Geographic variation and factors associated with under-five mortality in Ethiopia. A spatial and multilevel analysis of Ethiopian mini demographic and health survey 2019.

BACKGROUND: The distribution of under-five mortality (U5M) worldwide is uneven and the burden is higher in Sub-Saharan African countries, which account for more than 53% of the global under-five mortality. In Ethiopia, though U5M decreased substantially between 1990 and 2019, it remains excessively high and unevenly distributed. Therefore, this study aimed to assess geographic variation and factors associated with under-five mortality (U5M) in Ethiopia.
METHODS: We sourced data from the most recent nationally representative 2019 Ethiopian Mini-Demographic and Health Survey for this study. A sample size of 5,695 total births was considered. Descriptive, analytical analysis and spatial analysis were conducted using STATA version 16. Both multilevel and spatial analyses were employed to ascertain the factors associated with U5M in Ethiopia.
RESULTS: The U5M was 5.9% with a 95% CI 5.4% to 6.6%. Based on the multivariable multilevel logistic regression model results, the following characteristics were associated with under-five mortality: family size (AOR = 0.92, 95% CI: 0.84,0.99), number of under-five children in the family (AOR = 0.17, 95% CI: 0.14, 0.21), multiple birth (AOR = 14.4, 95% CI: 8.5, 24.3), children who were breastfed for less than 6 months (AOR = 5.04, 95% CI: 3.81, 6.67), people whose main roof is palm (AOR = 0.57, 95% CI: 0.34, 0.96), under-five children who are the sixth or more child to be born (AOR = 2.46, 95% CI: 1.49, 4.06), institutional delivery (AOR = 0.57, 95% CI: 0.41, 0.81), resident of Somali and Afar region (AOR = 3.46, 95% CI: 1.58, 7.55) and (AOR = 2.54, 95% CI: 1.10, 5.85), respectively. Spatial analysis revealed that hot spot areas of under-five mortality were located in the Dire Dawa and Somali regions.
CONCLUSION: Under-five mortality in Ethiopia is high and unacceptable when compared to the 2030 sustainable development target, which aims for 25 per 1000 live births. Breastfeeding for less than 6 months, twin births, institutional delivery and high-risk areas of under-five mortality (Somali and Dire Dawa) are modifiable risk factors. Therefore, maternal and community education on the advantages of breastfeeding and institutional delivery is highly recommended. Women who deliver twins should be given special attention. An effective strategy should be designed for intervention in under-five mortality hot spot areas such as Somali and Dire Dawa.

Background

Under-five mortality is defined as deaths reported at ages 0 to 59 months and includes neonatal, post-neonatal, and child deaths [1]. It is a significant indicator of the socioeconomic, health and environmental conditions, and of national development and health equity and access [2].

Globally, in 1990, the number of under-five deaths was 12,494,000 (93 deaths per 1000 live births). Following an average annual decline of 3.1%, by 2019 it was significantly reduced to 5,189,000 deaths (38 deaths per 1000 live births) representing a 58% reduction [3]. The 2030 sustainable development goals (SDG) aims to end the preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under-5 mortality to at least 25 per 1000 live births or lower [4].

The distribution of U5MR worldwide is uneven and the global burden of U5MR is concentrated in two regions, Sub-Saharan Africa and Central and Southern Asia, which accounts for more than 80% of U5MR [5]. The burden is even higher in Sub-Saharan African countries with 74 deaths per 1000 live births, which accounts for more than 53% of the global under-five mortality [6].

In Ethiopia, U5MR has substantially decreased from 200 per 1000 live births in 1990 to 49 per 1000 live births in 2020 but is not close to Sustainable Development Goal [3]. Additionally, different studies conducted in Ethiopia revealed several socioeconomic, demographic, and geographic or spatial variation. For example, Afar, Somali, and Benishangul Gumuz are the three high-risk regions of Ethiopia for U5MR [7].

Different studies examining U5M previously showed that source of water [8], multiple births [6, 7], region [9, 10], age and sex of household head [1], maternal education status [11, 12], place of delivery [9, 10, 13], maternal age [14-16], sex of the child [8, 13, 16], residence [17, 18], family size [9, 15], number of children under five [18, 19], birth order [9, 15], and breastfeeding status [18, 20] have a significant association with under-five mortality.

Although U5M has been declining it remains high and takes the lives of many children, no research on U5M has been made until recently, with the Ethiopian mini–Demographic Health Survey (EMDHS) 2019. Research is required to inform policies and interventions for the prevention of U5M with the efficient allocation of scarce resources to priority areas based. This study aimed to assess the geographic variation and factors associated with under-five mortality in Ethiopia. The results of our study will assist in achieving the SDG, by providing an understanding of the current burden of U5M in Ethiopia, and its determinants, in addition to identifying areas and factors with the highest burden of U5M in Ethiopia.

Methods

Data source

Our source was the 2019 EMDHS, the second mini demographic health survey (DHS) conducted in Ethiopia (a land-locked country located in the Horn of Africa that lies between the 30N and 150N Latitude or 330E and 480E Longitude) [21]. Data collection was conducted from March 21, 2019, to June 28, 2019, the nine regions (Tigray, Afar, Amhara, Oromia, Somali, Benishangul Gumuz, Southern nation nationalities and People region (SNNPR), Harari, and Gambella) and two administrative cities (Addis Ababa and Dire Dawa). The study design was a population-based cross-sectional study. A frame of all census Enumeration areas (EAs) was used as a sampling frame for the 2019 EMDHS. 149,093 EAs were created which cover an average of 131 Households (HHS). A two-stage stratified cluster sampling technique was employed and each region was stratified into urban and rural areas, yielding 21 sampling strata were selected independently in each stratum. In the first stage, 305 clusters (93 urban and 212 rural) were selected with probability proportional to EAs size and with independent selection in each sampling stratum. In the second stage, a fixed number of 30 households per cluster was selected. Finally, women aged 15–49 in 9,150 (2,790 urban and 6,360 rural) households from 305 clusters were selected. The whole procedure of sampling is found in the full 2019 EMDHS report [21].

Study variables

The outcome variable was under-five mortality status, which was categorized as (child alive: Yes = 0 and No = 1). The age was recorded in months. The community-level predictors, were place of residence, region, community place of delivery, community wealth, community media exposure, and community toilet facility. The individual-level predictors were further categorized as socio-demographic and economic factors like the educational level of the mother, sex of the household head, age of household head, number of household members, number of children under the age of five, marital status of the mother, source of water, time to get water, type of toilet facility, household electricity, types of cooking fuel, main floor material, main wall material, and main roof material and maternal and child factors (such as maternal age at first birth, sex of the child), utilization of contraception, order of birth, mode of delivery, duration of breastfeeding and multiple births (Fig 1).

Fig 1

Conceptual framework of under-five mortality in Ethiopia 2019.

Spatial analysis

The data for spatial analysis was cleaned and merged using STATA version 16 and Microsoft Excel. ArcGIS version 10.8 and saTScan version 9.7 were used for the spatial analysis.

Spatial autocorrelation

Spatial autocorrelation (Global Moran’s I) analysis was conducted to examine whether under-five mortality was dispersed (Moran’s I value closer to -1), clustered (Moran’s I value closer to 1), or randomly distributed (Moran’s I value of 0) in Ethiopia [22].

Spatial interpolation

The under-five mortality was known in enumerated areas, while in areas that were not selected, the under-five mortality rates were predicted. Spatial interpolation was applied using the geostatistical ordinary Kriging spatial interpolation technique to predict under-five mortality from existing sample data points to un-sampled areas [23].

