Format

Send to

Choose Destination
Trop Med Int Health. 2016 May;21(5):675-86. doi: 10.1111/tmi.12680. Epub 2016 Mar 3.

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa.

Author information

1
Centre for Geoinformation Science, Department of Geography, Geoinformation and Meteorology, University of Pretoria, Hatfield, South Africa.
2
Earth Observation Directorate, South African National Space Agency, Pretoria, South Africa.
3
Department of Geography, University of Ibadan, Ibadan, Nigeria.
4
Centre for Environmental Study, Department of Geography, Geoinformation and Meteorology, University of Pretoria, Hatfield, South Africa.

Abstract

OBJECTIVE:

Nkomazi local municipality of South Africa is a high-risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria.

METHODS:

r software was used to statistically analyse data. Using remote sensing technology, a Landsat 8 image of 4th October 2015 was classified using object-based classification and a 5-m resolution. Spot height data were used to generate a digital elevation model of the area.

RESULTS:

A total of 60 718 malaria cases were notified across 48 health facilities in Nkomazi municipality between January 1997 and August 2015. Malaria incidence was highly associated with irrigated land (P = 0.001), water body (P = 0.011) and altitude ≤400 m (P = 0.001). The multivariate model showed that with 10% increase in the extent of irrigated areas, malaria risk increased by almost 39% in the entire study area and by almost 44% in the 2-km buffer zone of selected villages. Malaria incidence is more pronounced in the economically active population aged 15-64 and in males. Both incidence and case fatality rate drastically declined over the study period.

CONCLUSION:

A predictive model based on environmental factors would be useful in the effort towards malaria elimination by fostering appropriate targeting of control measures and allocating of resources.

KEYWORDS:

LULC; Landsat; Malaria; clasificación basada en objetos; classification basée sur l'objet; datos de uso y cobertura del suelo; elevación; elevation; environment; environnement; land use/land cover; malaria; medio ambiente; object-based classification; paludisme; remote sensing; sensores remotos; télédétection; élévation

PMID:
26914617
DOI:
10.1111/tmi.12680
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center