Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis.

BACKGROUND: Worldwide, diarrhea is the second most common cause of death and morbidity among under -five years' children. In sub-saran Africa, access to water, sanitation, and hygiene are very scanty and the burden of diarrhea diseases is countless relative to the rest of the world. Prior studies conducted in East Africa vary in design, sample size, and other data collection tools. Through those studies, it is hard to make regional comparisons. Combining datasets that are studied on similar people and having common variable identified enhances statistical power due to the large sample size, advance the ability to compare outcomes, and create the opportunity to develop new indicators. Hence, this study aimed to assess the prevalence and associated factors of diarrhea among under five years' children using the most recent national representative Demographic and Health Surveys from 12 East African countries. The information generated from this pooled datasets will give good insight into the sub-regional prevalence of diarrhea.
METHODS: This study utilized secondary data from 12 East African countries' most recent demographic health survey. Variables were extracted and appended together to assess the pooled prevalence of diarrhea and associated factors. A total of 90,263 under-five years of age children were encompassed in this study. STATA version was used to cross-tabulate and fit the models. To account for the hierarchical nature of the demographic health survey, multilevel logistic regression was calibrated. BIC, AIC, deviance, and LLR were used as Model comparison parameters. Variables with a p-value of <0.2 were considered for multivariable analysis. Adjusted odds ratio with 95% CI and p-value <0.05 were used to declare statistical significances of factors.
RESULTS: The pooled prevalence of diarrhea in under five years children was 14.28% [95%CI; 14.06%, 14.51%]. Being child whose mother age is 15-24 years [AOR = 1.41, 95% CI; 1.33, 1.49], 25-34 years[AOR = 1.17, 95%CI; 1.10, 1.23], being 7-12 months child [AOR = 3.10, 95%CI; 2.86, 3.35], being 12-24 months child [AOR = 2.56, 95%CI; 2.38, 3.75], being 25-59 months child [AOR = 0.88, 95%CI; 0.82, 0.95], being child from poor household [AOR = 1.16, 95%CI; 1.09, 1.23], delayed breast feeding initiation (initiated after an hour of birth) [AOR = 1.15, 95%CI; 1.10, 1.20], and being a child from community with low educational status [AOR = 1.10, 95%CI; 1.03, 1.18] were factors associated with diarrheal diseases.
CONCLUSION: The pooled prevalence of diarrhea among under five years of children in East African countries is high. Maternal age, child's age, wealth status of the household, the timing of breast feeding initiation, sex of the child, community level of educational status, working status of the mother, and the number of under five children were factors that were associated with diarrheal diseases. Scaling up of maternal and child health services by government and other concerned bodies should consider those economically marginalized communities. Additionally, awareness should be created for those uneducated mothers concerning the nature of childhood diarrhea.

Background

Diarrhea in children is defined as, three or more bowel movements (passage of loose stool) per 24 hours or watery stool that is different from normal [1]. Worldwide, diarrhea is the second cause of death and morbidity among under five years children. Each year 525, 000 under five years children lost their lives due to avoidable diarrhea diseases, and Globally, there are approximately 1.7 billion cases of childhood diarrheal case every year [2]. According to the report of Global Burden of Diseases(GBD) in 2019, in sub-Saharan Africa total Disability-adjusted life years (DALYs) estimate due to diarrhea was 13.01% [3, 4]. As poor access to sanitary materials is the main case of diarrheal diseases [5], In sub-saran Africa, access to water, sanitation, and hygiene (WASH) are very scanty [5] and the burden of diarrhea diseases is countless relative to the rest of the world. As claimed by the Global Burden of Diseases visual hub, total DALYS among under five years children in the Eastern sub-saran region contributed by diarrheal diseases was 10.21% [4]. Different studies showed that the prevalence of diarrheal diseases among children under five years is high in East African countries. Based on meta-analysis conducted in Ethiopia the prevalence of diarrhea ranges from 19% to 25% [6]. Other studies conducted in Uganda, Rwanda, and Malawi uncovered that the prevalence of diarrheal diseases was 32% [7], 26.7% [8], and 20% [9] respectively. Additionally, the culture of open defecation in East African countries is common. For example, a systematic review done in Ethiopia showed a low level of open defecation free areas(i.e. 16%) [10]. Other studies also showed a low level of open defecation free communities like Kenya (14%) [11], Uganda(22.9%) [12]. In general, in East African countries, access to basic sanitation and hygiene services is very low [13].

Prior studies disclosed that many factors are associated with diarrhea among under five years children. Among those; child’s age [7, 14–16], mother/caregiver’s educational status [8, 14–18], place of residency [7, 15, 19], not vaccinated to Rota [8], maternal/caregiver’s age [17], travel time to the water sources [17], wealth index [7, 15, 17, 18], birth interval [16], unimproved sources of drinking water [16], exclusive breast feeding, timing of breast feeding initiation [7], regional location [7], mother’s working status [7] and unimproved toilet facilities [9].

So far, many studies have been done in East Africa about diarrheal diseases among under five years children. However, those individual studies vary in design and sample size, which is difficult to perform the regional comparison. Making a regional comparison is important to meet current global initiatives agendas like Sustainable Development Goal (SDG). Combining datasets that are studied on similar people and having common variable identifiers enhances statistical power due to the large sample size, advances the ability to compare outcomes and paves the way to develop new indicators. To date, in East Africa, studies conducted to describe diarrheal diseases among under five years children by merging cross-national datasets are limited. Hence, this study aimed to assess the prevalence and associated factors of diarrhea among under five years children using the most recent (2008–2019) nationally representative Demographic and Health Surveys (DHS) from 12 East African countries. The Information generated from this pooled data will give a good insight into the sub-regional prevalence of diarrhea. This study might also help policy makers, global organizations, NGOs, and researchers to identify the most vulnerable East African region to diarrhea, to give urgent interventional measures and resource allocation. This study found that the conjoined prevalence of diarrheal diseases in east Africa high and modifiable factors like wealth status of households, time of breast feeding initiation and educational status were the main determinants of diarrheal episodes among under-five children.

Methods and materials

Data sources

As the majority of the population of East African countries are rural residents, more than fifty percent of the resident of East African countries lacks improved WASH indicators [20]. East African countries are countries with the highest prevalence of diarrheal diseases among under-five children when compared to the rest of the world [17]. This study used data from 12 Eastern African countries of most up to dated demographic health surveys. Eastern African countries embodied in this study were Burundi, Ethiopia, Comoros, Uganda, Rwanda, Tanzania, Mozambique, Madagascar, Zimbabwe, Kenya, Zambia, and Malawi. Mayotte, Reunion, South Sudan, Djibouti, Seychelles, and Mauritius were omitted because of no history of DHS conduction. Additionally, Eritrea and Sudan were also not included due to the long period since their last conduction of DHS, i.e. Eritrea in 2002 and Sudan in 1989/90 (Table 1). It was conducted using the principle of a two-stage stratified sampling procedure. In the first stage, Enumeration Areas (EAs) were randomly selected proportionally to their respected clusters. In the second stage, households were selected. The primary objective of conducting DHS is to provide up-to-date information about health and health-related indicators for planning, policy formulation, monitoring, and evaluation of population and health programs in the respective countries.