Spatial scan statistics

The scan analysis was performed using SaTscan, based on the Bernoulli test for cases (child is not alive) and controls (child is alive). The upper limit used was the default maximum spatial cluster size of less than 50% of the population, allowing both small and large clusters to be detected, while clusters that contained more than the maximum limit with the circular shape of the window were avoided [24]. Most likely clusters were identified using p-values and likelihood ratio tests, which is the ratio of the likelihood of the alternative hypothesis (higher activity level inside the window) over the likelihood of the null hypothesis (same activity level inside and outside).

Data management and analysis

We used STATA version 16 and R statistical software version 4.0.5 to analyze the data. A total of 31 variables were retained for the analysis. Residents who were not De jure were excluded which affected under-five mortality, as they could not respond to most of the socio-demographic and economic characteristics even though they could answer the maternal and child characteristics. This exclusion changed our sample size from 5,753 to 5,695.

The outcome variable was re-coded to (child alive: Yes = 1 and No = 0). Four community variables were generated by taking the individual variable, calculating their proportion, and dichotomizing them based on their mean or median according to their distribution.

In the end, we had 29 predictor variables, of which 6 were community-level predictor variables and 23 were individual-level predictor variables.

Based on EMDHS, respondents in the same cluster showed similar outcomes or functions at the same level and the data has a hierarchical structure. This made binary logistic regression not the most appropriate as it violates the assumption of independence of the residuals. Instead, a model that considers clustering effect should be used [7, 10]. Multilevel logistic modeling separates the within-cluster effects from the between-cluster effects [25]. Therefore, to assess the predictors associated with U5M, a non-weighted multilevel logistic regression model was used.

Bivariable multilevel logistic regression was used to screen each predictor variable for a p-value less than 0.2. Significant variables were included in multivariable multilevel logistic models. Twenty predictor variables (3 of which were community-level predictor variables) were included in the multivariable analysis. In the multivariable analysis, a p-value less than 0.05 was considered a factor associated with U5M.

The first model fitted was the null model (intercept model), which contained the outcome variable only (under-five mortality status) with the cluster number. The intra-cluster correlation (ICC) was used to assess whether there was a random effect. An ICC of 0.130 which meant there was a minimum of 13% under-five mortality was explained by between-cluster differences. We found that 87% of under-five mortality was explained by within-cluster differences, which was not negligible. The second model was fitted using the outcome, the cluster and the individual-level predictor variables only. The probability of U5M was predicted as a function of individual-level predictors.

For the third model the outcome variable, the cluster number, and the community-level predictor variables were accounted for. Then the final model was fitted by taking both the individual-level and the community-level predictor variables into account.

The models were compared by using a log-likelihood statistic, where the best model was selected based on smallest deviance.

Ethical consideration

Permission for data access was obtained from a major demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifiers.

Results

Socio-demographic background and maternal and child health factor

A total of 5,695 live births were included in this study, of which 2,942 (51.66%) were male, 5,528 (97.07%) were single children, 2,937 (51.57%) were delivered at home and 6.04% of the children were born via cesarean section (Table 1).

Table 1

Sociodemographic, maternal, and child health factors.

Sociodemographic, Maternal and child health factors		Frequency	Percentage
Age of the mother at first birth	Less than 18	3,024	53.10%
	19 to 34	2,650	46.53%
	35 and more	21	0.37%
Sex of child	Male	2,942	51.66%
	Female	2,753	48.34%
Current Contraceptive utilization	Not used	3,875	68.04%
	Used	1,820	31.96%
Multiple births	Singleton	5,528	97.07%
	Multiple	167	2.93%
Birth order	1st order	1,228	21.56%
	2nd and 3rd order	1,883	33.06%
	4th and 5th order	1,298	22.79%
	6th order +	1,286	22.58%
Place where the mother delivered	Home delivery	2,937	51.57%
	Facility delivery	2,758	48.43%
Delivery by cesarean section	No	5,351	93.96%
	Yes	344	6.04%
Months of Breastfeeding	Less than 6 months	918	16.12%
	6 months and more	4,777	83.88%
Sex of household head	Male	4,561	80.09%
	Female	1,134	19.91%
Age of household head (years?)	Less than 35	2,479	43.53%
	35 to 50	2,596	45.58%
	50 and more	620	10.89%
Marital Status	Married	5,346	93.87%
	Unmarried	349	6.13%
Highest educational level	No education	3,135	55.05%
	Primary	1,802	31.64%
	Secondary	467	8.20%
	Higher	291	5.11%
Wealth index	Poor	2,940	51.62%
	Middle	797	13.99%
	Rich	1,958	34.38%
Water source	Improved	3,660	64.27%
	Unimproved	2,035	35.73%
Time to fetch water	More than 30 min	1,765	30.99%
	Less than 30 min	2,896	50.85%
	On premises	1,034	18.16%
Type of latrine used	Improved	1,164	20.44%
	Not improved	4,531	79.56%
Household has electricity	No	4,073	71.52%
	Yes	1,622	28.48%
Cooking fuel in use	Clean fuel	432	7.59%
	Solid fuel	5,263	92.41%
Type of floor of the house	Earth/sand/dung	4,384	76.98%
	Wood	49	0.86%
	Cement	799	14.03%
	Carpet	463	8.13%
Type of the house wall	Cane/Palm/Trunk/Reed	1,261	22.14%
	Bamboo/wood	3,531	62.00%
	Cement/stone with lime/bricks/covered adobe	903	15.86%
Type of the house roof	Thatch	1,488	26.13%
	Palm/bam	995	17.47%
	Cement	198	3.48%
	Corrugated iron	3,014	52.92%

Out of the total children’s mothers, most were married (5,346, 93.87%) and 3,135 (55.05%) had no education, only 291 (5.11%) attained higher education, and 51.62% were of poor economic status. 64.27% had access to improved water; 30.99% travel more than 30 minutes, and 50.85% travel less than 30 min to get water because they do not have water on-premises. The majority of household heads were male, 4,561 (80.09%), and the most common age ranges of the household head was 35–50 years (Table 1).

At the community level, the majority were rural residents 4,389 (77.07%), and 3,457 (60.70%) in the community practiced home delivery. The number of communities that used unimproved toilets was 3,175 (55.75%). And 3,391 residents of the population (59.54%) were not exposed to the media (Table 2).

Table 2

Community-level variable.

Community level variable		frequency	Percentage
Place of residence	Urban	1,306	22.93%
	Rural	4,389	77.07%
Community place of delivery	Home delivery	3,457	60.70%
	Facility delivery	2,238	39.30%
Community toilet utilization	Improved	2,520	44.25%
	Not improved	3,175	55.75%
Community wealth status	Poor	2,776	48.74%
	Rich	2,919	51.26%
Community media exposure	Not exposed to media	3,391	59.54%
	Exposed to media	2,304	40.46%

Prevalence of under-five mortality

The under-five mortality was 5.9% (95% CI 5.4%, 6.6%) and it varied across regions ranging from 2.13% in Addis Ababa to 9.82% in the Somali region. Out of the total, there were 918 (15.79%) children who died having been breastfed for less than 6 months (Table 3).

Table 3

Under-five mortality among predictors.

