Date of Award

January 2014

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

James Childs

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 World Health Organization, 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=.01), mean annual temperature (p<.01), annual range of temperatures (p<.01), and mean temperature of the warmest quarter (p<.01), 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.

Comments

This is an Open Access Thesis.

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