Table 1

Survey years of each country with respective weighted sample size.

Country’s name	Survey year	Weighted sample size
Burundi	2016	12,774
Ethiopia	2016	10,337
Kenya	2014	18,517
Comoros	2012	3,030
Madagascar	2008	11,769
Malawi	2015	16,336
Mozambique	2011	10,722
Rwanda	2019	7,616
Tanzania	2017	9,268
Uganda	2016	14,153
Zambia	2018	9,183
Zimbabwe	2015	5,944

Variables were extracted after a deep literature review and appended together to assess the pooled prevalence of diarrhea and associated factors in East Africa among under five children. In this study, the children’s dataset (KR file) was used. Ultimately, a total of 129,651(weighted) children under the age of five were encompassed in this study.

Study variables

Dependent variable

The outcome variable was binary, children who had diarrhea at any time during the 2 weeks preceding the interview. The response variable diarrhea is recoded as follows: Those mothers/caregivers who responded yes to the question “had diarrhea in the last two weeks?” were coded as 1 and those who answered no were coded as 0 [21].

Independent variables

We sub-portioned the independent variables into two groups; level-1 (individual-level factors) and level -2(community-level factors).

Level-1 factors

Child’s age, child’s sex, number of under five years children, immunization status, duration of breast feeding in months, age of the mother/caregiver, education of the mother, mother’s working status, mass media exposure of the mother, household wealth status, type of latrine, type of drinking water source and timing of breast feeding initiation after birth were considered for this study.

Level-2 factors

The place of residence, community level of poverty, and community-level of educational status were variables assigned as community-level factors. The variable community level of poverty and community-level of educational status were generated by aggregating individual level factors at the cluster/community level.

Operational definition

Media exposure. This variable is composite which consisted of watching television, listening to the radio, and reading magazines. Watching television (those who watch television less than once a week, at least once a week and every day are coded as = yes, otherwise = no), frequency of listening to the radio (listening less than once a week, at least once a week and every day are coded as = yes, otherwise = no) and frequency of reading Newspaper or magazine (reading less than once a week, at least once a week and every day are coded as = yes, otherwise = no) [22].

Visits to health facility or visited by health worker. Women either visited by health worker or had visited health facility in the last 12 months are categorized under “yes” and those who neither visited health facility nor visited by health worker were categorized under “no”.

Type of toilet. Population using toilet characterized by flushing to somewhere else, pit latrine—without slab, bucket toilet, hanging toilet or other toilet were coded as “unimproved toilet” and population using toilet which flush—to piped sewer system, flush—to septic tank, flush—to pit latrine, flush—don’t know where, pit latrine—ventilated improved pit, pit latrine—with slab or composting toilet were coded as “improved toilet” [22].

Drinking water type. Household using drinking water which is, piped into dwelling piped to yard/plot public tap/standpipe, piped to a neighbor, tube well or borehole, protected well, protected spring, rainwater, tanker truck, cart with small tank or bottled water were coded as “improved drinking water” and household categorized under unprotected well, unprotected spring, surface water or other sources of drinking water was coded as “unimproved drinking water” [22].

Timing of BF initiation. Children who initiated BF within one hour of birth are labeled as “early” and coded 1, apart from that labeled as “delayed” and coded as 0 [23].

Community level of poverty. Proportion of households assigned to poorest and poorer wealth index. Those fall at the median value and above are categorized under the high poverty level, and those who fall below the median value of the variables are categorized under the low poverty level. Median is used as a cut point because of skewed distribution. The same way of categorization was used for community-level educational status.

Community-level of educational status. Proportion of mother’s/caregiver’s of the child who is educated primary and above are categorized as having “high level of educational status” and otherwise “low level of educational status”.

Perceived distance from health facility. The DHS program asks caregivers or mothers their perception whether the distance from health facility is a “big problem “or “no a big problem” when they were seeking medical advice or treatment for themselves when they are sick.

Immunization status. Fully vaccination definition is adopted from the number of children aged 12–23 months who received one dose of BCG vaccine, three doses of polio vaccine, three doses of pentavallent vaccine (DTP-hepB-Hib), three-dose of pneumococcal conjugate vaccine (PCV), two-doses of virus vaccine, and one dose of measles vaccine was considered as “fully vaccinated” otherwise “not fully vaccinated [24].

Data analyses

Cross tabulations and summary statistics were done using STATA version 16 software. The forest plot technique was utilized to display the prevalence of diarrhea across countries. To plot 95% CI of the coefficient of each variable of the best-fitted model, STATA command “coefpot” was applied. AS the DHS datasets have hierarchical nature (sample is not taken randomly), non-independencies of observations and violation of equal variance assumption of the single level statistical model like logistic regression are inevitable.

In the multistage stratified clustered sampling of DHS, children within a cluster are more likely to relate to certain characteristics as compared to children between the clusters. To overcome those problems, to draw reliable inferences, we calibrated so what sophisticated model called the multilevel logistic model to identify factors associated with diarrhea. We first calibrated the null model (model with only constant/intercept) in order to declare nesting of observation within clusters and to determine the use of multilevel analysis. To warrant the use of multilevel analysis, ICC (intra-class coefficient) was checked. Intra-class coefficient takes the value between 0 and 1. If the intraclass coefficient value approaches value one, then it indicates observations within the cluster are more similar than observations between clusters. Therefore, it implies that a multilevel model is necessary for that specific dataset. It also shows how much of the response’s total variation is explained by clustering.

Deviance Information Criterion (DIC), Log-Likelihood Ratio (LLR), Akaike information criteria (AIC), and Bayesian information criteria (BIC) were used as a model comparison and selection parameters. The model with the lowest values of those parameters was selected as the best-fitted model. The model comparison was done among the null model (a model with no independent variables), model I (a model with only individual-level factors), model II (a model with only community-level factors) and model III (a model with both individual and community level independent variables). Variables with a p-value <0.2 in the bi-variable analysis were considered in the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤ 0.05 in the multivariable model were used to declare significant factors associated with diarrhea.

Ethical consideration

This study used datasets of national representative demographic health surveys. Therefore, ethical is approval not required. But, datasets for this study were requested by providing a clear explanation about the objectives and necessity of this study. We registered and requested the DHS dataset to the online database (www.dhsprogram.com) and received an authorization letter to download the requested datasets.