Characteristics		Is the child alive?		total
		Yes 5357(94.06%)	No338(5.94%)
Is child twin?	Yes	123(2.16%)	44(0.77%)	167(2.93%)
	No	5234(91.9%)	294(5.16%)	5,528(97.06%)
Region	Tigray	436(7.65%)	14(0.24%)	450(7.90%)
	Afar	616(10.81%)	35(0.612%)	651(11.43%)
	Amhara	478(8.39%)	23(0.40%)	501(8.79%)
	Oromia	674(11.83%)	41(0.72%)	715(12.55%)
	Somali	580(10.18%)	57(1.00%)	637(11.18%)
	Benishangul	477(8.37%)	45(0.79%)	522(9.17%)
	SNNPR	631(11.08%)	26(0.46%)	657(11.54%)
	Gambella	409(7.18%)	35(0.61%)	444(7.79%)
	Harari	408(7.16%)	29(0.51%)	437(7.67%)
	Addis Ababa	281(4.93%)	6(0.11%)	287(5.04%)
	Dire Dawa	367(6.44%)	27(0.47%)	394(6.92%)
Breastfeeding	Less than 6 months	773(13.57%)	145(2.55%)	918(16.12%)
	6 months and more	4,584(80.49%)	193(3.39%)	4,777(83.88%)
Birth order	1st order	1,142(20.05%)	86(1.52%)	1,228(21.56%)
	2-3rd order	1,793(31.48%)	90(1.56%)	1,883(33.06%)
	4-5th order	1,231(21.62%)	67(1.18%)	1,298(22.79%)
	6th + order	1,191(20.91%)	95(1.67%)	1,286(22.58%)
Place of delivery	Home delivery	2,733(47.99%)	204(3.58%)	2,937(51.57%)
	Facility delivery	2,624(46.06%)	134(2.35%)	2,758(48.43%)

The global Moran’s index was 0.09, z-score 3.5, and p-value <0.001, suggesting that the spatial distribution under-five mortality was not at random in the 2019 EMDHS (Fig 2).

Fig 2

Spatial autocorrelation of under-five mortality in Ethiopia 2019.

Source: https://open.africa/dataset/africa-shapefiles).

Hotspot analysis

A high proportion of U5M was observed around the regions of Somali and Dire Dawa (Fig 3) [26].

Fig 3

Hotspot analysis of under-five mortality in Ethiopia 2019.

(Source: https://open.africa/dataset/africa-shapefiles).

The spatial interpolation or prediction analysis result showed that the under-five mortality ranged from 0% to 50%. The areas predicted to have the highest risk were located in Somali, Dire Dawa, Benishangul Gumuz, Gambella, and SNNPR (Fig 4) [26].

Fig 4

Empirical bayesian kriging prediction map of under-five mortality in Ethiopia 2019.

(Source: https://open.africa/dataset/africa-shapefiles).

Spatial scan statistics analysis

As observed in Fig 5, significant clusters were identified in the Somali region. Located within 7.384788 N, 45.908936 E with 612.75 km radius. Children living in the primary cluster were 62% more likely to die before they celebrate their fifth birthday when compared to those who lived outside the window (relative risk (1.62), likelihood ratio (7.36), and p-value (0.03) (Fig 5) [26].

Fig 5

Spatial scan statistics of under-five mortality in Ethiopia 2019.

(Source: https://open.africa/dataset/africa-shapefiles).

Factors associated with under-five mortality

After filtering, all variables in bivariable multilevel logistic regression, 4 models were fitted (null model, individual-level model, community-level model, and both individual- and community-level model). The ICC was 0.13 with 95% CI: 0.08 to 0.2, which is not negligible. The fourth model was the best-fitting model with the lowest deviance. When the number of household members increased by one, the odds of under-five mortality decreased by 8.5% (AOR = 0.92, 95% CI: 0.84, 0.99). The odds of under-five death decreased by 82.8% (AOR = 0.17, 95% CI: 0.14, 0.21) as the number of under-five children in the family increased by one. Children who were part of multiple births had 14 times higher odds of under-five mortality (AOR = 14.4, 95% CI: 8.5, 24.3) than singletons. Children who were breastfed for less than 6 months had 5 times higher odds of experiencing under-five mortality (AOR = 5.04, 95% CI: 3.81, 6.67) than those who were breastfed. People whose main roof was palm/bamboo had 43% (AOR = 0.57, 95% CI: 0.34, 0.96) decreased odds of having under-five deaths in the family than those with corrugated iron. Under-five children who were the sixth born or more had 2.5 (AOR = 2.46, 95% CI: 1.49, 4.06) times higher odds of under-five mortality compared to the second and third born. Place of delivery was found to have a significant association with U5M; those who were delivered in institutions had 43% less chance of experiencing U5M (AOR = 0.57, 95% CI: 0.41, 0.81) compared to those delivered at home. Being a resident of the Somali and Afar region increased the odds of under-five death by 3.5 (AOR = 3.46, 95% CI: 1.58, 7.55) and 2.5 (AOR = 2.54, 95% CI: 1.10, 5.85) times, respectively, compared to the Tigray region (Table 4).

Table 4

Multivariable multilevel logistic regression analysis of both individual and community-level factors associated with under-five mortality in Ethiopia, EMDHS 2019.

Variable	Null model	Model2 AOR (95%CI)	Model3 AOR(95%CI)	Model4 AOR(95%CI)
Education
No education		1		1
Primary		1.14 (0.83,1.56)		1.14 (0.83,1.58)
Secondary		0.59 (0.31, 1.13)		0.58(0.30,1.12)
Higher		0.65(0.28, 1.55)		0.60(0.25,1.44)
Household members		0.922(0.85, 1.01)		0.92(0.83,0.99) *
Number of under-five children		0.17 (0.14, 0.22)		0.17(0.14, 0.21) ***
Sex
Male		1		1
female		0.83(0.64, 1.07)		0.84(0.64,1.09)
Mode of delivery by C/S
No		1		1
Yes		1.36 (0.79, 2.35)		1.37(0.79,2.39)
Preceding birth order
1st		1		1
2-3rd		1.32(0.92, 1.91)		1.34(0.93,1.94)
4-5th		1.52(0.97, 2.39)		1.51(0.96,2.38)
6+		2.44(1.48, 4.02)		2.46(1.49,4.06) ***
Time to water
>30min		1		1
<30min		0.77(0.56, 1.04)		0.89(0.64,1.23)
On-premises		0.66(0.38, 1.14)		0.76(0.43,1.34)
Cooking fuel
Clean fuel		1		1
Solid fuel		1.27(0.62, 2.58)		0.93(0.45,1.94)
Age at 1st birth
19–34		1		1
< = 18		0.91(0.70, 1.20)		0.90(0.68,1.18)
> = 35		2.58(0.55, 12.01)		2.72(0.55,13.59)
Breastfeeding
< 6 months		1		1
> = 6 months		5.09(3.85, 6.72)		5.05(3.82,6.67) ***
Place of delivery
Home delivery		1		1
Facility delivery		0.54 (0.40,0.75)		0.57(0.41,0.81) **
Main floor
earth/sand/dung		1		1
Wood		1.71(0.53, 5.52)		2.14(0.66,6.95)
Cement		0.89(0.50, 1.59)		0.86(0.48,1.58)
Carpet		1.56(0.85, 2.86)		1.28(0.67,2.46)
Main wall
bamboo/wood		1		1
cane/palm/trunks/re ed		1.42(0.97, 2.07)		1.06(0.70,1.58)
cement/stone		1.53(0.89,2.61)		1.34(0.76,2.34)
Main roof
Corrugated iron		1
thatch		1.25(0.87,1.79)		1.14(0.77,1.67)
palm/bamboo		0.71(0.44,1.15)		0.57(0.34,0.96)*
calamine/cement		1.14(0.54,2.44)		1.04(0.47,2.29)
Contraceptive utilization
No		1		1
Yes		0.72(0.53,0.98)		0.82(0.60,1.13)
Marital status
Currently Married		1		1
Currently Unmarried		1.19(0.74,1.92)		1.22(0.75,1.97)
Multiple Twin child
No		1		1
Yes		15.63(9.33,26.20)		14.39(8.51,24.33)***
Region
Tigray			1	1
Afar			1.60(0.76,3.41)	2.55(1.10,5.85)*
Amhara			1.47(0.68,3.19)	0.97(0.44,2.15)
Oromia			1.73(0.84,3.55)	1.52(0.73,3.15)
Somali			2.86(1.39,5.89)	3.46(1.59,7.55)**
Benishangul gumuz			2.74(1.32,5.68)	1.95(0.91,4.20)
SNNPR			1.17(.55,2.47)	0.94(0.44,2.03)
Gambella			2.71(1.29,5.69)	1.94(0.90,4.21)
Harari			2.37(1.12,5.04)	1.95(0.87,4.36)
Addis Ababa			0.71(0.25,2.03)	0.64(0.21,2.02)
Dire Dawa			2.18(1.01,4.70)	2.02(0.91,4.50)
Community delivery place
Home delivery			1	1
Facility delivery			0.75(0.52, 1.09)	0.94(0.62,1.43)
Community media
Not exposed			1	1
exposed			1.09(0.75,1.58)	0.94(0.64,1.40)
Intercept	0.49(0.30,0.81)	0.22(0.08,0.63)	0.35(0.19,0.64)	0.16(0.04,0.57)
ICC	0.130	0.036	0.091	0.025
Log-likelihood	-1263.56	-918.15	-1247.40	-903.72

