General Characteristics of Respondents and Households
Following a response rate of 95.4% for treatment and 98.5% for control, 2676 and 3311 live births were recorded for the mortality computation. Of this, 896 children under-five were identified and eligible for nutrition screening in the treatment (441 children) and control (455 children). Among women of reproductive age interviewed, 74.8% were married, 21.3% single, 1.1% divorced or separated and 2.0% widowed. Additionally, 0.8% children were identified as orphans and 3.3% reported to have lost either one parent. Overall, 74.2% women were involved in paid employment in farming or within the agricultural value chain. Functional literacy was higher among women (56.9%) and 73.8% of all women reported to have ever attended school.
On general characteristics of households where women and children resided, only 6.7% of households had access to electricity. Although 85.4% of households reported to have had access to improved sources of water, 36% reported to have inconsistencies in supply with longer travelling time for water access (22 minutes). Only 50.6% households had access to improved sanitation services which among those with unimproved sources, majority (50%) reported to using pit latrines with slabs. In determining overall socioeconomic status of households, we measured wealth classified households into wealth quintiles using principal component analysis. In the treatment area, the poorest (5th quintile) comprised the highest quintile and was 5.5% greater than the poorest in the control areas. Combining the two poorest quintile shows that the treatment area accommodates 43% compared to only 35.5% in the control areas. In the control areas, the richest quintile constitutes the highest category (24.1%) compared to just 16.2% in the treatment areas (Table 1).
Indicator
|
Control
|
Treatment
|
Functional Literacy among women
|
55.4%
|
56.9%
|
Percentage of women ever attended school
|
72.4%
|
73.8%
|
Households with access to improved water sources
|
88.3%
|
85.4%
|
Households with access to improved sanitation sources
|
51.3%
|
50.6%
|
Wealth Distribution among Households
|
Richest
|
24.1%
|
16.2%
|
Fourth
|
17.9%
|
21.7%
|
Middle
|
22.4%
|
19.2%
|
Second
|
18.3%
|
20.3%
|
Poorest
|
17.2%
|
22.7%
|
Table 1: Background characteristics among households in Treatment and Controls
Low Birth Weight (LBW)
Overall, the prevalence of low birth weight among eligible women of reproductive age was 31(6.4%) compared to the 10.5% at the national level and 5.8% and 6.8% respectively at Rumonge and Bururi provinces [18]. Disaggregating the prevalence rate by background information (Table 2), LBW was higher in the control (7.2%) than the treatment (5.7%). Among age groups, LBW was most manifested among women aged between 30 and 34 years.
All five indicators were used to create a model to predict risk of low birth weight. The model showed that 81% (Nagelkerke R2) of the variance in low birth weight and accurately classified 94.1% of all cases. Among the five variables, malnutrition among women were significantly associated with low birth weight (OR 1.4 95% CI 1.2 – 7.2 p=0.043). Increasing inclusion of cassava in household diet was associated with increased risk of low birth weight (OR 3.8 95%CI 1.5 – 9.5). Residing in treatment area and normal blood pressure were associated with a reduced risk of low birth weight but this was statistically insignificant (Table 2).
