Estimating The Incidence Of Japanese Encephalitis In Divisions Of Bangladesh

Type Journal Article - Public Health Theses
Title Estimating The Incidence Of Japanese Encephalitis In Divisions Of Bangladesh
Author(s)
Publication (Day/Month/Year) 2014
URL http://elischolar.library.yale.edu/cgi/viewcontent.cgi?article=1250&context=ysphtdl
Abstract
Japanese Encephalitis (JE) is a mosquito-borne viral disease responsible for 30,000 to
50,000 reported cases and up to 15,000 reported deaths every year. The disease is a significant
public health concern in Bangladesh, but there is no regular surveillance or immunization for JE
in the country. The objective of this study is to determine whether a practical model to estimate
the incidence of JE in Bangladesh can be developed using data on population, environmental
characteristics, and/or vector distribution for JE-endemic countries in Asia and the Western
Pacific. Information on JE incidence, area, land cover, climate, population characteristics, land
elevation, and distribution of mosquito vectors was collected for JE-endemic areas. Sources of
the information included population and agricultural censuses, the Food and Agricultural
Organization of the United Nations, the WHO, WorldClim, the CIA World Factbook, and
mosquitomap.org. Generalized linear models examined the association between the variables
and the outcome of Japanese Encephalitis. The best model was used to estimate the incidence of
JE in each division of Bangladesh. The most statistically significant model used a negative
binomial distribution and included variables for population density (p=.0052), mean annual
temperature (p<.0001), annual range of temperatures (p<.0001), and mean temperature of the
warmest quarter (p<.0001), with the population size as an offset variable. The estimated
incidence in each division of Bangladesh ranged from 2.6 to 5.9 cases per 100,000 population.
The division with the highest risk for JE was Rajshahi. A pilot vaccination program in that
division may be more cost-effective than in other divisions. Limitations in data quality may have
hindered the utility of several variables, so active case-finding may be useful for validating the
model

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