*** P-value<0.001,

** p-value<0.01,

* p-value<0.05

The residual plot shows that the model performed adequately, with the Kolmogorov smirnov (KS) test being insignificant (p-value = 0.29), and also the deviation was not significant (Fig 6).

Fig 6

residual diagnostic plot for the final model of under-five mortality in Ethiopia 2019.

Discussion

The study mainly focused on the spatial distribution of under-five mortality and on factors that influence in Ethiopia. The findings of this study revealed that the prevalence of under-five mortality was 5.9% (95% CI: 5.4, 6.6). The multivariable multilevel logistic regression model result showed that family size, number of under-five children, birth order, breastfeeding, multiple birth, region, and institutional delivery were associated with under-five mortality in Ethiopia. The spatial analysis result revealed that Somali, Dire Dawa and Eastern Oromia were high-risk areas for under five mortality in Ethiopia.

Increased number of family size resulted in decreased odds of under-five death. This finding is consistent with previous studies conducted in Ethiopia [16], Eastern Nigeria [27], and Ghana [28]. This may be due to the mother of the children being more experienced in taking good and adequate care of the children as the family size increases, in addition to there being more family members that can care for under-five children. However, this is in contrast to another study conducted in Ethiopia, which demonstrated that the odds of under-five mortality increased as the number of under-five children increased [18]. This may indicate that, as the number of under-five children increases, it may become difficult to fulfill their basic needs, and family exhausted to look after the children.

Birth order had significant effect on under-five mortality. This study indicates that, as order of birth increased, the likelihood of child death also increased. This finding is consistent with a study conducted in rural parts of Ethiopia [9]. However, it does not support another study conducted in Ethiopia which found that being firstborn increased the odds of under-five mortality twofold when compared to those with five and above birth orders [18]. This may be because firstborns are associated with younger and less experienced mothers, which may be related to increased mortality. In addition, firstborns are more likely to be hospitalized as a result of congenital malformations and perinatal conditions in their early childhood [29]. This changes for firstborn children as they get older, while younger brothers and sisters are more likely to be hospitalized for injuries and avoidable conditions, indicating less parental care [30].

Being breastfed for less than six months increased the odds of under-five mortality compared with those who were breastfed for six and more months. This is consistent with a finding in Ethiopia [18]. Breastfeeding is known as a preventive method for reducing child mortality and as well as a prevention for delayed growth [31, 32].

Being multiple birth children increased the odds of under-five mortality when compared to singleton children. This finding is in line with several studies conducted in Ethiopia at different times, and study conducted in rural parts of Ethiopia [6, 7, 9, 16, 18]. This may be due to multiple births imposing short- and long-term medical complications as well as adding increased burden on the mother and the family [33].

Facility or institutional delivery reduced the odds of under-five mortality by 43% as compared to home delivery. This finding is consistent with studies conducted in Ethiopia [9, 10] and the Democratic Republic of Congo [13]. This is because home delivery increases the death of under-five children due to a higher risk of intrapartum and postpartum complications. These complications include exposure to common vaccine-preventable diseases if there is absence of vaccination, and a lower likelihood of attending prenatal and postpartum visits [10, 34].

The odds of under-five mortality were higher for residents of Afar and Somali regions compared to the Tigray region. This coincides among other things, with Somali and Afar regions having the first and second lowest coverage of children with all basic as well as second and first prevalence respectively [35, 36]. Additionally, the Somali region was one of the regions that were in the primary cluster from our spatial scan statistics analysis where under-five mortality is more likely to occur. This is not supported by studies conducted in Ethiopia based on the 2011 and 2016 EDHS [9, 10], where other regions were found to have a significant association with under-five mortality. This discrepancy may be a result of the different number of enumerated areas (clusters) used in the different studies, which may have led to different samples, or different statistical models used for analysis.

Conclusion

Under-five mortality in Ethiopia was 5.9% of livebirth. Being breastfed for more than 6 months, twin birth, institutional delivery and high-risk areas of U5M (Somali and dire Dawa) were modifiable risk factors. Therefore, maternal and community education on the advantage of breastfeeding and institutional delivery is highly recommended. Women who deliver multiple births should be given special attention. Further, effective strategies should be designed for interventions on hot spot areas of U5M, namely Somali and Dire Dawa.

Strengths and limitations of the study

We used EMDHS 2019 data which is more inclusive and generalizable as Enumeration Areas (EAs) are taken from most parts of the country. The larger sample size increased the power of the study and helped to make geographical comparisons. As this was cross-sectional, factors associated are not necessarily of causal effects, since results do not show a temporal relationship. Additionally, potentially important variables such as antenatal care visits, postnatal care, and preceding birth interval were excluded from the study due to their high rate of missing values, hence the study may missing important predictive factors associated with U5M.

7 Dec 2021

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Reviewer #1: Overall authors have explained regional variation in under five mortality and the key predictors of U5M in Ethiopia. I have few comments to improve this paper further.

1. Authors have used random intercept model for binary multilevel logistic regression. Authors can also do some more testing of their model by including random slopes for their significant predictors in this model to check if the effect of their key predictors remains the same for each cluster or is it different. If they don't found any major difference by including the random slopes, then they can provide the current results as it is.

2. Authors have used different household condition indicators such as toilet clean fuel main roof etc than wealth index which captures the economic condition of the household better in the analysis. Is there any specific reason to adapt this strategy?

3. Authors should also provide the residuals plots for their final multilevel model in the appendix.

4. Authors have shown that there is a significant large cluster of population where U5MR is significantly higher than other areas in Figure 4. Authors can include a table with basic socio demographic profile of this high cluster region. This can be helpful to understand which population mostly suffers with high U5M in that region so that specific interventions and programmes can be targeted for those subgroups of population.

5. Authors should include a short paragraph in the discussion on the implications of their findings in context of the SDG goals.

6. Please see the flow of your texts in the discussion once more and edit the sentences wherever required for more clarity.

Reviewer #2: This publication is of high importance because it attempts to present sophisticated analyses of a legitimate population-based survey data to highlight key risk factors for under five mortality in Ethiopia. However, there are two problems with the manuscript: the first is that it is poorly written in English, which makes understanding of all processed hard; secondly, there are some explanations about the statistics used that (partly due to the poor English) are difficult to understand. Double check both the English and the statistical methods used (binary, bivariable?, multi-level logistic regression?).

Reviewer #3: This is an engaging, insightful paper and mostly methodologically sound paper (see bellow).