Results

Characteristics of the study population

The majority of the study participants were from Kenya (14.3%), Malawi (12.6%), Uganda (11%), and Burundi (9.9%%). The median age of the child was 28 months, with an interquartile range (IQR) of 13 to 43 months. Most of the children (78.4%) were from rural and more than half were born from primary level educated women (Table 2).

Table 2

Characteristics of the study population in East Africa (N = 129,651).

Characteristics	Weighted frequency	Percent
Country
Burundi	12,774	9.85
Ethiopia	10,337	7.97
Kenya	18,517	14.28
Comoros	3,030	2.34
Madagascar	11,769	9.08
Malawi	16,336	12.6
Mozambique	10,722	8.27
Rwanda	7,616	5.87
Tanzania	9,268	7.15
Uganda	14,153	10.92
Zambia	9,183	7.08
Zimbabwe	5,944	4.58
Age of child in months
0–6	11,315	12.5%
7–12	9,895	11.0%
13–24	18,690	20.7%
25–59	50,363	55.8%
Sex of the child
Male	45,441	50.3%
Female	44,822	49.7%
Immunization status
Fully immunized	52,436	58.5%
Not fully immunized	37,206	41.5%
Maternal age
15–24	25,984	28.8%
25–34	43,609	48.3%
35–49	20,670	22.9%
Maternal educational status
None	23,191	25.7%
Primary	45,620	50.5%
Secondary &above	21,452	23.8%
Wealth status
Poorest	21,723	24.1%
Poorer	19,555	21.7%
Middle	17,428	19.3%
Richer	16,712	18.5%
Richest	14,845	16.5%
Perceived distance from health facility
Perceived as big problem	36,019	44.5%
Perceived as Not big problem	44,864	55.5%
Types of latrine
Unimproved toilet	26578	40.4%
Improved toilet	39288	59.6%
Source of drinking water
Unimproved	31,305	35.4%
Improved	57,405	63.6%
Being Twin
No	87,937	97.3%
Yes	2,473	2.7%
Mother’s Current working status
No	33,103	40.9%
Yes	47,803	59.1%
No. of under five years children
≤2	73,621	81.6%
>2	16,641	18.4%
Time of breast feeding initiation after birth
Immediately to 1 hour	55,910	62.0%
1 hour	34,352	38.0%
Media exposure
Exposed	59,387	65.8%
Not exposed	30,876	34.2%
Community level factors
Place of residency
Urban	21,507	23.8%
Rural	68,756	76.2%
community level educational status
Low level	43998	48.7%
high level	46264	51.3%
Community level of poverty
Low level	46435	51.4%
High level	43828	48.6%

Prevalence of diarrhea in East Africa

The pooled prevalence of diarrhea among under five years of children in East Africa was 15.86% [95% CI: 15.66, 16.06]. The highest prevalence of diarrhea among under five years children was observed in Burundi with a prevalence rate of 22.84% [95%CI: 22.80%, 23.88%] followed by Uganda (22.10%) and Malawi (21.57%). Conversely, the lowest prevalence was also noticed in Madagascar (8.16%), Ethiopia (11.33%), and Tanzania (12.33%). The estimated prevalence of diarrhea is significant in all 12 countries because of the estimated prevalence of each country didn’t overlap with pooled regional prevalence estimate (Fig 1).

Fig 1

Forest plot of the pooled prevalence of diarrhea among under five years of children in Eastern African countries.

The weighted percentage on the right side of the forest plot indicates the influence of each country’s studies on the pooled prevalence of diarrhea. Accordingly, Kenya, Mozambique, Ethiopia, and Tanzania were the most influential countries on the pooled prevalence of diarrhea in children. The broken vertical line of the forest plot stands for the pooled prevalence of diarrhea with the confidence interval corresponding to the width of the diamond. The diamond to the left and right of this broken line indicates the point prevalence of diarrhea in the corresponding country is lower and higher than the pooled prevalence of diarrhea, respectively. A point crossing the broken line denotes that overlapping of confidence interval with pooled prevalence. The unbroken vertical line in this study indicates no prevalence of diarrhea (zero prevalence).

Sensitivity analysis

To declare the stability of those compiled studies, sensitivity analysis was conducted (Table 3). In the sensitivity analysis, each analysis is produced by excluding a single study (country in this case). This analysis is performed to view outlier studies (studies that yield exaggerated effect size) while studying by pooling more than one study. Including such outliers, studies distort overall results. From Table 2 we can illustrate that Ethiopia’s point estimate of the prevalence of diarrhea is outside the combined confidence interval. This means, omitting Ethiopia from the study will increase the pooled prevalence of diarrhea to 25%. Hence, the rest studies’ point estimates are inside the confidence interval of the combined effect, we can conclude that those studies are stable.

Table 3

Sensitivity analysis of the pooled prevalence of diarrheal diseases among pregnant women in East Africa.

Study omitted	point Estimate	95% CI
		LCIL	UCIL
Burundi(2016)	12.99	2.99	22.99
Ethiopia(2016)	24.92	8.46	41.38
Kenya (2014)	13.65	2.67	24.62
Comoros(2012)	12.92	2.89	22.95
Madagascar(2008)	12.89	2.80	22.98
Malawi(2015)	13.03	3.05	23.01
Mozambique(2011)	12.89	2.80	22.98
Rwanda(2019)	12.94	2.92	22.96
Tanzania (2017)	13.00	3.02	22.99
Uganda(2016)	12.99	2.99	22.98
Zambia(2018)	13.10	3.14	23.05
Zimbabwe (2015)	12.90	2.84	22.96
Combined effect	13.41	3.48	23.34

Model comparison

Measures of random effects/measure of variation included intraclass/cluster correlation (ICC), median odds ratio (MOR), and proportional change in variance (PCV). ICC was calculated to ensure intra-cluster variability of the study participants. Children from the same cluster are highly likely to share common characteristics than children outside the cluster. Output from the intercept-only model (null model) uncovered that the intraclass correlation coefficient is 1.6%. This indicates that 1.6% of the variation of diarrheal diseases is contributed by the difference between clusters.

The median odds ratio generated from the null model also shows the inter-cluster variation of diarrheal diseases. The median odds ratio value (1.25) generated by the null model is interpreted as; when two individuals having the same characteristics (covariates) picked from different clusters randomly, the individual from higher risk cluster had 24% higher odds of encountering diarrheal diseases compared to the individual from the low risk cluster. Additionally, the proportional change in variance from model III (full model) illustrates that28.3% of the odds of childhood diarrhea across the selected East African countries was accounted by both individual and community-level factors (Table 4).

Table 4

Model comparison and fitness parameter outputs.

