Cholera in Cameroon, 2000-2012: Spatial and Temporal Analysis at the Operational (Health District) and Sub Climate Levels.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Moise C Ngwa, Song Liang, Ian T Kracalik, Lillian Morris, Jason K Blackburn, Leonard M Mbam, Simon Franky Baonga Ba Pouth, Andrew Teboh, Yang Yang, Mouhaman Arabi, Jonathan D Sugimoto, John Glenn Morris
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2016
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0005105; https://doaj.org/article/4a6a3f5c8ff5489cbd0b64c92b436001

INTRODUCTION:Recurrent cholera outbreaks have been reported in Cameroon since 1971. However, case fatality ratios remain high, and we do not have an optimal understanding of the epidemiology of the disease, due in part to the diversity of Cameroon's climate subzones and a lack of comprehensive data at the health district level. METHODS/FINDINGS:A unique health district level dataset of reported cholera case numbers and related deaths from 2000-2012, obtained from the Ministry of Public Health of Cameroon and World Health Organization (WHO) country office, served as the basis for the analysis. During this time period, 43,474 cholera cases were reported: 1748 were fatal (mean annual case fatality ratio of 7.9%), with an attack rate of 17.9 reported cases per 100,000 inhabitants per year. Outbreaks occurred in three waves during the 13-year time period, with the highest case fatality ratios at the beginning of each wave. Seasonal patterns of illness differed strikingly between climate subzones (Sudano-Sahelian, Tropical Humid, Guinea Equatorial, and Equatorial Monsoon). In the northern Sudano-Sahelian subzone, highest number of cases tended to occur during the rainy season (July-September). The southern Equatorial Monsoon subzone reported cases year-round, with the lowest numbers during peak rainfall (July-September). A spatial clustering analysis identified multiple clusters of high incidence health districts during 2010 and 2011, which were the 2 years with the highest annual attack rates. A spatiotemporal autoregressive Poisson regression model fit to the 2010-2011 data identified significant associations between the risk of transmission and several factors, including the presence of major waterbody or highway, as well as the average daily maximum temperature and the precipitation levels over the preceding two weeks. The direction and/or magnitude of these associations differed between climate subzones, which, in turn, differed from national estimates that ignored subzones differences in climate variables. ...