There is one important thing to resolve. The authors need to explain why the geospatial analysis was pertinent. Sure, it produced great maps, but the inferences that made it to the Discussion and Conclusion sections could have been made with simple aggregation instead of the employed GIS methods. If this is not the case, the authors need to explain why their approach was used and discuss that in the Discussion section.

The paper should undergo professional proofreading as some formulations were strange.

There were some minor mistakes and suggestions, which were marked and commented on in the attached PDF.

Reviewer #4: I have reviewed the manuscript and it brings a perspective of spatial and multilevel analysis to understanding the variations in under-five mortality in Ethiopia. The multilevel analysis introduces community level variables not examined before enabling the study to consider factors beyond individual ones. The study uses more recent data. The method of data analysis, that is, multivariate multilevel logistic regression is fine but its limitation is that it does not handle the aspect of censored data well. In performing the analysis, it is not clear in the manuscript if the data were weighted or not. The data source section (page 4, lines 91-108), this could have been summarized further and high light the information collected by the 2019 EMDHS relevant to study. On study variables (page 5, lines 112-122), the presentation of the variables could have been efficiently done in a table with definitions and categorization. More especially for community level variables. There are too many variables considered at individual level. Is it possible to reduce them to relevant ones only based on reviewed literature? For spatial analysis (page 5, lines 124-125), state what was actually done. Highlight the benefits and limitations of using multilevel analysis. In the results section (pages 8-9), is possible to use 1 decimal place for the percentages in the text. Also, check the table titles and ensure they are named appropriately. For multilevel analysis results, the intra-cluster correlation (ICC) is not shown in the table. A theoretical framework work should have been considered by the study. In the discussion section of the manuscript, emphasis should have been on discussing community level factors and the geographical variation in under-five mortality observed. The manuscript should have discussed if the under-five mortality rates in the 2019 EMDHS were reproduced for bench marking prior to deriving its own. The conclusion and recommendations of the manuscript should have been based on the multilevel analysis (community level variables) results in addition to the spatial variation findings on under-five mortality.

Reviewer #5: This paper is well motivated and carries out a series of multivariate and spatial analysis on Ethiopian data on under 5 child mortality. The survey data appears to be of excellent quality from a rigorous sampling design. The spatial analysis is a strong point and allows subregional rates to be detected that are higher or lower than the background. Spatial scan statistics provide a promising approach in this case and are good complements to the other approaches. The authors should properly cite the SaTScan software, along with the author M.Kulldforf (see the user manual). I would encourage citing Stata properly as well (note spelling and case).

In my experience, SaTScan runs need quite a bit of tinkering of the parameter file to get good hot spot representation (i.e., like that in the spatial EB approach) . I think the cluster detection can be improved by selecting a smaller window size or radius in order to detect smaller secondary clusters other than the primary two presented here. Otherwise, this is an informative paper on an important topic. Table 4 could use better formatting. Please consider using coefficient plots. Additional proofing of the manuscript is also recommended to improve clarity.

Reviewer #6: This is a thoughtful and interesting analysis of under five mortality in Ethiopia in the years preceding the current crisis and civil war. The analysis focusses on the major individual and area level variables that have been identified in the literature and the results are largely in line with previous findings. While my overall evaluation is positive there are a number of issues that need to be addressed before publication:

1. The style of writing is extremely telegraphic, to the point that at times the meaning is not strictly clear. For instance, your community level variables refer to place of delivery; toilet use; wealth and media exposure. I presume these are based on majority characteristics from your individual level analysis, but this should be made clearer. In particular, I see no reference to media exposure at the individual level, so what does this variable refer to?

2. A major contribution of this paper is the difference between the different areas of Ethiopia and the relation to community characteristics. It would be important, for non-Ethiopian readers, to provide a bit more background on the different regions, socially and historically. A comparison based on the community characteristics you describe would be particularly useful. You should also provide references for the spatial analysis you present (Moran’s I and the spatial interpolation).

3. Your choice of model (multilevel binomial) is appropriate but the presentation is a bit overloaded. Many of the variables appear to be non-significant in the multivariate model, even if they were significant at the bivariate level. This may be due to collinearity between the variables. Removing the non-significant variables would bring out better the major covariates related to U5M. It is also important to indicate that the coefficients are relative risks (eb). There is no need for both log-likelihood and deviance values (as you need, D = -2LL) but you should indicate the degrees of freedom and the variance of the random effects (clusters) as well as the number of individual cases (level 1) and clusters (level 2).

4. Your discussion focusses on the major factors reducing under 5 mortality, but you do not discuss differences between the models. For instance, use of solid fuel increases U5M in model 2 and reduces it in model 4; Amhara has higher mortality in model 3, lower mortality in model 4. In both cases, the coefficients are non significant (within the CI) but the reverse should still be mentioned (also see previous comment on removing non-significant comparisons).

5. Some of the comments in your discussion appear to be inconsistent. For instance, larger families have lower mortality, but so do higher birth orders. You mention mother’s age at first birth but not at current birth, which may be important in explaining this paradox. You rightly identify risk factors subject to intervention, such as short breast feeding or home delivery, but I fail to see how twin births fall into this category, except in the sense that greater attention should be given to mothers with multiple births.

In sum, this is a valuable contribution which I hope to see published, and my comments are intended to strengthen what is, at heart, a sound analysis based on a rich and valuable dataset.

Reviewer #7: The paper is sound and generallly well-written. The topic is very relevant and the analyses performed compatible and consistent with the research question. I have some minor remarks that I consider should be addressed before approving for publication:

Overall, please explain a bit further the difference between the mini DHS survey and the main DHS survey. Why do you think this new data is helpful to track the trends in U5M. The authors extensively explain the sampling design, but do not explain the difference between the mini and the main DHS survey, which would be helpful in interpreting the results. Is the mini DHS survey comparable to the main DHS survey? Can we use this to compare them in time?

In addition, it would be important to know whether any data quality assessment of deaths was performed before the regression analysis. Would any bias be added to the study due to misreporting of deaths in the survey or issues related to coverage areas? Did the authors correct the registry of deaths in any way or were deaths under 5 taken as it is from the survey? How do the authors feel about this data and do they trust the information?

In addition, some minor remarks/suggestions in the writing part that felt confusing:

lines 82- 84: the sentence is confusing. I would add here a period. and Say: "Though U5M has been declining, it is still high and is taking the lives of many children. Despite this fact, no research on U5M has been done with recent data available from the Ethiopian mini ... "

lines 85-87: similarly, the sentence is confusing. I would suggest: "...to show the current burden of U5M that is crucial for better planning different policies and interventions for U5M prevention. This allows for an efficient allocation of scarce resources according to spatial..."

lines 188: suggestion to make the sentence clearer: "Sociodemographic background" and not "background socio-demographic"

line 195: correct to : "are of poor economic status"

Reviewer #8: This paper aims to examine geographic variation and factors attributable to Under-Five Mortality (UFM) in Ethiopia. Recognizing as an important marker for health equity and access, UFM is considered the best proxy measure or indicator for socioeconomic development. In addition, child mortality rate is also a useful marker of overall development and a Millennium Development Goal (MDG) indicator and its importance has been further emphasized in an ambitious target under the Sustainable Development Goals. Generally, literature search reveals numerous or countless studies on factors determining UFM conducted in the Sub-Saharan African countries, however, spatial distributions and geographic variations are less investigated compared to the UFM factors.

The data sources used, statistical tests and analysis performed systematically support, but NOT to the full extent, in answering the research questions and its intended aims or objectives. Results and conclusion made in relation to research aims and objectives or intentions are supported by data and analysis, in general. However, there needs further refinement and improvement for author(s) to take into the consideration to make this paper more added-value to the knowledge and literature.