Fitness parameter	Null model	model I	model II	model III
Community level variance	.053	0.039	0.051	0.038
ICC	1.6%	1.17%	1.54%	1.16%
MOR	1.25[1.21,1.29]	1.20[1.17,1.25]	1.241[1.20–1.281]	1.20[1.17,1.25]
PCV (%)	Reference	26.4%	3.8%%	28.3%
Model fitness
Log- likelihood ratio(LLR)	-55586
DIC(-2LLR)	111172
AIC	111176.1
BIC	111195.7

NOTE: ICC, intra-cluster correlation; MOR, median odds ratio; DIC, deviation information criterion. Null Model is the empty model, baseline model without any determinant variable. Model I is adjusted for individual-level factors. Model II is adjusted for community-level factors. Model III is the final model adjusted for both individuals and community-level factors.

According to Table 4, model III (model embodies both individual-level factors and community-level factors) is best the fitted model for this dataset. This is because model three (full model) is with the lowest BIC and deviance.

Factors associated with diarrheal diseases

From the bivariable multilevel modeling, variables except for toilet type and media exposure all variable showed significant association with the dependent variable at p-value <0.2 (Fig 2).

Fig 2

Coefficient plot of both community and individual-level factors of diarrhea among under five years children in East Africa with respective 95% CI.

The odds of diarrhea were 41% [AOR = 1.41, 95% CI: 1.33, 1.49] and 17% [AOR = 1.1.17, 95% CI: 1.10, 1.23] higher in children whose mother’s age is 15–24 and 25–34 years respectively as compared to 35–49 years of age mother. Similarly, the odds of diarrhea was 3.10 [AOR = 3.10, 95% CI: 2.86, 3.35] and 2.56 times [AOR = 2.56, 95% CI: 2.38, 2.75] higher in children whose age is 7–12 and 12–24 months respectively as contrasted to children aged 0–6 months. Conversely, the odds of diarrhea is 12% lower in children aged 25–59 months compared to children aged 0–6 months. Regarding the sex of the child, being male raises the odds of diarrhea by 8% [AOR = 1.08, 95%CI: 1.03, 1.12] higher than compared to counterpart (Table 5).

Table 5

Multivariable multilevel logistic analysis result of diarrhea diseases among under five years children in East African countries.

Characteristics	Model I (95%CI AOR)	Model II (95%CI AOR)	Model III ((95%CI AOR)
Maternal age
35–49 years	®		®
15–24 years	1.41[1.33,1.49]*		1.41[1.33, 1.49]*
25–34 years	1.17[1.10,1.23]*		1.17[1.10, 1.23]*
Child’s age
0–6 months	®		®
7–12 months	3.10[2.87,3.35]*		3.10[2.86, 3.35]*
12–24 months	2.56[2.38,2.75]*		2.56[2.38, 2.75]*
25–59 months	0.88]0.82,0.95]*		0.88[0.82, 0.95]*
Maternal Educational status
Secondary &above	®		®
None	0.94[0.89,1.01]		0.96[0.90, 1.03]
Primary	1.04[0.99,1.10]		1.05[0.99, 1.11]
Twin
No	®		®
Yes	1.01[0.89, 1.15]		1.01[0.89, 1.15]
No. of under five years children
< 2 child	®		®
≥2 children	0.88[0.84, 0.92]*		0.88[0.85, 0.92]*
Wealth index
Rich	®		®
Poor	1.14[1.09, 1.21]*		1.16[1.09, 1.23]*
Middle	1.12[1.06, 1.19]*		1.13[1.06, 1,21]*
Mother Working status
Working	®		®
Not working	0.86[0.82, 0.90]*		0.86[0.82, 0.90]*
Timing breast feeding initiation
Within one hour	®		®
Delayed initiation	1.15[1.10, 1.20]*		1.15[1.10, 1.20]*
Visit to health facility or visited by health worker
Yes	®		®
No	0.97[0.93, 1.01]		0.97[0.93, 1.02]
Sex of child
Female	®		®
Male	1.08[1.03, 1.12]*		1.08[1.03, 1.12]*
Community level factors
Residency
Urban		®	®
Rural		1.02[0.98, 1.07]	0.98[0.92, 1.03]
Community level educational status
High		®	®
Low		1.15 [0.09, 1.22]	1.10[1.03, 1.18] *
Community level poverty
Low		®	®
High		1.02 [0.97, 1.07]	0.98[0.93, 1.03]
Constant	0.098[0.09, 0.11]	0.19 [0.18, 0.20]	0.11[0.10, 0.12]

NOTE: ®- reference, AOR- adjusted odds ratio.

Another pertinent finding was the timing of breastfeeding initiation after birth. The odds of diarrhea is 1.15 [AOR = 1.15, 95% CI: 1.10, 1.20] times higher in children who initiated breastfeeding after one hour of birth compared to those who initiated within one hour of birth. Additionally, for children belonging to poor and middle wealth households, the odds of diarrhea increase by 16% [AOR = 1.16, 95% CI: 1.09, 1.23] and 13% [AOR = 1.13, 95% CI: 1.06, 1.21] respectively in contrast to children from rich household.

The odds of diarrheal diseases decrease by 12% [AOR = 0.88 95%CI: 0.85, 0.92] and 14% [AOR = 0.86 95%CI: 0.82, 0.90] in children from households having two and more children and in children whose mother were not working respectively. The odds of diarrhea is 1.1 times higher in children who was born from a community of low level of education relative to a high level.

Discussion

The prevalence of diarrhea among under-five years of age children is high in East Africa. This high prevalence is may due to the low level of sanitation in East African countries [13]. The conjoint (pooled) prevalence of diarrhea in the nominated East African countries is 14.28% [14.06%, 14.51%]. The pooled prevalence of diarrhea diseases among children generated from this study is consistent with the study done in India [25] and lower than the study in Egypt (19.5%) [26], Ghana (19.2%) [27], India (25.2%) [28] and cross-sectional survey from south Wollo district, Ethiopia (23.1%) [29]. However, this finding is higher than research done in Nigeria (12.7%) [30]. This dissimilarity is possibly attributable to a difference in socio-demographic characteristics, location, climate, culture of stool disposal, water access, and a culture of handwashing.

Being young-aged cohort of mothers, compared to an old aged cohort of mothers (i.e. 35–49 ages), was found to be elevated the probability of prevalence of diarrhea among under five years children. This finding is in agreement with the study done in Uganda [7] and Nepal [31]. This might be explained by; older mothers having good knowledge and experiences about child health in general and diarrheal diseases specifically [32]. Government effort to educate young mothers is therefore called for.

