Sanitation predictors of childhood morbidities in Ethiopia: evidence from Dabat Health and Demographic Surveillance System

This cross-sectional study is a re-census employed for the purpose of reconciliation of the Dabat Health and Demographic Surveillance System (DHDSS). The surveillance system has been running since 1996 and has been collecting information on vital events (like birth, death, migration, and pregnancy registrations) and hygiene and sanitation conditions by updating every six months. The site is located in the Dabat district in the northwest part of Ethiopia. Dabat district was initially selected purposively as a surveillance site for its unique three climatic conditions, namely Dega (high land and cold), Woina Dega (midland and temperate), and Kolla (low land and hot). The choice was made with the assumption that there would be differences in morbidity and mortality in the different climatic areas. In 2014, the district had an estimated population size of 173,052 living in 26 rural and four semi-urban kebeles (the smallest administrative unit in Ethiopia). The DHDSS covers thirteen selected kebeles (four semi-urban and nine rural kebeles). A total of 67,385 people were living in the selected kebeles [5].

Data about under-five children were extracted from DHDSS database which were collected by a re-census for the purpose of reconciliation. A re-census was employed in thirteen purposively selected kebeles. Kebeles were initially selected from three different agro-ecological zones for the purpose of showing differences in morbidity and mortality in the different climatic areas. Hence, we employed a re-census, and data collectors visited all the households in the selected kebeles. Data were collected using a pre-tested structured interviewer-administered questionnaire and an observational checklist. The living environment and housing conditions were observed, and photo cameras were used to take pictures by taking participants’ consent. Training was given for data collectors and field supervisors about the data collection tools, data collection procedures, and data collectors’ ethics. Mothers were interviewed about the most common childhood morbidities of their children and sanitation condition. Supervisors had also checked the completeness of all the filled questionnaires.

Childhood morbidity, the primary outcome variable of this study, was defined as the occurrence of one or more gastrointestinal health problems (such as diarrhea, vomiting, and abdominal pain) and respiratory health problems (such as common cold, cough, pneumonia, asthma, and other sanitation-related respiratory problems). Childhood diarrheal disease was defined as having three or more loose or watery stools in 24 h [6]. The occurrence of the symptoms of the above morbidities in the past 2-week period was taken by asking the mothers/guardians. We used the local names of each disease to make clear for the mothers or guardians. Moreover, for the children who visited health institution, we checked the medication history to identify the specific types of diseases, and we asked the mothers or guardians to tell us the name of the diseases confirmed by the physicians. The sanitation predictor variables like housing condition, household energy sources, household light sources, domestic wastewater management practice, household’s solid waste management practice, household’s defecation practices, and drinking water supply system were defined and classified based on the minimum or indicators for access to basic sanitation set by the World Health Organization (WHO) and United Nations Children’s Fund (UNICEF) [7]. A housing condition was taken as good housing if the roof is covered by corrugated iron sheet (CIS); the floor is timber or concrete; rooms had two or more windows; the illumination system is adequate; and the energy was from clean sources like electricity. Illumination system was taken as good if the intensity of light is adequate and not emitting pollutants to the room like electricity and solar. Household cooking energy was taken as relatively clean energy if the source has no or minimal pollutants such as electric power, kerosene, and coal. Drinking water sources were taken as protected if the sources are protected from animal contact, flooding, and wind such as protected well, protected spring, protected rain catchment, and tap water. Households’ handwashing practice was taken as good if all family members washed hands after visiting the toilet, changing the baby’s diaper, or touching wastes and before eating and food preparation, otherwise taken as poor.

Data were entered to Household Registration System (HRS2) version 2.1 and analyzed using Stata version 14.0. Univariable binary logistic regression analysis was used to choose variables for the multivariable binary logistic regression analysis, and variables which had a p value less than 0.2 by the univariable analysis were put into the final model. Variables which had significant association were identified on the basis of AOR with 95% CI and p value < 0.05. Pearson’s chi-square goodness of fit test was used to check model fitness.