I foresee two major drawbacks of this study, which are described below:

1. The Primary Investigator should not limit the independent variables to individual and community level predictors. It is suggested to take into the consideration of inclusion of health interventions, such as malaria, sanitation and hygiene (WASH), reproductive health (RH), vaccinations, micronutrient supplementation and treatments. Most studies have revealed that there are significant association between these health interventions and reduction of UFM. For example, BCG, OPV, Measles, TT, etc. vaccinations have significantly contributed to drastic reduction in deaths of children in most developing countries. Furthermore, measure DHS captures or collects information on health interventions; and it's worthwhile to examine its association with UFM in Ethiopia.

2. Since the other known predictors of child morality are generally attributable to food security and accessibility in conflict areas thus, it is recommended to discuss how such conflicts or disruptions would influence or alter the geographic variations of UFM in Ethiopia? Other risk factors to take into considerations are: indoor air pollution (determined by type of cooking fuel used in household), nutrition, access to basic health services (ANC, PNC, FP, RH, etc.), poverty status by regions, fertility rates, among others.

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Reviewer #6: Yes: Jon Anson

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1 Jun 2022

PLOS ONE

Point by point response for editors/reviewers’ comments

The manuscript title “Geographic variation and factors associated with under-five mortality in 2 Ethiopia. A spatial and multilevel analysis of Ethiopian Mini Demographic and 3 Health Surveys 2019”

Manuscript number: PONE-D-21-20589

Dear editor/reviewer,

I would like to thank you for these constructive, building, and improvable comments on this manuscript that would improve the substance and content of the manuscript. We considered each comment and clarification question of reviewers on the manuscript thoroughly. My point-by-point responses for each comment and question are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Reviewer comment and authors response

Reviewer #1:

General comments:

Reviewer's comment: - Overall authors have explained regional variation in under five mortality and the key predictors of U5M in Ethiopia. I have few comments to improve this paper further.

Reviewer's comment:

1. Authors have used random intercept model for binary multilevel logistic regression. Authors can also do some more testing of their model by including random slopes for their significant predictors in this model to check if the effect of their key predictors remains the same for each cluster or is it different. If they don't find any major difference by including the random slopes, then they can provide the current results as it is.

Authors response: thanks for the detailed comment we have tested using the random slop in checking its effect and didn’t find noticeable difference from the random intercept model.

Reviewer's comment:

2. Authors have used different household condition indicators such as toilet clean fuel main roof etc. than wealth index which captures the economic condition of the household better in the analysis. Is there any specific reason to adapt this strategy?

Authors response: Thank you very much for your comment. We have considered using the wealth index as an economic condition of the household in our analysis, and the wealth index variable failed to enter the multivariant multilevel mixed model since the significant level was high during the univariable multilevel mixed model.

Reviewer's comment:

3. Authors should also provide the residuals plots for their final multilevel model in the appendix.

Authors response: thanks again for the detailed comment, we have provided the residual plots for the final model in figure 6 and on page 10, line 248-249, kindly refer to the revised manuscript.

Reviewer's comment:

4. Authors have shown that there is a significant large cluster of population where U5MR is significantly higher than other areas in Figure 4. Authors can include a table with basic socio demographic profile of this high cluster region. This can be helpful to understand which population mostly suffers with high U5M in that region so that specific interventions and programmes can be targeted for those subgroups of population.

Author’s response: Thank you very much for you very constructive comment. In the descriptive statistics section example in table one already reported. We believe that no need to include the specific socio-demographic characteristics of the hot sport areas. If there any help for us we will do it in next round revision.

Reviewer's comment:

5. Authors should include a short paragraph in the discussion on the implications of their findings in context of the SDG goals.

Authors response: Thank you very much for your comment. We have accepted the comment and kindly refer to the revised manuscript specified on page 10, line 258 up to 260.

Reviewer's comment:

6. Please see the flow of your texts in the discussion once more and edit the sentences wherever required for more clarity.

Authors response: Thank you very much for your comment. We have accepted the comment and kindly refer to the description part of the revised manuscript.

Reviewer #2:

General comments:

Reviewer's comment: - This publication is of high importance because it attempts to present sophisticated analyses of a legitimate population-based survey data to highlight key risk factors for under five mortalities in Ethiopia. However, there are two problems with the manuscript: the first is that it is poorly written in English, which makes understanding of all processed hard; secondly, there are some explanations about the statistics used that (partly due to the poor English) are difficult to understand. Double check both the English and the statistical methods used (binary, bivariable? multi-level logistic regression?).

Reviewer response: thank you for your comment. We accept and kindly find the revised manuscript. When we say binary, we want to explain that our outcome variable has only the possibility of failure or success, and also with regards to bivariable, we wanted to illustrate that we first run a multilevel logistic regression model consisting of the outcome variable and one predictor variable for each predictor variables then choosing those predictors that have passed our significant level (less than the P-value of 0.2) to the multivariable multilevel logistic regression model. We used multilevel logistic regression due to the hierarchical nature of the data.

Reviewer #3:

General comments:

Reviewer's comment: - This is an engaging, insightful paper and mostly methodologically sound paper.

There is one important thing to resolve. The authors need to explain why the geospatial analysis was pertinent. Sure, it produced great maps, but the inferences that made it to the Discussion and Conclusion sections could have been made with simple aggregation instead of the employed GIS methods. If this is not the case, the authors need to explain why their approach was used and discuss that in the Discussion section.

Authors response: thank you for your detailed comment, we accepted and the main reason we employed GIS is for cost minimization and to help the policymakers to focus on the arias noted by GIS and saTscan, we have also edited in the discussion section specifically on page 11, line 300-302 in the revised manuscript.

Reviewer's comment:

The paper should undergo professional proofreading as some formulations were strange.

There were some minor mistakes and suggestions, which were marked and commented on in the attached PDF.

Authors response: thank you for your comments. We accept and kindly find the revised manuscript.

Reviewer #4

Reviewer's comment: - I have reviewed the manuscript and it brings a perspective of spatial and multilevel analysis to understanding the variations in under-five mortality in Ethiopia. The multilevel analysis introduces community level variables not examined before enabling the study to consider factors beyond individual ones.

The study uses more recent data. The method of data analysis, that is, multivariate multilevel logistic regression is fine but its limitation is that it does not handle the aspect of censored data well.

Authors response: thanks for the comment, our data is cross-sectional hierarchical data with a binary outcome and for this type of data multilevel logistic regression is the best method of data analysis.

Reviewer's comment:

In performing the analysis, it is not clear in the manuscript if the data were weighted or not.

Authors response: thanks for the comment we have accepted and revised it kindly find on page 6 line 166 up to line 167 in the revised manuscript.

Reviewer's comment:

The data source section (page 4, lines 91-108), could have been summarized further and high light the information collected by the 2019 EMDHS relevant to study.

Authors response: thanks for the comment we accepted and kindly refer to the revised manuscript specifically (page 4, lines 91- 97).

Reviewer's comment:

On study variables (page 5, lines 112-122), the presentation of the variables could have been efficiently done in a table with definitions and categorization. More especially for community level variables.

Authors response: thank you for your comment, we have considered using the table to illustrate the study variables and since the variables are presented in table 1 up to 3 additional tables, we feared that an additional table might be a bit redundant.

Reviewer's comment:

There are too many variables considered at individual level. Is it possible to reduce them to relevant ones only based on reviewed literature?

Authors response: thanks for your detailed comment, all of our variables are from literature reviews and we have reduced many not significant variables during the bivariable multilevel logistic regression.

Reviewer's comment:

For spatial analysis (page 5, lines 124-125), state what was actually done. Highlight the benefits and limitations of using multilevel analysis.

Authors response: thank you for the detailed comment the spatial analysis that was employed are reported in detail on page 5-6, line 127-145.

