Individual and community-level factors of treatment-seeking behaviour among caregivers with febrile children in Ethiopia: A multilevel analysis.

BACKGROUND: Early diagnosis and treatment of childhood fever are essential for controlling disease progression and death. However, the Treatment-seeking behaviour of caregivers is still a significant challenge in rural parts of the African region. This study aimed to assess individual and community-level factors associated with treatment-seeking behaviours among caregivers of febrile under-five age children in Ethiopia.
METHOD: The recent Ethiopian Demographic and Health Survey data (EDHS 2016) was used for the study. The survey collected information among 1,354 under-five children who had a fever within two weeks before the survey. The data were extracted, cleaned, and recoded using STATA version 14. Multilevel logistic regressions were used to determine the magnitude and associated factors of treatment-seeking behaviour among caregivers with febrile children in Ethiopia. Four models were built to estimate both fixed and random effects of individual and community-level factors between cluster variations on treatment-seeking behaviour. The Adjusted Odds Ratios with 95% Confidence Intervals (CI) of the best-fitted model were reported at p<0.05. RESULT: This study revealed that 491 (36.26%) caregivers seek treatment for their febrile children. Living in metropolitan and small peripheral regions, delivery at health institutions, being poorer, middle and richer wealth quintiles, having a child with diarrhoea, cough, short rapid breathing, and wasting were positively associated with treatment-seeking behaviour of caregivers.
CONCLUSION: The caregivers had poor treatment-seeking behaviour for their febrile children in Ethiopia. Health education programmers should emphasise the importance of seeking early treatment, taking action on childhood febrile illness signs.

Introduction

Treatment-seeking behaviour is the sequence of actions and integral part of a person’s, a family’s, or a community’s identity that patients and caregivers take to solve their problem [1-3]. Febrile illnesses are complicated to diagnose and manage clinically because of the large variety of fever causing infections with similar clinical manifestations [4]. In developing countries, the most common cause of referral is an acute febrile illness [5]. Fever is most prevalent in African countries, such as Ethiopia and Tanzania [6, 7]. Malaria and pneumonia are the leading causes of morbidity and mortality in under-five children in these countries [8, 9]. In the study conducted in the rural communities of Somalia, the majority of fevers (84.4%) were associated with other symptoms, including cough, running nose, and sore throat, and only 37.5% of fever cases were managed at a formal health care facility [10, 11].

Moreover, fever treatment within 48 hours with effective anti-malarial is used as the milestone parameter in national sample surveys to measure the success of malaria case management policies [10]. A study conducted in Zambia showed that seeking early and appropriate treatment was suboptimal [12]. In malaria-endemic areas, fever has been used as a proxy for malaria even though the cause could be different [13, 14]. World Health Organization (WHO) 2016 report showed that approximately 34% of households of caregivers in sub-Saharan Africa seek early treatment for their febrile children from health care professionals [15]. In many sub-Saharan Africa countries such as Malawi [16], Liberia [17], Tanzania [18], and Zambia [12], the magnitude of caregivers’ early treatment-seeking behaviour for febrile children was 67.3%, 98.5%, 56.8%, and 27.0%, respectively. Enhancing treatment-seeking behaviour regarding fever in Ethiopia is imperative [19-21]. In Ethiopia, healthcare-seeking behaviour is poor, and only 35% of under-five children with fever receive appropriate treatment [22]. Even though malaria remains a major cause of fever, its incidence has been steadily alleviated since 2003 [23, 24].

Bloodstream infection, including bacterial zoonosis, is one of the challenges for healthcare providers with similar clinical manifestations among hospitalised patients [25]. WHO has proposed guidelines on the Integrated Management of Childhood Illness regarding appropriate supportive care at the health facility level for those febrile children requiring antibiotic treatment due to the inadequacy of health care facilities and skilled health care workers. Febrile children are treated through the informal sector, which greatly impacts the treatment of many childhood illnesses and contributes to morbidity and mortality [26]. Different works of literature reported that seeking fever treatment is influenced by caregivers’ knowledge, educational status, age, and availability of health facilities [27-30]. In low-income countries, community-based agents are the first people to treat childhood fever [31-34]. However, there was a significant increase in treatment-seeking behaviour for febrile children at the community level after the introduction of health care providers advised in malaria control [35]. In Sub-Saharan Africa, diseases causing fever contribute a significant impact on health [4].