The odds of developing diarrheal diseases is higher in children who were aged 7–12 and 13–24 months compared to children aged 0–6 months. This finding is in agreement with other studies [7, 14–16, 33]. This high odds of childhood diarrhea prevalence might attribute to the start of supplementary feeding after the age of six months. Children who started supplementary feeding have a high probability of feeding unhygienic foods that might have paved the way to diarrheal diseases. After the age of 6 months children, due to the development of hand-mouth coordination, are highly likely to bring the infectious agent to their mouth and are expected to increase the episode of diarrhea diseases at this age. Conversely, the odds of diarrheal disease among children aged 25–59 months is reduced by 12% as compared to children aged 0–6 months. The possible explanation for this result is, old age children are more potential to hold out against diarrheal diseases because of the more developed immunity compared to younger children.

In this study, it was found that families who had two under-five years children or above were less likely to have diarrhea than those who had only one child. This finding is contrary to previous studies which had suggested that, as the number of under five children increases, the probability of occurrence of diarrheal diseases is high [34, 35]. This inconsistency may be due to those households with a high number of under-five children being more familiar with the sequel of childhood diarrheal diseases; the more they experienced and knowledgeable in knowing ways of transmission of diarrheal diseases, the less likely to have diarrhea in the subsequent child.

Childhood diarrheal disease was statistically associated with household wealth status. Children from poor and middle-income households are at high odds of diarrheal diseases compared to children from rich households. Many studies also fortify this evidence [7, 15, 17, 18]. This high odds of diarrheal diseases in those groups of children can be explained by, economically marginalized families being doubtful to bring their sick child to health facilities due to concern of transport fees and cost of health services. Another possible justification for this finding is that children from the poor household are less likely to get a balanced diet and more likely to encounter malnutrition that precipitates and elongate the duration of diarrhea [36, 37]. The policy implication of this finding is, government and other concerned bodies might have to intervene malnutrition to cease diarrheal diseases.

Delayed breastfeeding initiation was positively associated with diarrhea (increases the likelihood of diarrhea). Children who begin their first breastfeeding after one hour of birth have higher odds of diarrheal episode compared to their counterparts. The finding is in accord with a recent study [7], indicating that children breastfed in the first half an hour after birth reduce the probability of diarrhea. Additionally, being children born from mothers belonging to low community-level educational status and being male are at high probability of experiencing diarrhea as compared to counterparts. A similar declaration was also made by other studies [8, 16, 18, 38, 39]. Lastly, children of not working mothers have a 12% lower odds to have diarrhea than those of mothers who work. This might be elucidated by, mother who always present at household had more time to take care of the health of her child.

Strengths and limitations of the study

Finally, several strengths and limitations can be drawn from this study. The main strength of this study is, as we compiled several datasets from different countries which used almost the same data collection tools, the statistical power of this study is reliable, and the generalizability of this study from such a huge sample size is trustworthy. In addition to this, the hierarchical nature of the surveys was considered through conducting an advanced statistical model (multilevel). The main limitation of this study is that due to the cross-sectional study of DHS, causation can’t be assured. Social desirability bias is inevitable. Additionally, the variable types of water sources were dropped from this study because of the high number of missing values and this might have over or underestimated our model performance. Hence, we used data from a secondary survey, other pertinent behavioral and cultural factors are not embodied in this study.

Conclusions

The pooled prevalence of diarrhea among under five years of children in the 12 East African countries is high. This may raise some difficulty in achieving the Sustainable development goal (SDG-3) that targeted reducing under-five mortality to less than 25 deaths per 1000 live births as diarrhea is the main cause of under-five death [40]. Maternal age, child’s age, wealth status of households, the timing of breastfeeding initiation, sex of a child, community level of educational status, working status of mothers, and the number of under-five children were factors that were associated with diarrheal diseases among under-five children in East African countries. Scaling up of maternal and child health services by government and other concerned bodies should consider those economically marginalized communities. Additionally, awareness should be created for those uneducated mothers concerning the nature of childhood diarrhea.

22 Nov 2021

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Reviewer #1: PONE-D-21-28429

Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

This manuscript describes the pooled prevalence of diarrhea among East Africa countries and its predictors using the DHS data. While the manuscript is of some interest, the manuscript could be strengthened by several modest changes as outlined below.

Comments

- English language needs to be improved and edited by a native English speaker.

- East Africa is repeated in keywords.

- LINE 47 – Do you mean “second most common cause of death”?

- A brief introduction of East Africa (e.g. rural/urban ratio, characteristics related to diarrhea and current WASH program in East Africa in the Methods section would be helpful to the readers.

- Literacy and education are different and could not be able to categorize as literacy according to educational level.

- If you control both maternal education and education of caregiver (considered mostly would be mothers), there is an collinearity issue.

- It is better to rearrange the categories among variables in order in Table. (e.g. 15-24 yrs first followed by 25-34, 35-49, same apply to education, wealth index, etc..)

- Some variables mentioned in the methods such as media exposure are missing in Table 1.

- Clear explanation of recoding the variables is deemed necessary. I noticed that you explained for some (but still need better explanation) while missing for some variables (e.g. distance from health facility)

- LINE 176, 177 – what do you mean by the proportion in the 95%CI. Mean should come with SD while median should come with IQR.

- Any relation between diarrhea and time of breastfeeding initiation for children > 2 years?

- Why do you include Visit to health facility or visited by health worker �  visit to health facility due to diarrhea? If yes, which happened after diarrhea, should not be controlled for it. Otherwise, please explain.

- For pooled prevalence in the forest plot, are there any common characteristics between countries (e.g. low income countries, lower middle income countries or high income countries)? If yes, you can try a sub-group analysis because there is a big difference in prevalence among countries. You can also explore the heterogeneity by the I-square value.

- Adjusting the scale in the x-axis of forest plot will give a better picture of point estimate and 95%CI.

- I appreciated that the authors tried to do sensitivity analysis here. As Table 2 and Figure 2 provide same information, you can omit one.

- Double check on the sensitivity analysis result. As you mentioned, although Ethiopia has a great influence on the pooled prevalence, in fact, Madagascar has lowest prevalence and weight % is not so much different from Ethiopia but result does not change in sensitivity analysis while prevalence becomes doubled after omitting Ethiopia.

- Figure 2 is not eye catching and the number are overlapped. Is the x-axis percentage? Better redraw it.

- LINE 218-236 – remove the subtitles and explanation of Table 2 results should go under the subtitle ‘Model comparison’.

- It is interesting that type of toilet is not associated with diarrhea. Is there any open defecation culture and such kinds of variables included in the dataset? If yes, you should consider it. Are every household have a toilet?

- As Figure 2 and Table 3 give same information, omit one.

- Title for Table 3 is missing.

- LINE 249-259 – can omit as there is no information there.