Reviewer's comment:

In the results section (pages 8-9), is possible to use 1 decimal place for the percentages in the text. Also, check the table titles and ensure they are named appropriately.

Authors response: thanks for the detailed comment, we have considered using one decimal place and since we wanted to be precise as possible that is why we used two decimal places.

Reviewer's comment:

For multilevel analysis results, the intra-cluster correlation (ICC) is not shown in the table.

Authors response: thanks for the detailed comment, we accept and kindly find the revised table on page 24 in the revised manuscript.

Reviewer's comment:

A theoretical framework work should have been considered by the study.

Authors response: thank you for the detailed comment, we have provided the theoretical framework kindly find the revised manuscript specifically in figure 1.

Reviewer's comment:

In the discussion section of the manuscript, emphasis should have been on discussing community level factors and the geographical variation in under-five mortality observed.

Authors' response: thanks for the detailed comment, we only discussed the community factors that are significantly associated with under-five mortality, and most of the community-level factors are not significantly associated with under-five mortality.

Reviewer's comment:

The manuscript should have discussed if the under-five mortality rates in the 2019 EMDHS were reproduced for benchmarking prior to deriving its own.

Authors response: thanks for your comment we have discussed benchmarking prior to EDHS, kindly find it on page 10 between the lines 258-261.

Reviewer's comment:

The conclusion and recommendations of the manuscript should have been based on the multilevel analysis (community level variables) results in addition to the spatial variation findings on under-five mortality.

Authors' response: thanks for the detailed comment, most of the community-level variables were not significantly associated with under-five mortality that is why we didn’t give focused on the community-level variables in the conclusion.

Reviewer #5

Reviewer's comment: This paper is well motivated and carries out a series of multivariate and spatial analysis on Ethiopian data on under 5 child mortality. The survey data appears to be of excellent quality from a rigorous sampling design. The spatial analysis is a strong point and allows subregional rates to be detected that are higher or lower than the background. Spatial scan statistics provide a promising approach in this case and are good complements to the other approaches.

The authors should properly cite the SaTScan software, along with the author M.Kulldforf (see the user manual). I would encourage citing Stata properly as well (note spelling and case).

Authors response: thank you for your detailed comment, we accepted kindly refer to the revised manuscript specifically page 6, lines 139 and 143.

Reviewer's comment:

In my experience, SaTScan runs need quite a bit of tinkering of the parameter file to get good hot spot representation (i.e., like that in the spatial EB approach) . I think the cluster detection can be improved by selecting a smaller window size or radius in order to detect smaller secondary clusters other than the primary two presented here.

Authors response: thanks for the detailed comment, we agree that the cluster can be improved by selecting a smaller window size to detect smaller secondary clusters but we aim to identify the primary ones that is why we used the default maximum spatial cluster size of less than 50% of the population, which allows both small and large clusters to be detected.

Reviewer's comment:

Otherwise, this is an informative paper on an important topic. Table 4 could use better formatting. Please consider using coefficient plots. Additional proofing of the manuscript is also recommended to improve clarity.

Authors response: thank you for your detailed comment, we have improved the clarity kindly refer to the revised manuscript. It known that it possible to report using coefficient plot but we believe table four format appropriate in this regard.

Reviewer #6

Reviewer's comment:

This is a thoughtful and interesting analysis of under-five mortality in Ethiopia in the years preceding the current crisis and civil war. The analysis focusses on the major individual and area level variables that have been identified in the literature and the results are largely in line with previous findings. While my overall evaluation is positive there are a number of issues that need to be addressed before publication:

1. The style of writing is extremely telegraphic, to the point that at times the meaning is not strictly clear. For instance, your community level variables refer to place of delivery; toilet use; wealth and media exposure. I presume these are based on majority characteristics from your individual level analysis, but this should be made clearer. In particular, I see no reference to media exposure at the individual level, so what does this variable refer to?

Authors response: thank you for your detailed comment, we accepted and kindly refer to the revised manuscript specifically page 5, line 115-123.

Reviewer's comment:

2. A major contribution of this paper is the difference between the different areas of Ethiopia and the relation to community characteristics. It would be important, for non-Ethiopian readers, to provide a bit more background on the different regions, socially and historically. A comparison based on the community characteristics you describe would be particularly useful. You should also provide references for the spatial analysis you present (Moran’s I and the spatial interpolation).

Authors response: thank you for your comment, we didn’t find any significant community characteristics that’s why we didn’t compare based on the community characteristics. Also, we accepted and kindly find the revised manuscript for the edited reference on pages 5, line 130, and 135 respectively.

Reviewer's comment:

3. Your choice of model (multilevel binomial) is appropriate but the presentation is a bit overloaded. Many of the variables appear to be non-significant in the multivariate model, even if they were significant at the bivariate level. This may be due to collinearity between the variables. Removing the non-significant variables would bring out better the major covariates related to U5M.

Authors response: thanks for the detailed comment, we have checked for collinearity and there was no noticeable collinearity between variables.

Reviewer's comment:

It is also important to indicate that the coefficients are relative risks (eb). There is no need for both log-likelihood and deviance values (as you need, D = -2LL) but you should indicate the degrees of freedom and the variance of the random effects (clusters) as well as the number of individual cases (level 1) and clusters (level 2).

Authors response: thanks for the detailed comment, we accept we have edited out the deviance from the table kindly refer to the revised table 4 in the revised manuscript. As for the number of individual cases and clusters we thought it would be repetition since it has been mentioned in the data source page 4, lines 101 and 105, and also on the result section on page 8, line 194.

Reviewer's comment:

4. Your discussion focusses on the major factors reducing under 5 mortalities, but you do not discuss differences between the models. For instance, use of solid fuel increases U5M in model 2 and reduces it in model 4; Amhara has higher mortality in model 3, lower mortality in model 4. In both cases, the coefficients are non-significant (within the CI) but the reverse should still be mentioned (also see previous comment on removing non-significant comparisons).

Authors response: thanks for the detailed comment. Indeed it is advantageous to discuss the differences between the models, we feer that the paper might loos its pertinence.

Reviewer's comment:

5. Some of the comments in your discussion appear to be inconsistent. For instance, larger families have lower mortality, but so do higher birth orders. You mention mother’s age at first birth but not at current birth, which may be important in explaining this paradox. You rightly identify risk factors subject to intervention, such as short breast feeding or home delivery, but I fail to see how twin births fall into this category, except in the sense that greater attention should be given to mothers with multiple births.

Authors response: thanks for the comment, Since also multiple births are prone to develop short to long-term complications kindly refer to the citation (32). We accept and we edited in multiple births and compared it with singleton birth kindly refer to the revised manuscript specifically on page 12, line 313.

Reviewer's comment:

In sum, this is a valuable contribution which I hope to see published, and my comments are intended to strengthen what is, at heart, a sound analysis based on a rich and valuable dataset.

Reviewer #7

Reviewer's comment:

The paper is sound and generally well-written. The topic is very relevant and the analyses performed compatible and consistent with the research question. I have some minor remarks that I consider should be addressed before approving for publication:

Overall, please explain a bit further the difference between the mini-DHS survey and the main DHS survey. Why do you think this new data is helpful to track the trends in U5M? The authors extensively explain the sampling design, but do not explain the difference between the mini and the main DHS survey, which would be helpful in interpreting the results. Is the mini-DHS survey comparable to the main DHS survey? Can we use this to compare them in time?

Authors response: thanks for the detailed comment. Indeed it is advantageous to discuss the differences between the mini-DHS and main DHS, we feer that the paper might loos its pertinence.

Reviewer's comment:

In addition, it would be important to know whether any data quality assessment of deaths was performed before the regression analysis. Would any bias be added to the study due to misreporting of deaths in the survey or issues related to coverage areas? Did the authors correct the registry of deaths in any way or were deaths under 5 taken as it is from the survey? How do the authors feel about this data and do they trust the information?