Principal component analysis was used to determine the wealth index of rural households using asset data. In the final model, the common factor scores were summed and ranked into poorest quintile, second quintile, third quintile, fourth quintile, and richest quintile.

A total of 8219 under-five children were included in this study. Of these children, 4149 (50.48%) were male. Four thousand eight hundred eighty-nine (59.48%) children were aged between 25 and 59 months. The overwhelming majority, 6491 (78.98%), of children were rural residents. Almost all, 8143 (99.08%), of the mothers interviewed were illiterate. One thousand seven (12.25%) of the households were the poorest by their economic status (Table 1).

Six thousand one hundred seventy (75.07%) of the study participants lived in poor housing condition. Almost all, 8114 (98.72%), of the houses were earthen floor, and 2381 (28.97%) of the residential buildings were tukul, a house built with wood and grass. Five thousand eight hundred two (70.59%) of the residential buildings had no window(s). The general illumination system in 6323 (76.93%) houses was poor. Seven hundred forty-three (9.04%) of the households used open fire as a light source. The majority, 6897 (83.92%), of the households utilized dirty cooking energy, and fire wood was the commonest source (Table 2).

More than half, 4979 (60.58%), of the households fetched drinking water from unprotected sources. Unprotected spring was the commonest drinking water source in the study area, which accounted for 3409 (41.48%). Two thousand one hundred fifty-two (26.18%) of the households traveled more than 30-min round trip to fetch water, and almost all 8070 (98.19%) collected 20 l/p/d and below. Seven thousand seventy-one (86.03%) of the households used narrow-mouthed containers to store drinking water. Six hundred seventy-nine (8.26%) of the households treated drinking water at home. Sedimentation, 451(66.42%), was the commonest home-based water treatment method in the area (Table 3).

In the study area, 2376 (28.91%) of the households used any type of latrine to defecate. Of the households who used latrine, 2140 (90.07%) households used traditional pit latrine. Seven thousand three hundred forty (89.31%) and 7030 (85.53%) respectively disposed domestic wastewater and solid wastes in open field. The handwashing practice of 6416 (78.06%) households was poor (Table 4).

About 575 (7.00%) of under-five children had hygiene- and sanitation-related diseases. Gastrointestinal and respiratory health problems accounted for 287 (49.91%) and 288 (50.09%), respectively. Of the children who had hygiene- and sanitation-related diseases, 83 (14.43%) children had both gastrointestinal and respiratory health problems.

Housing condition, household cooking energy sources, household’s solid waste management practice, household’s domestic wastewater management practice, household’s defecation practices, drinking water sources, type of drinking water storage container, home-based water treatment, and volume of water daily collected were the variables fitted in the univariable binary logistic regression model. The variables having p value less than 20% were housing condition, household cooking energy sources, household’s defecation practices, type of drinking water storage container, and volume of water daily collected, which were selected for multivariable binary logistic regression analysis. By the multivariable binary logistic regression analysis, housing condition, household cooking energy sources, type of drinking water storage container, and volume of water daily collected were statistically associated with childhood morbidities (Table 5).

This survey revealed that the occurrence of childhood morbidities was associated with housing condition. The odds of occurrence of morbidities was 1.27 times higher among children who lived in poor housing condition compared with children who lived in good housing condition [AOR = 1.27, 95% CI = (1.04, 1.54)].

This study revealed that household cooking energy sources were significantly associated with childhood morbidities. The occurrence of childhood morbidities was 1.52 times to be higher among children of households with dirty cooking energy sources compared with children of households with relatively clean energy sources [AOR = 1.52, 95% CI = (1.22, 1.89)].

As depicted by this survey, childhood morbidities were statistically associated with the volume of water daily collected. The odds of having childhood morbidities was 1.95 times higher among households who collected 20 l/p/d and below [AOR = 1.95, 95% CI = (1.19, 3.18). The current study had also discovered that the type of drinking water storage containers was associated with childhood morbidities. The odds of childhood morbidities was 27% less among children whose families stored drinking water in narrow-mouthed containers [AOR = 0.73, 95% CI = (0.56, 0.96)].