4.4 Article

An urban-to-rural continuum of malaria risk: new analytic approaches characterize patterns in Malawi

Journal

MALARIA JOURNAL
Volume 20, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12936-021-03950-5

Keywords

Urbanicity; Environmental risk; Malaria prevention; Remote sensing; Spatial analysis

Funding

  1. US Centers for Disease Control
  2. President Malaria Initiative
  3. CDC [5 U01 CI000189]
  4. Department of African and Afro-American Studies
  5. Global Public Health Program of the University of Michigan
  6. NIAID [1U19AI089683-01]

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The urban-rural designation is an important risk factor in infectious disease epidemiology, but relying on politically determined dichotomization may not capture the full complexity of driving factors. A continuous composite measure of urbanicity showed better predictive power for malaria risk compared to traditional rural/urban or population density variables. This method can provide a clearer mechanistic understanding and can be applied to other infectious disease processes in rapidly urbanizing contexts.
Background The urban-rural designation has been an important risk factor in infectious disease epidemiology. Many studies rely on a politically determined dichotomization of rural versus urban spaces, which fails to capture the complex mosaic of infrastructural, social and environmental factors driving risk. Such evaluation is especially important for Plasmodium transmission and malaria disease. To improve targeting of anti-malarial interventions, a continuous composite measure of urbanicity using spatially-referenced data was developed to evaluate household-level malaria risk from a house-to-house survey of children in Malawi. Methods Children from 7564 households from eight districts throughout Malawi were tested for presence of Plasmodium parasites through finger-prick blood sampling and slide microscopy. A survey questionnaire was administered and latitude and longitude coordinates were recorded for each household. Distances from households to features associated with high and low levels of development (health facilities, roads, rivers, lakes) and population density were used to produce a principal component analysis (PCA)-based composite measure for all centroid locations of a fine geo-spatial grid covering Malawi. Regression methods were used to test associations of the urbanicity measure against Plasmodium infection status and to predict parasitaemia risk for all locations in Malawi. Results Infection probability declined with increasing urbanicity. The new urbanicity metric was more predictive than either a governmentally defined rural/urban dichotomous variable or a population density variable. One reason for this was that 23% of cells within politically defined rural areas exhibited lower risk, more like those normally associated with urban locations. Conclusions In addition to increasing predictive power, the new continuous urbanicity metric provided a clearer mechanistic understanding than the dichotomous urban/rural designations. Such designations often ignore urban-like, low-risk pockets within traditionally rural areas, as were found in Malawi, along with rural-like, potentially high-risk environments within urban areas. This method of characterizing urbanicity can be applied to other infectious disease processes in rapidly urbanizing contexts.

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