of transmission based upon infection prevalence among children aged 2 to 10 years (PfPR2-10) [25]. In brief, data on national case reporting, national and international medical intelligence, climate, and aridity were used to define conservatively the margins of stable and unstable P. falciparum transmission [24]. Stable malaria transmission was assumed to represent a minimum average of 1 clinical case per 10,000 population per annum (pa) in a given administrative unit. Unstable malaria transmission was used to define areas where transmission was biologically plausible and/or had been documented but where incidence was likely to be less than 1 case per 10,000 population pa. In Africa, this was largely in areas where aridity limits the survival of larvae and causes desiccation of adult vectors. Finally, no transmission was assumed where assembled intelligence stated no malaria risk because (1) national reporting systems had, over several years, not reported a single P. falciparum clinical case, or (2) where temperatures were too low for sporogony to complete within the average lifespan of the local dominant vector species. Within the stable transmission margins, empirical community survey data on parasite prevalence were assembled and geolocated to provide the basis for an urban-rural and sample-size-adjusted geospatial model within a Bayesian framework to interpolate a continuous space-time posterior prediction of PfPR2-10 for every 5 × 5 km pixel for the year 2007 [25]. This model also generated classified output that assigned each pixel to one of four malaria endemicity classes: malaria-free or unstable, PfPR2-10 <5%; PfPR2-10 = 5% to 40%; and PfPR2-10 >40% (Figure 3). These classifications of stable transmission correspond to ranges of PfPR that have been proposed in the selection of suites of interventions at scale to reach control targets at different time periods [26,27].


The effects of spatial population dataset choice on estimates of population at risk of disease.

Tatem AJ, Campiz N, Gething PW, Snow RW, Linard C - Popul Health Metr (2011)

The spatial distribution of P. falciparum malaria PfPR2-10 predictions stratified by endemicity class. They are categorized as low risk PfPR2-10 <5%, light red; intermediate risk PfPR2-10 = 5% to 40%, medium red; and high risk PfPR2-10 >40%, dark red. The map shows the class to which PfPR2-10 has the highest predicted probability of membership. The rest of the land area was defined as unstable risk (medium gray areas, where PfAPI = 0.1 per 1,000 pa) or no risk (light gray).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045911&req=5

Figure 3: The spatial distribution of P. falciparum malaria PfPR2-10 predictions stratified by endemicity class. They are categorized as low risk PfPR2-10 <5%, light red; intermediate risk PfPR2-10 = 5% to 40%, medium red; and high risk PfPR2-10 >40%, dark red. The map shows the class to which PfPR2-10 has the highest predicted probability of membership. The rest of the land area was defined as unstable risk (medium gray areas, where PfAPI = 0.1 per 1,000 pa) or no risk (light gray).
Mentions: The Malaria Atlas Project has recently published revised global limits of unstable and stable P. falciparum infection risk [24] and a modeled, mapped distribution of the intensity of P. falciparum within the stable margins of transmission based upon infection prevalence among children aged 2 to 10 years (PfPR2-10) [25]. In brief, data on national case reporting, national and international medical intelligence, climate, and aridity were used to define conservatively the margins of stable and unstable P. falciparum transmission [24]. Stable malaria transmission was assumed to represent a minimum average of 1 clinical case per 10,000 population per annum (pa) in a given administrative unit. Unstable malaria transmission was used to define areas where transmission was biologically plausible and/or had been documented but where incidence was likely to be less than 1 case per 10,000 population pa. In Africa, this was largely in areas where aridity limits the survival of larvae and causes desiccation of adult vectors. Finally, no transmission was assumed where assembled intelligence stated no malaria risk because (1) national reporting systems had, over several years, not reported a single P. falciparum clinical case, or (2) where temperatures were too low for sporogony to complete within the average lifespan of the local dominant vector species. Within the stable transmission margins, empirical community survey data on parasite prevalence were assembled and geolocated to provide the basis for an urban-rural and sample-size-adjusted geospatial model within a Bayesian framework to interpolate a continuous space-time posterior prediction of PfPR2-10 for every 5 × 5 km pixel for the year 2007 [25]. This model also generated classified output that assigned each pixel to one of four malaria endemicity classes: malaria-free or unstable, PfPR2-10 <5%; PfPR2-10 = 5% to 40%; and PfPR2-10 >40% (Figure 3). These classifications of stable transmission correspond to ranges of PfPR that have been proposed in the selection of suites of interventions at scale to reach control targets at different time periods [26,27].

Bottom Line: Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling.In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography, University of Florida, Gainesville, USA. andy.tatem@gmail.com.

ABSTRACT

Background: The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods: The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results: The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions: Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

No MeSH data available.


Related in: MedlinePlus