Variable Disaggregation
|
N (%)
|
Odds Ratio (OR)
|
Sig. Level (P-value)
|
LBW Prevalence by Treatment Versus Control Zones
|
|
|
|
Treatment
|
14 (5.7%)
|
1.37
|
0.499
|
Control
|
17 (7.2%)
|
|
|
|
|
LBW Prevalence by BP status of woman
|
|
|
|
High/Low Blood Pressure
|
7(9.1%)
|
1.75
|
0.409
|
Normal Blood Pressure
|
21 (5.3%)
|
|
|
|
|
LBW Prevalence by Nutritional Status of woman
|
|
|
|
Normal Nutritional
|
20 (4.8%)
|
1.40
|
0.043
|
Malnourished
|
8 (12.5%)
|
|
|
|
|
LBW Prevalence by Household Hunger Scale
|
|
|
|
No hunger detected in household
|
17 (5.0%)
|
1.56
|
0.277
|
Moderate Hunger detected in household
|
13 (9.4%)
|
Severe Hunger detected in household
|
1 (14.3%)
|
|
|
|
|
LBW Prevalence by Wealth Quintiles
|
|
|
|
Richest
|
2 (2.0%)
|
0.93
|
0.61
|
Fourth
|
5 (5.2%)
|
Middle
|
9 (8.7%)
|
Second
|
6 (6.5%)
|
Poorest
|
9 (9.7%)
|
Table 2: Low Birth Weight Prevalence disaggregated by background variables
Childhood Morbidity (Prevalence of Fever)
General fever prevalence among children under 5 was 49.5% in comparison to 34.3% and 35.3% respectively in Rumonge and Bururi province with 42% at the national level [18]. Child nutrition was associated with fever incidence and this was evident from an assessment of all indicators on food frequency, access and quality. Household Hunger Scale, an indicator that measures access and frequency of food shows that children in households with severe hunger were more predisposed to fever than those with moderate and no hunger (Table 3). Non-consumption of acceptable minimum diet, an indicator that measures food quality and diversity consumed by both breastfed and non-breastfed children showed a significant association with fever and was the highest predictor to fever (OR 1.67 95%CI 1.07 – 2.61 p=0.028).
Variable Disaggregation
|
N (%)
|
Odds Ratio (OR)
|
Sig. Level (P-value)
|
Prevalence by Treatment Versus Control Zones
|
|
|
|
Treatment
|
139 (50.5%)
|
1.04
|
0.857
|
Control
|
135 (48.4%)
|
|
|
|
|
Prevalence by Stunting Status of Child
|
|
|
|
No Stunting
|
151 (50.0%)
|
1.11
|
0.661
|
Stunted
|
40 (48.4%)
|
|
|
|
|
Prevalence by Household Hunger Scale
|
|
|
|
No hunger detected in household
|
180 (47.5%)
|
1.20
|
0.438
|
Moderate Hunger detected in household
|
85 (51.8%)
|
Severe Hunger detected in household
|
9 (81.8%)
|
|
|
|
|
Prevalence by Minimum Acceptable Diet Consumed
|
|
|
|
Minimum Acceptable diet consumed
|
72 (43.6%)
|
1.67
|
0.028
|
No Acceptable Minimum Diet Consumed
|
90 (54.9%)
|
|
|
|
|
Prevalence by Ownership and Sleeping in Mosquito Nets
|
|
|
|
Mosquito Net Present
|
62 (48.4%)
|
1.47
|
0.108
|
Mosquito Net Absent (Not Present)
|
133 (55.9%)
|
Table 3: Prevalence of Fever disaggregated by background variables
Childhood Malnutrition (Acute and Chronic)
Global Acute Malnutrition (wasting) rate in general was 7.6% and this compares to 5% at the national level. Among the background variables disaggregated, month of child was significantly associated with risk of acute malnutrition (OR 1.16 95%CI 0.68 – 1.96 p=) with the 6 to 17 being the category with highest prevalence. Another interesting trend is how the risk of malnutrition reduces with increasing age of a child. Increased household wealth and reduced household size were associated with reduced risk of acute malnutrition (Table 4). Likewise, periodic household hunger was associated with increased risk of acute malnutrition.
Variable Disaggregation
|
N (%)
|
Odds Ratio (OR)
|
Sig. Level (P-value)
|
Prevalence of GAM by Communes
|
|
|
|
Rumonge
|
7.80%
|
0.568
|
0.604
|
Vyanda
|
8.90%
|
|
|
|
|
Prevalence of GAM by Age range in months
|
|
|
|
6 to 17
|
12.70%
|
1.16
|
0.04
|
18 to 29
|
8.50%
|
30 to 41
|
6.80%
|
42 to 53
|
5.10%
|
54 to 59
|
4.50%
|
|
|
|
|
Prevalence of GAM by Household Hunger Scale
|
|
|
|
No Hunger
|
6.90%
|
1.02
|
0.965
|
Moderate Hunger
|
10.90%
|
Severe Hunger
|
10.50%
|
|
|
|
|
Prevalence of GAM by Wealth Quintiles
|
|
|
|
Highest
|
5.20%
|
0.68
|
0.013
|
Fourth
|
5.10%
|
Middle
|
10.80%
|
Second
|
8.90%
|
Lowest
|
10.40%
|
Prevalence of GAM by Household size
|
|
|
|
1 to5
|
7.10%
|
0.45
|
0.152
|
6 to 10
|
8.60%
|
more than 10
|
9.50%
|
Table 4: Prevalence of Global Acute Malnutrition (GAM) by background variables
The Global Chronic Malnutrition (stunting) rate was 45.8% (95%CI 42.5 - 49.1) which compares to 55% at the national level. Prevalence was higher among boys 48.4% (95% CI 43.7 - 53.1) than girls 43.3% (95% CI 38.8 - 47.9). Non-receipt of Vitamin A six month before the survey was significantly associated with increased risk of childhood stunting (OR 1.76 95%CI 1.07 – 2.90). Households that cultivated less diversified crops were associated were 1.26 times more likely to be stunted than those with more diversified crops (Table 5).
