Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity

Abstract

Background: The efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.

Methods: First, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.

Results: Masks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.

Conclusion: The availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

Figure 1

The spatial limit of Plasmodium falciparum malaria risk defined by P. falciparum annual parasite incidence (PfAPI) with temperature and aridity masks[18]. Areas were defined as stable (dark grey areas, where PfAPI ≥ 0.1‰ pa), unstable (middle grey areas, where PfAPI < 0.1‰ pa) or no risk (light grey) [17-19]. The borders of the 87 countries defined as P. falciparum endemic are shown. The community surveys of P. falciparum prevalence conducted between 1985 and 01 March 2008 are plotted. Of the 4,887 surveys that could be geo-positioned, 4,077 fell within the predicted limits of P. falciparum malaria risk. The data shown are age-standardized (PfPR_2–10) and shown as a continuum from 0–100%. The dashed lines indicate the separation between the area defined as 'Africa+' (Africa, Yemen and Saudi Arabia) and the rest of the world used in the analyses.

Figure 2

(a) Modified GRUMP [24] masked by the limits of Pf malaria transmission [18] and projected to 2007 [27]. The red border highlights where data from Tatem et al [28] were inserted. (b) The date of the census data used in the construction of the population surface in (a), and (c) the administrative unit level of census data used in the construction of the population surface in (a).

Figure 3

The Normalized Difference Vegetation Index (NDVI) classes used in this study. The NDVI was calculated from AVHRR, then classified into NDVI < 0.350, corresponding to drier, relatively less vegetated areas and NDVI > = 0.35, corresponding to wetter, relatively more vegetated areas.

Figure 4

Steps taken in the analysis of the effects of high population densities and urbanization on PfPRs. Analyses were undertaken globally and split between Africa+ and the rest of the world.

Figure 5

Land area and number of PfPR surveys excluded by varying population density thresholds. GRUMP was reclassified using the thresholds on the x-axis, and for each threshold level, the land area below the threshold was calculated, as well as the number of PfPR surveys within this area.

Figure 6

Boxplots showing the differences in PfPRs by author-defined rural-urban survey pairs for surveys where mean annual NDVI is (a) less than 0.35 and (b) greater than or equal to 0.35 (* = p < 0.05, ** = p < 0.01).

Figure 7

Boxplots showing the differences in PfPR values by GRUMP-UE defined rural/urban assignment for cities where mean annual NDVI is (a) less than 0.35 and (b) greater than or equal to 0.35 (* = p < 0.05, ** = p < 0.01). The number of points in each class is shown in brackets below each plot.

Figure 8

Boxplots showing the differences in PfPRs by GRUMP-UE/GPW3-defined rural, peri-urban, urban survey sets for (a) all surveys in Africa+ and those where mean annual NDVI is (b) less than 0.35 and (c) greater than or equal to 0.35 (* = p < 0.05, ** = p < 0.01).

Figure 9

Peri-urban and urban extents for all malaria endemic countries within the limits of Pf malaria transmission. The extents are a combination of GRUMP urban extents and GPW thresholded population densities, defined using the MAP PfPR database.