Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia

Tuberculosis (TB) remains a key health menace in Ethiopia and its districts. This study aimed to assess the spatial-temporal clustering of notified pulmonary TB (PTB) cases in East Gojjam Zone, Northwest Ethiopia. A retrospective study was conducted among all PTB cases reported from 2013-2019. Case...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one 2021-01, Vol.16 (1), p.e0245378-e0245378
Hauptverfasser: Asemahagn, Mulusew Andualem, Alene, Getu Degu, Yimer, Solomon Abebe
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Tuberculosis (TB) remains a key health menace in Ethiopia and its districts. This study aimed to assess the spatial-temporal clustering of notified pulmonary TB (PTB) cases in East Gojjam Zone, Northwest Ethiopia. A retrospective study was conducted among all PTB cases reported from 2013-2019. Case notification rates (CNRs) of PTB cases at Kebele (the lowest administrative unit), woreda, and zone levels were estimated. The PTB clustering was done using global Moran's I statistics on Arc GIS 10.6. We used Kulldorff SaTScan 9.6 with a discrete Poisson model to identify statistically significant spatial-temporal clustering of PTB cases at Kebele level. Similarly, a negative binomial regression analysis was used to identify factors associated with the incidence of PTB cases at kebele level. A total of 5340 (52%) smear-positive and 4928 (48%) smear-negative PTB cases were analyzed. The overall mean CNR of PTB cases at zone, woreda and Kebele levels were 58(47-69), 82(56-204), and 69(36-347) per 100,000 population, respectively. The purely spatial cluster analysis identified eight most likely clusters (one for overall and one per year for seven reporting years) and 47 secondary clusters. Similarly, the space-time scan analysis identified one most likely and seven secondary clusters. The purely temporal analysis also detected one most likely cluster from 2013-2015. Rural residence, distance from the nearest health facility, and poor TB service readiness were factors (p-value
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0245378