Factors Associated with Household Water Quality in Rwandan Low-Income Households: A Cross-Sectional Analysis Público
Millsaps, Katherine (Spring 2021)
Abstract
Background: Access to safe water is a human right and is one of the focuses for public health work and policies as it is included in the Sustainable Development Goal 6.1. The consumption of poor-quality water that has been contaminated with fecal matter from humans or animals puts humans at risk for diarrheal diseases and other illnesses that are linked to waterborne pathogens.
Aim: The aim of this study is to identify the factors associated with household drinking water quality among low-income Rwandan households. This was done by describing the household and water sample characteristics and using statistical modeling to determine which factors are associated with water quality. Additionally, water quality will be explored for differences across the 5 provinces of Rwanda.
Methods: The data used in this study were collected from a cross-sectional study in May and June of 2013 through a blend of random and convenience sampling with 480 households from 120 villages. Enumerators collected data from a household survey (i.e. household information, sociodemographic, water practices) as well as a water sample from drinking water. Thermotolerant coliforms (TTC) were used as a proxy indicator for water quality. The data were analyzed using crude and multivariate log binomial and linear regression models to find positive and negative predictors for both binary and log transformed continuous TTC.
Results: From the crude models, the factors associated with poor drinking water quality at the household level were storage container mouth type, toilet facility type, water source type, water fetching time, water fetching distance, purchasing water, SES quartile, and seasonality. In the linear regression, there were four predictors of TTC: water source type, SES quartile, seasonality, and province. The multivariate log binomial regression model did not identify any statistically significant associations.
Discussion: This analysis between household and water sample characteristics with water quality at a household level supports the conclusion that these household and source factors can influence fecal contamination, especially water source type and seasonality. Additionally, there should be future studies conducted to better understand the variation in water quality across provinces.
Table of Contents
Table of Contents
1. Background 1
1.1 Global Information on Safe Water 1
1.2 Rwanda, Water and Waterborne Disease 5
Figure 1: Rwanda’s Structural Division
Map 1: Provinces of Rwanda
1.3 Factors Associated with Fecal Contamination of Household Drinking Water 8
Figure 2: Identified Fecal Contamination Pathways of Interest Impacting Household Drinking Water
1.3.1 Flooring Type 9
1.3.2 Owning Animals 9
1.3.3 Storage Container 10
1.3.4 Handwashing Practices and Facilities 10
1.3.5 Water Fetching 11
1.3.6 Toilet Facility Type 11
1.3.7 Accessing Drinking Water 12
1.3.8 Water Treatment 13
1.3.9 Water Source Type 13
1.3.10 Time Since Water Collection 14
2. Methods 14
2.1 Study Design 14
2.2 Sampling Strategy 15
Figure 3: Geographic Hierarchy in Rwanda
2.3 Sample Size 16
Figure 4: Sample Size Reduction Process
2.4 Sampling 17
Figure 5: List of Districts by Province
2.5 Measures 18
2.5.1 Household Surveys 18
2.5.2 Water Sample Collection and Processing 19
2.6 Data Management 19
2.6.1 Respondent Variables 20
2.6.2 Household Variables 20
2.6.3 Household WASH Variables 21
Figure 6: Improved and Unimproved Toilet Facilities
2.6.4 Water Sample Variables 21
Figure 7: Improved and Unimproved Water Sources
2.6.5 Water Quality Variables 23
2.7 Data Analysis 24
2.8 Ethics 26
3. Results 27
3.1 Study Population 27
Table 1: Survey Respondent and Household Characteristics
3.2 Handwashing Trends 29
Table 2: Self-Reported Trends in Handwashing Occasions by Household Water Sample Contamination
3.3 Water Quality Results 30
Figure 8: Distribution of Household Water Samples of Microbiological Groups by Province
Table 3: Household Drinking Water Quality by Province and Reported Water Source Type
3.4 Factor Being Used in Regression Modeling 32
Table 4: Modeling Variables by Household Water Sample Contamination
3.5 Log Binomial Regression 36
3.5.1 Crude Log Binomial Regression 36
Table 5: Estimates from Crude Log Binominal Regression Models
3.5.2 Multivariate Log Binomial Regression 37
3.5.3 Collinearity Evaluation 38
3.5.4 Interaction Assessment 38
3.5.5 Confounding Assessment 38
Table 6: Confounding Assessment of Multivariate Log Binominal Regression Models
3.5.6 Adjusted Multivariate Log Binomial Regression 40
Table 7: Factors Associated with Water Quality for a Binary Outcome
3.6 Linear Regression 41
3.6.1 Crude Linear Regression 41
Table 8: Estimated from Crude Linear Regression Models
3.6.2 Multivariate Linear Regression 42
3.6.3 Collinearity Evaluation 43
3.6.4 Interaction Assessment 43
3.6.5 Confounding Assessment 44
Table 9: Confounding Assessment of Multivariate Linear Regression Models
3.6.6 Adjusted Multivariate Linear Regression 45
Table 10: Factors Associated with Water Quality for a Continuous Outcome
4. Discussion 46
4.1 Limitations 48
4.2 Future Directions 50
4.3 Conclusion 52
5. References 53
6. Appendix 58
6.1 DAG of Variables Involved in Household Water Quality 58
6.2 Distribution of Continuous Variables 59
6.3 Correlation Matrix – Binary 62
6.4 Correlation Matrix – Continuous 63
6.5 Codebook 64
6.6 DelAgua-LSHTM Gap Phase Household Survey 67
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