- The authors tend to cover all information in the text and again in the table. You should only include the most important details in the text when you also have a table.

- The discussion is weak in light of the findings. The discussion section should be rewritten. The ideas are incoherently mixed. The discussion needs to focus on the key implications of the data with a separate paragraph for each concept.

- There are repetition of results in the discussion.

- First paragraph of discussion, should start with brief answer to your research question. Do you think the diarrhea prevalence in East Africa is low or high? And why do you think? Is there any target? Etc.

- In my perspective, comparing the pooled prevalence of East Africa and the prevalence of e.g. Ethiopia is not meaningful because Ethiopia is already included in the pooled prevalence. Furthermore, you mentioned the reason as ‘dissimilarity’ between East Africa and e.g. Ethiopia also not meaningful.

- LINE 316-317 is confusing. Children have to provide supplementary food when the time arrives and it cannot be concluded as ‘highly likely to ingest unhygienic foods’. It mainly depends on the caregivers’ knowledge.

- The defecation culture in East Africa (?open defecation), any efforts in WASH program, government’s implementation, caregiver’s knowledge, etc. should be discussed.

Reviewer #2: Comment

General comment

• The whole manuscript should be revised by English language expert as it is full of grammatical errors

• What will add this paper from the previous similar studies at sub Sahran African level (e.g: Demissie GD, Yeshaw Y, Aleminew W, Akalu Y (2021) Diarrhea and associated factors among under five children in sub-Saharan Africa: Evidence from demographic and health surveys of 34 sub-Saharan countries. PLoS ONE 16(9): e0257522. https://doi.org/10.1371/journal. pone. 0257522 ). At least you did not recognize by using these studies as reference. You have to also identify the major gap of these studies that you filled.

• Implication and justification for the significantly associated variables the discussion part is not strong and hence should be revised

Background

• Sentence on Line 51 and 52 should be clear. “Global 51 burden of diseases 2019 report shows that sub-Saharan total DALYs estimate due to diarrhea was 52 13.01% respectively (3, 4).”

• Line 57 and 58: Reference should be incorporated

Methods and materials

• The sample size should be weighted sample size and you did not mention if it is weighted otherwise it should be

• You have to also prepare a table which explains that survey years and weighted sample size for each country

• Line number 98 and 99 is repeated

• Time to fetch drinking water source should be included as one variable as it it is already in DHS data sets

• Why did you include “community level of poverty and community level of illiteracy variables” as community level factors as these variables are already included in the level 1 as household wealth index and education status as individual level?

• From your operational definition(media exposure), I hope it is a composite variable and you have to explain this

• Category of your dependent and independent variables should be supported by references

• Part of data analysis is very long paragraphs. Please divide each in to more than two parts(line number 144-166)

Result

• You have to revise measurement of a variable(distance to health facility)

• Line 249 “multivariate” is not correct word as your outcome variable is binary. So use the word “multivariable”

• Line number 259 spelling error

Discussion

• Line number 300-306 is part of result part. Please avoid this from discussion part

• You did not study “incidence” so avoid it Line number 308

• Justifications should be supported by evidences(references) in the discussion part

• I did not see your significant community level variables in your discussion

References

• Revise your References citation based on the standard

**********

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Reviewer #1: No

Reviewer #2: No

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28 Dec 2021

Author’s response to reviews

Title: Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Authors:

Abiyu Abadi Tareke (abiyu20010@gmail.com)

Ermias Bekele Enyew (Ermiashi@gmail.com)

Version: 1 Date: 21 December 2021

Author’s response to reviews:

Plos one

Point by point response for editors/reviewers comments

Manuscript title: Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Manuscript ID: PONE-D-21-28429

Dear editors/reviewers:

Dear all,

We would like to thank you for your generous, revealing and constructive comments about this manuscript. You valuable comments would advance the substance and content of the manuscript. The authors accounted each comments and clarification questions of editors and reviewers in a focused way.

Our point-by-point responses to each comment and questions are detailed on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Response to Editors and reviewers’ comments

This manuscript describes the pooled prevalence of diarrhea among East Africa countries and its predictors using the DHS data. While the manuscript is of some interest, the manuscript could be strengthened by several modest changes as outlined below.

Comments

General comments

• The whole manuscript should be revised by English language expert as it is full of grammatical errors

• Author’s response: We extensively edit the manuscript accordingly to enhance the grammatical error through asking the support of nearby English language professionals. Hence, the corrected document is shown in the track change of the revised submission. (For further amendment the revised manuscript)

• What will add this paper from the previous similar studies at sub Sahran African level (e.g: Demissie GD, Yeshaw Y, Aleminew W, Akalu Y (2021) Diarrhea and associated factors among under five children in sub-Saharan Africa: Evidence from demographic and health surveys of 34 sub-Saharan countries. PLoS ONE 16(9): e0257522. https://doi.org/10.1371/journal. pone. 0257522 ). At least you did not recognize by using these studies as reference. You have to also identify the major gap of these studies that you filled.

• Author’s response: thank you for your valuable comment. This paper studied by Demissie GD, Yeshaw Y, Aleminew W, Akalu Y (2021) was not officially released at the time of inception. This paper is not released online till we finish the first draft of the manuscript. From the beginning, we focused to research diarrheal diseases in East Africa rather than sab-saran Africa because of east Africa residing children are the most vulnerable to diarrheal diseases. This is because of lowest sanitary infrastructure in the region.

• Implication and justification for the significantly associated variables the discussion part is not strong and hence should be revised.

• Author’s response: thank you for your priceless comment. We tried to revise all of justifications under the discussion session. See the revised version of the manuscript.

Background

• Sentence on Line 51 and 52 should be clear. “Global 51 burden of diseases 2019 report shows that sub-Saharan total DALYs estimate due to diarrhea was 52 13.01% respectively (3, 4).”

Authors’ response: We made correction for this comment

• Line 57 and 58: Reference should be incorporated

• Authors’ response: We made correction for this comment

Methods and materials

• The sample size should be weighted sample size and you did not mention if it is weighted otherwise it should be

• Authors’ response: We made correction for this comment

• You have to also prepare a table which explains that survey years and weighted sample size for each country

• Authors’ response: We generated new table accordingly.

• Line number 98 and 99 is repeated

• Authors’ response: We made correction for this comment

• Time to fetch drinking water source should be included as one variable as it it is already in DHS data sets

• Authors’ response: We made correction for this comment.

• Why did you include “community level of poverty and community level of illiteracy variables” as community level factors as these variables are already included in the level 1 as household wealth index and education status as individual level?

• Authors’ response: We included this variable in both categories because the many studies are done in this way.

• From your operational definition(media exposure), I hope it is a composite variable and you have to explain this

• Authors’ response: We made correction for this comment.