Authors response: Thank you very much for your ideal comment. Frankly speaking, we authors had no any room to check death registry. Since is secondary data and there is no strong death registry in the country it is difficult to check death by any other means. Before the data is collected training was given for data collectors this may be one way data quality assurance. The second it large survey and conducted carefully with expertise and representative of the country under-five mortality. The information is trustful.

Reviewer's comment:

In addition, some minor remarks/suggestions in the writing part that felt confusing:

lines 82- 84: the sentence is confusing. I would add here a period. and Say: "Though U5M has been declining, it is still high and is taking the lives of many children. Despite this fact, no research on U5M has been done with recent data available from the Ethiopian mini ... "

Authors response: thank you for your detailed comment, we accepted and kindly find the edited statements on pages 3 and 4, lines 81 – 84.

Reviewer's comment:

lines 85-87: similarly, the sentence is confusing. I would suggest: "...to show the current burden of U5M that is crucial for better planning different policies and interventions for U5M prevention. This allows for an efficient allocation of scarce resources according to spatial..."

Authors response: thank you for your detailed comment, we accepted and kindly find the edited statements on page 4 lines 85 – 88.

Reviewer's comment:

lines 188: suggestion to make the sentence clearer: "Sociodemographic background" and not "background socio-demographic"

Authors response: thank you for your detailed comment, we accepted and kindly find the edited statements on page 8, line 193.

Reviewer's comment:

line 195: correct to: "are of poor economic status"

Authors response: thank you for your detailed comment, we accepted and kindly find the edited statements on page 8, line 199.

Reviewer #8

Reviewer's comment:

This paper aims to examine geographic variation and factors attributable to Under-Five Mortality (UFM) in Ethiopia. Recognizing as an important marker for health equity and access, UFM is considered the best proxy measure or indicator for socioeconomic development. In addition, child mortality rate is also a useful marker of overall development and a Millennium Development Goal (MDG) indicator and its importance has been further emphasized in an ambitious target under the Sustainable Development Goals. Generally, literature search reveals numerous or countless studies on factors determining UFM conducted in the Sub-Saharan African countries, however, spatial distributions and geographic variations are less investigated compared to the UFM factors.

The data sources used, statistical tests and analysis performed systematically support, but NOT to the full extent, in answering the research questions and its intended aims or objectives. Results and conclusion made in relation to research aims and objectives or intentions are supported by data and analysis, in general. However, there needs further refinement and improvement for author(s) to take into the consideration to make this paper more added-value to the knowledge and literature.

I foresee two major drawbacks of this study, which are described below:

Reviewer's comment:

1. The Primary Investigator should not limit the independent variables to individual and community level predictors. It is suggested to take into the consideration of inclusion of health interventions, such as malaria, sanitation and hygiene (WASH), reproductive health (RH), vaccinations, micronutrient supplementation and treatments. Most studies have revealed that there are significant association between these health interventions and reduction of UFM. For example, BCG, OPV, Measles, TT, etc. vaccinations have significantly contributed to drastic reduction in deaths of children in most developing countries. Furthermore, measure DHS captures or collects information on health interventions; and it's worthwhile to examine its association with UFM in Ethiopia.

Authors response: thank you for the detailed comments, we have considered using the above-listed variables, and some we used like WASH variables (time to water), and contraceptive utilization, but still some variables like (water source and toilet type) were not able to pass the bivariable multilevel logistic regression and some variables like (BCG, OPV, measles, and TT) were no available in the EMDHS data.

Reviewer's comment:

2. Since the other known predictors of child morality are generally attributable to food security and accessibility in conflict areas thus, it is recommended to discuss how such conflicts or disruptions would influence or alter the geographic variations of UFM in Ethiopia? Other risk factors to take into considerations are: indoor air pollution (determined by type of cooking fuel used in household), nutrition, access to basic health services (ANC, PNC, FP, RH, etc.), poverty status by regions, fertility rates, among others.

Authors response: thank you for the detailed comments, the EMDHS data doesn’t possess the data that is attributable to food security and whether the EA was a conflict area at the time of collecting the data. additionally, we have considered indoor air pollution and added the variable of cooking fuel to the final models but the results were insignificant and we didn’t want to discuss its results. We have also considered basic health services and most were insignificant during the bivariable multilevel logistic regression model and were not added to the multivariable multilevel logistic regression model.

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7 Jul 2022

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Several reviewers have seen this document, and have provided lots of editorial and technical feedback. This is an article that has sufficient merit for publication, for global as well as Ethiopian interest. However, despite SEVERAL attempts and specific feedback from reviewers, the authors have failed to submit the manuscript to someone with good English writing skills, so it can be written in "standard English" as is clearly stated in criteria 5 for PLOS ONE publication.

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1 Aug 2022

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Point by point response for editors/reviewers’ comments

The manuscript title “Geographic variation and factors associated with under-five mortality in Ethiopia. A spatial and multilevel analysis of Ethiopian Mini Demographic and Health Surveys 2019”

Manuscript number: PONE-D-21-20589R2

Dear editor/reviewer,

I would like to thank you for these constructive, building, and improvable comments on this manuscript that would improve the substance and content of the manuscript. We considered each comment and clarification question of reviewers on the manuscript thoroughly. My point-by-point responses for each comment and question are described in detail on the following pages. Further, the details of changes were shown by track changes in the supplementary document attached.

Reviewer comment and authors response

Reviewer #1:

General comments:

Reviewer's comment: - THIS IS A GOOD STUDY BUT IS TERRIBLY WRITTEN! THE ONLY WAY IT WILL BE PUBLISHED IS THAT THE AUTHORS LOOK IN DETAIL AT ALL THE IMPRECISIONS AND BADLY WRITTEN CONTENT. ONLY IF THEY RE-WRITE ENTIRELY WITH AN ENGLISH-SPEAKING PERSON (WHOSE FIRST LANGUAGE IS ENGLISH OR IS VERY PROFFICIENT IN ENGLISH), THIS MANUSCRIPT WILL BE PUBLISHED. OTHERWISE, UNFORTUNATELY, IT CANNOT BE PUBLISHED! PLEASE PAY CLOSE ATTENTION TO ALL THE WRITING

Authors response: Thank you very much for you very important comment specially the language of the manuscript and recommendation to give our manuscript for language editor to English speaking person. We accept your comment and gave our manuscript for English speaking person (Elizabeth Korevaar). We acknowledge her support in our revised manuscript. She gave us extensive language edition and we corrected it in the revised version of the manuscript.

Reviewer's comment: The comment of the reviewer was given on the PDF part of the manuscript with detail comments.

Authors response: - We accept all comments and corrected it accordingly.

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2 Sep 2022

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3 Sep 2022

PLOS ONE

Point by point response for editors/reviewers’ comments

The manuscript title “Geographic variation and factors associated with under-five mortality in Ethiopia. A spatial and multilevel analysis of Ethiopian Mini Demographic and Health Surveys 2019”

Manuscript number: PONE-D-21-20589R2

Dear editor,

I would like to thank you for these constructive, building, and improvable comments on this manuscript that would improve the substance and content of the manuscript. We considered each of your recommendation of language edition. Further, the details of changes were shown by track changes in the supplementary document attached.

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20 Sep 2022

Geographic variation and factors associated with under-five mortality in Ethiopia. A spatial and multilevel analysis of Ethiopian Mini Demographic and Health Surveys 2019.

PONE-D-21-20589R3

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27 Sep 2022

PONE-D-21-20589R3

Geographic variation and factors associated with under-five mortality in Ethiopia.  A spatial and multilevel analysis of Ethiopian Mini Demographic and Health Survey 2019

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