Raw and spatially smoothed coverage
Five subsequent wealth quintiles or subsets of clusters have been created based on the national assets score, excluding drinking water supply. The first quintile included the poorest 20% households and the fifth quintile included the richest 20%. Based on the asset score, households were assigned to one of the five wealth quintiles. Clusters categorized by the number of households each cluster denotes for each quintile are presented in Fig 1. The number of households varies across quintiles with 26.9-41.2% of clusters had only 1-3 households, meanwhile 11.3-48.2% of the clusters possessed 17-29 households. The spatial distribution of clusters by quintile and for the overall population (Fig 1) showed a similar distribution of clusters in the first three quintile, while quintile four and five had a greater number of clusters around the capital Addis Ababa. The first three quintiles had higher number of clusters at the North (Tigray and Amhara regions) and South (SNNPR). The trend continued to the fourth quintile, except the distribution of clusters extended to the centre and around the capital, Addis Ababa. The fifth quintile had a similar pattern around the capital and clusters sparsely distributed at the periphery.
The number of clusters with varying percentage of raw coverage by wealth quintile are presented in Fig 2. The percentage of clusters with high number of unimproved water coverage (60-100%) declined from the poorest quintile (56.3%) to the richest (12.6%). The difference in raw coverage rates among the sampled clusters was statistically tested using one-way ANOVA. The difference in raw coverage between quintiles was statistically significant, F (3, 1697) =21.1, p <0.001. The percentage coverage of unimproved water in the first three quintiles was different from the fourth and fifth quintiles. Spatially, clusters with higher percentage of unimproved water coverage were located at the North (Amhara and Afar regions), South (SNNPR) and East (Somalia) regions (Fig 3). While in the capital Addis Ababa, Dire Dawa city administration and Gambella region, the coverage of unimproved water is low.
As 26.9-41.9% of the clusters in different quintiles had only three or less households, a spatially smoothed coverage rate was calculated for each cluster to overcome the small number issue. Spatially smoothed rates were calculated by borrowing information from neighbouring clusters to produce a more stable and less noisy estimate of the rate associated with each cluster. Clusters categorized by smoothed coverage in each quintile are presented in Fig 4. The percentages of clusters with 0-20% and >80-100% coverage has decreased in all quintiles except quintile 5, which showed a rise in the percentage of clusters with 0-20% coverage. The spatial smoothing has adjusted the raw coverage rates of clusters in each quintile towards a level fitting the average scenario of the surrounding. It resulted a decline and rise in the coverage rate among clusters included in this study. Those clusters with upper and lower end were adjusted and converged to the middle (>20-80%). Fig 5 showed the distribution of spatially smoothed coverage rates for overall population and by quintile. The pattern of clusters with >60-100% coverage of unimproved water was clearly observed at the north and south and becomes infrequent as we moved from the first quintile to the fifth.
The difference between spatially smoothed and raw coverage rates for overall population and each quintile was calculated by subtracting the raw coverage from spatially smoothed coverage rates. Direction wise (rising and declining), an increasing trend of spatially smoothed coverage rate was observed at the north and south, especially in the first two quintiles. The rates in quintile 3 and 4 were most intensively under and overestimated, respectively. In contrast, the degree or extent of change is low in the fifth quintile and being spatially smoothed was different among quintiles and related with the number of households each cluster represents. Clusters with small number of households showed a considerable change by spatial smoothing process.
Hotspots and clustering trends
The global spatial autocorrelation was calculated using Global Moran’s I. The analysis based on feature locations and attribute values revealed a clustering pattern of unimproved water coverage across the whole clusters (Global Moran’s I = 0.174, p-value < 0.0001). Following a statistically significant positive result from the global spatial autocorrelation, Local Moran’s I was also calculated for the overall population to show hot and cold spots. Unlike the global spatial autocorrelation, local spatial autocorrelation was calculated for both the raw and spatially smoothed data. The local clustering trend of high and low spots among sampled clusters was identified in both raw and spatially smoothed coverage (Fig 6a and b). However, clustering of spatial outliers was eliminated in the spatially smoothed coverage. The raw coverage revealed 72 high-high and 120 low-low statistically significant clusters (p<0.05), followed by 8 high-low and 27 low-high clusters. Meanwhile, the spatially smoothed coverage showed 155 high-high and 149 low-low statistically significant clusters (p<0.05). Clusters in the North (Amhara region and Afar), in the East (Somalia region) and in the south (SNNPR region) had statistically significant unimproved hot spots (95% confidence). Clusters in the centre, south and west had statistically significant cold spots as presented in (Fig 6a and b).
Point-of-use treatment practices
Of the total households (5005), 613 (10.46%) treat their water prior to drinking with any of the methods (use either boiling, filtration, bleach, SODIS, let it stand and settle, and cloth straining) and 365(6.24%) of households treat using adequate methods (use either boiling, filtration, bleaching or SODIS). The number of households and reportedly used treatment methods are: boiling 125 (2.14%), bleach 164 (2.80%), cloth straining 204 (3.48%), filtration 105 (1.80%), let it stand and settle 41 (0.70), and SODIS 5 (0.09%). The data shows, of the adequate methods, treating with SODIS the least to be used by the households in the country (Table 1).
The logistic regression shows that the odds of treating water among household head with highest education level was 2.50 more (95%CI=1.43, 4.36) compared to those who did not attend formal education. Household with highest wealth quintile had more odds of treating water compared to poorest (Table 1).
Table 1. Households with unimproved water sources treat their water point-of-use and associated factors, EDHS, 2016
Factors
|
Category
|
N (%)
|
POUWT
|
AOR (95% CI)
|
No
|
Yes
|
Owning Radio
|
No
|
4,671 (79.76)
|
4393
|
279
|
|
Yes
|
1,185 (20.24)
|
1099
|
87
|
1.25 (0.96, 1.64)
|
Owning Television
|
No
|
5,817 (99.33)
|
5458
|
360
|
|
Yes
|
34 (0.67)
|
34
|
87
|
0.52 (0.24, 1.13)
|
Household wealth quintile
|
Poorest
|
1,956 (33.40)
|
1825
|
132
|
|
Poorer
|
1,533 (26.18)
|
1461
|
73
|
0.58 (0.43, 0.79)
|
Middle
|
1,355 (23.14)
|
1289
|
66
|
0.71 (0.52, 0.97)
|
Richer
|
911 (15.55)
|
841
|
70
|
0.80 (0.57, 1.11)
|
Richest
|
101 (1.73)
|
75
|
26
|
1.97 (1.12, 3.47)
|
Household head education
|
No education
|
3,805 (65.22)
|
3605
|
200
|
|
Primary
|
1,765 (30.26)
|
1633
|
132
|
1.31 (1.02, 1.68)
|
Secondary
|
179 (3.06)
|
161
|
18
|
1.33 (0.79, 2.22)
|
Higher
|
85 (1.46)
|
71
|
15
|
2.50 (1.43, 4.36)
|
Residency
|
Rural
|
91 (1.55)
|
5412
|
354
|
|
Urban
|
5,766 (98.45)
|
80
|
11
|
0.91 (0.52, 1.61)
|
Presence of Children in the house
|
No
|
2,445 (41.75)
|
2288
|
157
|
|
Yes
|
3111 (58.25)
|
3203
|
209
|
1.02 (0.82, 1.27)
|