• Category of your dependent and independent variables should be supported by references

• Authors’ response: We made correction for this comment.

• Part of data analysis is very long paragraphs. Please divide each in to more than two parts(line number 144-166)

• Authors’ response: We made correction for this comment.

Result

• You have to revise measurement of a variable(distance to health facility)

• Authors’ response: We made correction for this comment.

• Line 249 “multivariate” is not correct word as your outcome variable is binary. So use the word “multivariable”

• Authors’ response: We made correction for this comment.

• Line number 259 spelling error

• Authors’ response: We made correction for this comment.

Discussion

• Line number 300-306 is part of result part. Please avoid this from discussion part

• Authors’ response: We made correction for this comment.

• You did not study “incidence” so avoid it Line number 308

• Authors’ response: We made correction for this comment.

• Justifications should be supported by evidences(references) in the discussion part

• Authors’ response: We made correction for this comment.

• I did not see your significant community level variables in your discussion

• Authors’ response: unfortunately, no community level variable is significant here. We tried to use appropriate variables and the correct way of analysis.

References

• Revise your References citation based on the standard

• Authors’ response: We made correction for this comment.

Author’s response towards the comments of reviewers.

Reviewer 1: English language needs to be improved and edited by a native English speaker.

Author’s response: Thanks reviewer for your beneficial comment. We extensively edit the manuscript accordingly to enhance the grammatical error through asking the support of nearby English language professionals. Hence, the corrected document is shown in the track change of the revised submission. (For further amendment the revised manuscript).

Reviewer 1: East Africa is repeated in keywords.

Author’s response: thank you reviewer for your valuable comment. We corrected accordingly. See the new version of the manuscript.

Reviewer 1: LINE 47 – Do you mean “second most common cause of death”?

Author’s response: thank you reviewer for your valuable comment. We corrected accordingly. See the new version of the manuscript.

Reviewer 1: A brief introduction of East Africa (e.g. rural/urban ratio, characteristics related to diarrhea and current WASH program in East Africa in the Methods section would be helpful to the readers.

Author’s response: thank you. Some hygienic review of east Africa is added to new revised version of the manuscript.

Reviewer 1: Literacy and education are different and could not be able to categorize as literacy according to educational level.

Author’s response: we have made some correction for this terminology. The term “literacy is replaced by educational status”.

Reviewer 1: If you control both maternal education and education of caregiver (considered mostly would be mothers), there is a collinearity issue.

Author’s response: thank you for your comment. We didn’t used educational status of caregiver and educational status of mother as separate variables. If a child has alive mother, his mother will be more likely to be his/her caregiver. In our opinion, there is not collinearity issue to address here as caregivers/mothers is not separate variable.

Reviewer 1: It is better to rearrange the categories among variables in order in Table. (E.g. 15-24 yrs first followed by 25-34, 35-49, same apply to education, wealth index, etc...)

Author’s response: thank you reviewer for your constructive comment. The age category 35-49 years is putted first because of it is the reference group. But other categories were placed in ascending order.

Reviewer 1: Some variables mentioned in the methods such as media exposure are missing in Table 1.

Author’s response: thank you. We added the missed variables to the mentioned table (see the revised version of this manuscript).

Reviewer 1: Clear explanation of recoding the variables is deemed necessary. I noticed that you explained for some (but still need better explanation) while missing for some variables (e.g. distance from health facility)

Author’s response: thank you reviewer for your comment.

Reviewer 1: LINE 176, 177 – what do you mean by the proportion in the 95%CI. Mean should come with SD while median should come with IQR.

Author’s response: thank you for your suggestion. We made some correction here.

Reviewer 1: Any relation between diarrhea and time of breastfeeding initiation for children > 2 years?

Author’s response: we didn’t notice relation between those two mentioned variables.

Reviewer 1: Why do you include Visit to health facility or visited by health worker �  visit to health facility due to diarrhea? If yes, which happened after diarrhea, should not be controlled for it. Otherwise, please explain.

Author’s response: those two variables were combined together. Women either visited by health worker or had visited health facility in the last 12 months are categorized under “yes” and those who neither visited health facility nor visited by health worker were categorized under “no”. This variables were included under our study because of some research noticed that those women who had visited health facility or had visited by health professional had good awareness about their own health and health of their child. This good awareness might help children to encounter less diarrheal episode. That why we included in our study. Those two variables are combined and not included in this study as separate independent variables to avoid the issue of collinearity.

Reviewer 1: For pooled prevalence in the forest plot, are there any common characteristics between countries (e.g. low income countries, lower middle income countries or high income countries)? If yes, you can try a sub-group analysis because there is a big difference in prevalence among countries. You can also explore the heterogeneity by the I-square value.

Author’s response: we added some explanation to your suggestions.

Reviewer 1: Adjusting the scale in the x-axis of forest plot will give a better picture of point estimate and 95%CI

Author’s response: we encounter skill gap here. Sorry for this problem.

Reviewer 1: I appreciated that the authors tried to do sensitivity analysis here. As Table 2 and Figure 2 provide same information, you can omit one.

Author’s response: thank you for your productive comment. We omitted the sensitivity analysis accordingly (see the revised version of this manuscript).

Reviewer 1: Double check on the sensitivity analysis result. As you mentioned, although Ethiopia has a great influence on the pooled prevalence, in fact, Madagascar has lowest prevalence and weight % is not so much different from Ethiopia but result does not change in sensitivity analysis while prevalence becomes doubled after omitting Ethiopia.

Author’s response: thank you for your constructive comment. According to your suggestion we omitted the sensitivity analysis in the revised manuscript and agreed to continue through table2 (see the revised version of this manuscript).

Reviewer 1: Figure 2 is not eye catching and the number are overlapped. Is the x-axis percentage? Better redraw it.

Author’s response: thank you for your productive comment. According to your suggestion we omitted the sensitivity analysis in the revised manuscript and agreed to continue through table2 (see the revised version of this manuscript).

Reviewer 1: LINE 218-236 – remove the subtitles and explanation of Table 2 results should go under the subtitle ‘Model comparison’.

Author’s response: We’ve made this correction.

Reviewer 1: It is interesting that type of toilet is not associated with diarrhea. Is there any open defecation culture and such kinds of variables included in the dataset? If yes, you should consider it. Are every household have a toilet?

Author’s response: no additional variable is added to this study beyond the mentioned variables in the manuscript.

Reviewer 1: As Figure 2 and Table 3 give same information, omit one.

Author’s response: We have removed figure 2 and decided to continue table 3

Reviewer 1: Title for Table 3 is missing. A

Author’s response: thank you for your comment. The title of table 3 is added according to you comment (see the revised version of this manuscript).

Reviewer 1: LINE 249-259 – can omit as there is no information there.