Variable Disaggregation
|
N (%)
|
Odds Ratio (OR)
|
Sig. Level (P-value)
|
GCM by Treatment Versus Control Zones
|
|
|
|
Treatment
|
138 (47.8%)
|
1.26
|
0.237
|
Control
|
121 (42.0%)
|
|
|
|
|
GCM by Nutritional Status of caregiver
|
|
|
|
Normal Nutritional
|
221 (44.9%)
|
1.18
|
0.554
|
Malnourished
|
35 (46.7%)
|
|
|
|
|
GCM by Household Hunger Scale
|
|
|
|
No hunger
|
163 (40.9%)
|
1.37
|
0.122
|
Moderate Hunger
|
90 (53.9%)
|
Severe Hunger
|
6 (54.5%)
|
|
|
|
|
GCM by Wealth Quintiles
|
|
|
|
Richest
|
39 (33.9%)
|
0.92
|
0.222
|
Fourth
|
46 (40.0%)
|
Middle
|
62 (51.7%)
|
Second
|
47 (42.0%)
|
Poorest
|
65 (56.5%)
|
|
|
|
|
GCM rate by Crop Harvest Diversity Index
|
|
|
|
Highest Crop Diversity Index
|
67 (41.1%)
|
1.26
|
0.213
|
Middle Crop Diversity Index
|
102 (45.7%)
|
Lowest Crop Diversity Index
|
89 (47.3%)
|
|
|
|
|
GCM rate by Vitamin A supplementation for children U5
|
|
|
|
Yes Supplementation
|
195 (42.8%)
|
1.76
|
0.027
|
No Supplementation
|
53 (57.0%)
|
Table 5: Prevalence of Global Chronic Malnutrition (GCM) by background variables
Childhood Mortality
In total, 5986 birth histories were collected in the treatment (2675) and control (3311) zones respectively from which childhood mortalities were computed. Neonatal mortality rate was 18.4 per 1000 live births compared to 9.4 in controlled communities and this compares to 23 deaths per 1000 live births at the national level.
Post Neonatal Mortality Rate (PNNR) was 7.4 and 11.1 respectively in the treatment and control. The national figure is 24 deaths per 1000 which is 123.3% more than the treatment figure. Infant mortality rate was 23.9% higher in the treatment area than the control. Also child mortality was lower in the catchment area and is in fact 127.5% lower than the national figure. Under-five mortality was slightly higher in the treatment area than control (0.6% percentage difference). Although statistically insignificant, there are differences in the rates of across the spectrum of childhood mortalities. For example, Neonatal mortality in the treatment is 64.7% higher than that of the control and is the highest contributor (53.7%) of all mortalities in the treatment (Figure 2).
In assessing the risk of childhood mortality, the risk of mortality is higher among children in control (HR 2.58 95%CI 1.28 – 5.21 p=0.008) than treatment. Also, the risk of a child to die within the first 28 days of life is significantly higher (HR 20.72 95% 8.64 – 49.65 p=0.001). On location, children in low land areas are 1.25 times (95%CI 0.68 – 2.29 p=0.469) more likely to die than those at the mountainous areas. Girls are at higher risk of childhood mortality than boys (HR 1.31 95%CI 0.76 – 2.29 p=0.325).