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Towards a spatial-temporal model of prevalence of nodding syndrome and epilepsy

Ongaya, Kizito ; Ssemalullu, Paul ; Oyo, Benedict ; Maiga, Gilbert and Aturinde, Augustus LU (2019) In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 275. p.67-77
Abstract

Nodding syndrome is an emerging disease which have unknown transmission patterns and no properly established mechanisms for diagnosis leading to numerous hypothetical postulations. It has affected thousands of children in Uganda with debilitating effect and serious economic consequences. Spatial-temporal analysis may provide a quick mechanism to establish comparative understanding of the various hypotheses ascribed to nodding syndrome and any other emerging diseases with similar clinical manifestation. There is considerable suspicion that “nodding syndrome is a form of epilepsy”, a hypothesis that has hardly been investigated in literature. The aim of the study described in this paper is to establish spatial-temporal relationships... (More)

Nodding syndrome is an emerging disease which have unknown transmission patterns and no properly established mechanisms for diagnosis leading to numerous hypothetical postulations. It has affected thousands of children in Uganda with debilitating effect and serious economic consequences. Spatial-temporal analysis may provide a quick mechanism to establish comparative understanding of the various hypotheses ascribed to nodding syndrome and any other emerging diseases with similar clinical manifestation. There is considerable suspicion that “nodding syndrome is a form of epilepsy”, a hypothesis that has hardly been investigated in literature. The aim of the study described in this paper is to establish spatial-temporal relationships between ailments diagnosed as nodding syndrome and ailments diagnosed as epilepsy. An exploratory cross section survey in three districts of Northern Uganda was done. Spatial data of health centers were recorded and ArcGIS was used for display. The findings show significant spatial-temporal correlation of diagnosis reporting of nodding syndrome to epilepsy. The regression statistics overall, epilepsy significantly (p < 0.05) ex-plains about 58% of Nodding syndrome variability. The F-statistic shows a very highly significant value (p = 8.20481E-13; p < 0.05), meaning that the output of the regression is not by chance.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Emerging diseases, Geographic information system, Nodding syndrome, Spatial-temporal, Surveillance
host publication
AFRICOMM 2018: e-Infrastructure and e-Services for Developing Countries
series title
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
editor
Dioum, Ibra ; Mendy, Gervais ; Ouya, Samuel ; Thiaré, Ousmane ; ; ; and
volume
275
pages
11 pages
publisher
Springer
external identifiers
  • scopus:85064131866
ISSN
1867-8211
ISBN
9783030160418
DOI
10.1007/978-3-030-16042-5_7
language
English
LU publication?
yes
id
1a6a4d55-ce7d-4e7d-827a-50137d8be062
date added to LUP
2019-05-08 13:04:42
date last changed
2019-11-25 09:29:42
@inproceedings{1a6a4d55-ce7d-4e7d-827a-50137d8be062,
  abstract     = {<p>Nodding syndrome is an emerging disease which have unknown transmission patterns and no properly established mechanisms for diagnosis leading to numerous hypothetical postulations. It has affected thousands of children in Uganda with debilitating effect and serious economic consequences. Spatial-temporal analysis may provide a quick mechanism to establish comparative understanding of the various hypotheses ascribed to nodding syndrome and any other emerging diseases with similar clinical manifestation. There is considerable suspicion that “nodding syndrome is a form of epilepsy”, a hypothesis that has hardly been investigated in literature. The aim of the study described in this paper is to establish spatial-temporal relationships between ailments diagnosed as nodding syndrome and ailments diagnosed as epilepsy. An exploratory cross section survey in three districts of Northern Uganda was done. Spatial data of health centers were recorded and ArcGIS was used for display. The findings show significant spatial-temporal correlation of diagnosis reporting of nodding syndrome to epilepsy. The regression statistics overall, epilepsy significantly (p &lt; 0.05) ex-plains about 58% of Nodding syndrome variability. The F-statistic shows a very highly significant value (p = 8.20481E-13; p &lt; 0.05), meaning that the output of the regression is not by chance.</p>},
  author       = {Ongaya, Kizito and Ssemalullu, Paul and Oyo, Benedict and Maiga, Gilbert and Aturinde, Augustus},
  booktitle    = {AFRICOMM 2018: e-Infrastructure and e-Services for Developing Countries },
  editor       = {Dioum, Ibra and Mendy, Gervais and Ouya, Samuel and Thiaré, Ousmane},
  isbn         = {9783030160418},
  issn         = {1867-8211},
  language     = {eng},
  pages        = {67--77},
  publisher    = {Springer},
  series       = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
  title        = {Towards a spatial-temporal model of prevalence of nodding syndrome and epilepsy},
  url          = {http://dx.doi.org/10.1007/978-3-030-16042-5_7},
  doi          = {10.1007/978-3-030-16042-5_7},
  volume       = {275},
  year         = {2019},
}