Author’s response: we made this correction.

Reviewer 1: The authors tend to cover all information in the text and again in the table. You should only include the most important details in the text when you also have a table.

Author’s response: Thank you for your suggestion, we have amended the comment accordingly.

Reviewer 1: The discussion is weak in light of the findings. The discussion section should be rewritten. The ideas are incoherently mixed. The discussion needs to focus on the key implications of the data with a separate paragraph for each concept.

Author’s response: some correction were made.

Reviewer 1: There are repetition of results in the discussion.

Author’s response: We’ve made this correction.

Reviewer 1: First paragraph of discussion, should start with brief answer to your research question. Do you think the diarrhea prevalence in East Africa is low or high? And why do you think? Is there any target? Etc.

Author’s response: We’ve made this correction.

Reviewer 1: In my perspective, comparing the pooled prevalence of East Africa and the prevalence of e.g. Ethiopia is not meaningful because Ethiopia is already included in the pooled prevalence. Furthermore, you mentioned the reason as ‘dissimilarity’ between East Africa and e.g. Ethiopia also not meaningful.

Author’s response: We’ve made this correction

Reviewer 1: LINE 316-317 is confusing. Children have to provide supplementary food when the time arrives and it cannot be concluded as ‘highly likely to ingest unhygienic foods’. It mainly depends on the caregivers’ knowledge.

Author’s response: we have made this correction.

Reviewer 1: The defecation culture in East Africa (?open defecation), any efforts in WASH program, government’s implementation, caregiver’s knowledge, etc. should be discussed.

Author’s response: we have made correction for this comment.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

25 Jan 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: PONE-D-21-28429R1

Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Thank you for the revised version. Although there is some improvement, the manuscript still requires a significant improvement from the previous version of the manuscript. My sense is that the authors could not sufficiently explain or solve the reviewer’s comments and its current form does not have sufficient quality to warrant publication in PLOS ONE.

Comments

- Point by point responses with LINE number of corrected sentences would facilitate to review

- Attaching the old version of manuscript makes me confused

- English language correction is deemed necessary.

- Introduction should be clear, concise, and identify the added scientific value from the study.

- Table 2 – What is the difference between variables ‘mother’s current working status’ and ‘mother’s working status’. The proportion are different.

- You cannot control both ‘maternal education status’ and ‘community level educational status’ together in a regression model. According to your methodology, these two are derived from a single variable/information, correct? Same applied to poverty/wealth index.

- Also, did you check collinearity?

- Regarding relation between diarrhea and time of breast feeding initiation, I also do not think these are related. That’s the reason why I am asking to you why because you study population is under 5 children and you controlled breastfeeding initiation in the regression model.

- Discussion section needs to be improved a lot.

Reviewer #2: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: Reviewer comments.docx

Click here for additional data file.

3 Feb 2022

Author’s response to reviews

Title: Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Authors:

Abiyu Abadi Tareke (abiyu20010@gmail.com)

Ermias Bekele Enyew (Ermiashi@gmail.com)

Bayley Adane Takele (behaileadane@gmail.com)

Version: 2

Date: 3 February 2022

Author’s response to reviews:

Plos one

Point by point response for editors/reviewers comments

Manuscript title: Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Manuscript ID: PONE-D-21-28429

Dear editors/reviewers:

Dear all,

We would like to thank you for your generous, revealing and constructive comments to the second version of the manuscript. You valuable comments would advance the substance and content of the manuscript. The authors accounted each comments and clarification questions of editors and reviewers in a focused way.

Our point-by-point responses to each comment and questions are detailed on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Comments

- Point by point responses with LINE number of corrected sentences would facilitate to review

- Attaching the old version of manuscript makes me confused

- Author’s response: We made correction to this comments

- English language correction is deemed necessary.

- Author’s response: We made some correction to this comment

- Introduction should be clear, concise, and identify the added scientific value from the study.

- Author’s response: Some scientific values are added

- Table 2 – What is the difference between variables ‘mother’s current working status’ and ‘mother’s working status’. The proportion are different.

- Author’s response: Thank you reviewer for your constructive comment. We accept and modified it. The variable named “mother’s current working status” was added to the table mistakenly. The difference in the proportion is occurred because of this variable was tabulated without adding the command of sample weighting. That way it has lower frequency compared to the second variable ‘mother’s working status’. As the variable ‘mother’s working status’ has weighted sample we decided to continue with this variable and we removed the variable having un-weighted frequency.

- You cannot control both ‘maternal education status’ and ‘community level educational status’ together in a regression model. According to your methodology, these two are derived from a single variable/information, correct? Same applied to poverty/wealth index.

- Author’s response: we thank you for your valuable comment. Community level factors are pertinent factors for children health services, we considered the community level factors by aggregating them from the respective individual level factors to indicate the neighboring effect. This helps policymakers to take intervention at both individual and community levels. For example, child born from mother who is from communities with lower level of community education might be clustered in specific location and taking appropriate intervention in this group of women could have a great advantage to increase child health services including diarrhea prevention and treatment services. In our opinion educational status at individual level are different from educational status generated at community level.

- Also, did you check collinearity?

- Author’s response: Thank you reviewer for your concern. Yes, we have checked it. The multicollinearity issue was addressed using pseudo linear regression analysis through applying the command “estat vif” and the mean variance inflation factor (VIF) of 2.31. Which ensure that non –presence of multicollinearity b/n predictor variables.

- Regarding relation between diarrhea and time of breast feeding initiation, I also do not think these are related. That’s the reason why I am asking to you why because you study population is under 5 children and you controlled breastfeeding initiation in the regression model.

- Author’s response: Time of breastfeeding initiation is included in this study because of early initiation of breast feeding has many health benefit. According to WHO feeding colostrum in the first hour increases the likelihood babies will continue to be breastfed which gives them a head start in the “race against malnutrition“. This means children who had history of early breast feeding initiation are less likely to encounter malnutrition and if the probability of encountering malnutrition is low the probability of diarrhea will also decrease. Additionally, the more initiation of breast feeding early the more likely to continue their breast feeding properly. And this will reduce the frequency of diarrhea. Therefore, we included this variable considering those scientific backgrounds.

- Discussion section needs to be improved a lot.

- Author’s response: Thank you a lot for your comment. We accept all your comments and modified it accordingly.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

14 Feb 2022

Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

PONE-D-21-28429R2

Dear Dr. Tareke,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Khin Thet Wai, MBBS, MPH, MA

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All comments are adequately addressed.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

6 Apr 2022

PONE-D-21-28429R2

Pooled prevalence and associated factors of diarrhea among under-five years children in East Africa: A multilevel logistic regression analysis

Dear Dr. Tareke:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Khin Thet Wai

Academic Editor

PLOS ONE