Joint spatial time-series epidemiological analysis of malaria and cutaneous leishmaniasis infection

Adegboye, O.A., Al-Saghir, M., and Leung, D.H.Y. (2017) Joint spatial time-series epidemiological analysis of malaria and cutaneous leishmaniasis infection. Epidemiology and Infection, 145 (4). pp. 685-700.

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Malaria and leishmaniasis are among the two most important health problems of many developing countries especially in the Middle East and North Africa. It is common for vector-borne infectious diseases to have similar hotspots which may be attributed to the overlapping ecological distribution of the vector. Hotspot analyses were conducted to simultaneously detect the location of local hotspots and test their statistical significance. Spatial scan statistics were used to detect and test hotspots of malaria and cutaneous leishmaniasis (CL) in Afghanistan in 2009. A multivariate negative binomial model was used to simultaneously assess the effects of environmental variables on malaria and CL. In addition to the dependency between malaria and CL disease counts, spatial and temporal information were also incorporated in the model. Results indicated that malaria and CL incidence peaked at the same periods. Two hotspots were detected for malaria and three for CL. The findings in the current study show an association between the incidence of malaria and CL in the studied areas of Afghanistan. The incidence of CL disease in a given month is linked with the incidence of malaria in the previous month. Co-existence of malaria and CL within the same geographical area was supported by this study, highlighting the presence and effects of environmental variables such as temperature and precipitation. People living in areas with malaria are at increased risk for leishmaniasis infection. Local healthcare authorities should consider the co-infection problem by recommending systematic malaria screening for all CL patients.

Item ID: 53804
Item Type: Article (Research - C1)
ISSN: 1469-4409
Keywords: co-infection; cutaneous leishmaniasis; malaria; negative binomial; overdispersion; spatio-temporal
Date Deposited: 06 Jun 2018 00:44
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics @ 40%
41 ENVIRONMENTAL SCIENCES > 4101 Climate change impacts and adaptation > 410199 Climate change impacts and adaptation not elsewhere classified @ 30%
42 HEALTH SCIENCES > 4202 Epidemiology > 420202 Disease surveillance @ 30%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response) @ 100%
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