Barriers to cleaning of shared latrines in slums of Addis Ababa, Ethiopia

Shared latrines and other shared sanitation facilities are vital for communities that lack private latrines. However, the cleanliness of shared latrines continues to be a problem in sub-Saharan Africa, including slums of Addis Ababa, Ethiopia. Investigating the barriers to cleaning of shared latrine...

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Bibliographic Details
Published in:PloS one 2022-03, Vol.17 (3), p.e0263363-e0263363
Main Authors: Hailu, Kidist, Alemu, Zewdie Aderaw, Adane, Metadel
Format: Article
Language:English
Subjects:
Biology and Life Sciences
Bivariate analysis
Case-Control Studies
Cleaning
Cross-Sectional Studies
Data collection
Ecology and Environmental Sciences
Ethiopia
Ghettos
Health sciences
Households
Humans
Latrines
Locking
Management
Medicine and Health Sciences
Physical Sciences
Poverty Areas
Privacy
Regression analysis
Regression models
Research and Analysis Methods
Sanitation
Sanitation facilities
Slabs
Slums
Social Sciences
Superstructures
Toilet Facilities
Urbanization
Ventilation
Water
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Summary:Shared latrines and other shared sanitation facilities are vital for communities that lack private latrines. However, the cleanliness of shared latrines continues to be a problem in sub-Saharan Africa, including slums of Addis Ababa, Ethiopia. Investigating the barriers to cleaning of shared latrines may inform the future strengthening of comprehensive sanitation programs in slums of Addis Ababa, Ethiopia. Thus, a community-based unmatched case-control study was conducted among 100 case and 200 control households that were users of shared latrines from September to November 2017 in a slum district in Addis Ababa. Cases were those who had not cleaned their shared latrines and controls were those who had cleaned their shared latrines at least once during the week prior to data collection. Data were collected using a structured questionnaire and an on-the-spot-observational checklist and analyzed using bivariate (crude odds ratio [COD]) and multivariable (adjusted odds ratio [AOR]) unconditional logistic regression model. Variables having a p-value of less than 0.25 from the bivariate logistic regression analysis were retained into multivariable analysis. From the multivariable analysis, variables with p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0263363