The study conducted in different African countries reported multiple predictors of treatment-seeking behaviour among caregivers. These predictors were expendable income, economic constraint, previous history of a febrile patient, inaccessibility to health services, and undiagnosed perceived cause of febrile symptoms always linked to malaria were major significant predictors of treatment-seeking behaviour among caregivers [36-39]. Although fever is identified as a clinical sign of patients at Emergency and ICU departments and is the reason for the rational use of antibiotics, three are no studies identifying the treatment-seeking behaviours of caregivers for febrile children in Ethiopia [40]. Lack of treatment-seeking behaviour for childhood febrile illness among caregivers of under-5 children is still a major concern [29, 31]. Individual related factors such as the child’s and caregiver’s related factors [41], media exposure [42], caregiver’s perceived distance to a health facility (no problem or problem), antenatal care (ANC) visits [43], the use of postnatal care, place of delivery [44], community development index [44, 45] were predictors of treatment-seeking behaviour reported in the literature. However, predictors of treatment-seeking behaviour among caregivers and the extent of febrile illness in Ethiopian contexts is not known. Therefore, the current study aimed to determine the factors associated with treatment-seeking behaviour among caregivers with febrile under-five children in Ethiopia. Identifying caregivers’ treatment-seeking behaviour will help to create awareness and to take prompt action.

Methods and materials

Study design, setting, and period

We have used the 2016 Ethiopian Demographic and Health Survey (DHS) dataset (EDHS 2016). In Ethiopia, there were nine regional states such as Tigray, Afar, Amhara, Oromia, Somali, Benishangul-Gumuz, Southern Nations Nationalities and People Region (SNNPR), Gambela and Harari, and two administrative cities (Addis Ababa and Dire-Dawa). Eighty-four percent (84%) of the population lives in rural areas. The EDHS has consisted of a sample of households obtained through a two-stage stratified sampling procedure. The survey used the Ethiopian Population and Housing Census carried out in 2016 by the Ethiopia Central Statistical Agency (CSA) as the sampling frame. In the first stage, the country was divided into 645 (202 in urban and 443 in rural areas) primary sampling units or Enumeration Areas (EAs) by using the probability proportional to the size allocation method. In the second stage, a household listing was obtained in all selected EAs as a sampling frame, and an equal probability systematic sampling technique was carried out to select 28 households per cluster using the household listing. All women aged 15–49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. The 2016 EDHS 84915 enumeration areas (EAS) served as a sampling frame, 645 clusters were selected in the first stage, and from those clusters, 202 were urban, and 443 were from rural areas.

A total of 16,650 households were surveyed in the second stage. The 2016 EDHS interviewed a total of 15,683 women between the ages of 15–49 years. The data for the present study were extracted as follows: First, women who gave birth in the last five years were identified. Next, caregivers/mothers who had a child with fever were identified in the two weeks preceding the survey period. It is customary to get more than one under-five child per household. However, for data quality, if more than one under-five children per household, data were collected from children with the last (recent) birth. Finally, of the 10,417 under-five children, 10,006 were alive children, and a total of 1,354 (weighted = 1495) mothers/caregivers who had under-five children with fever were included for the analysis. A total of 1,354 caregivers/mothers with febrile children under five were used to analyse this study. Caregivers were defined as mothers aged between 15–59 years with a child/child under age 5 years who responded to the survey [46]. Fever is an abnormally high body temperature, usually accompanied by shivering, headache, and restlessness. Fever indicates the presence of various illnesses such as malaria, pneumonia, an ear problem, the common cold, influenza, and other infections. Treatment of fever is a Children with fever for whom advice or treatment was sought. The Sample of this EDHS study was Children under age 5 with fever two weeks before the survey, and the detailed sampling procedure was presented in the full 2016 EDHS report [47].

Variables of the study

The outcome variable for this study was treatment-seeking behaviour (Yes/ No), defined as whether or not a caregiver sought advice or treatment from a health facility for a living child under five who had a fever at any time in the two weeks preceding the survey. The advice or treatment was sought from a governmental or private health facility by a health care professional [44, 48–50]. Socio-demographic and other health-related variables were included as independent variables. The socio-demographic variables were maternal age (year), child age (month), region, sex of household head, sex of the child, child’s twin status, marital status, maternal educational level, and maternal currently working status. Other health related independent variables were size of child at birth, mass media exposure, wealth index combined, had diarrhoea, had a cough, had short rapid breathing, antenatal care, postnatal care, stunting, wasting, had anaemia, place of delivery, parity, vaccination, vitamin A in last six months, community development index, distance to the health facility, place of residence, and covered by health insurance.

Regions were categorised into three categories; Amhara, Oromia, Tigray, and SNNP regions were categorised as a large central region; the three administrative cities: Harar, Addis Ababa, and Dire Dawa, were categorised as a metropolitan region and the others (Somali, Gambelia, Afar, and Benishangul Gumuz region) were categorised as a small peripheral region. The aggregate community level explanatory variable: the community development index was constructed by aggregating individual-level characteristics at the cluster level by using an improved/unimproved source of drinking water, improved /unimproved sanitation facility, presence of electricity (no/yes) categorised as low, moderate, and good. Wealth Index was assessed to measure the socioeconomic status of the households based on household assets (television, bicycle/car, size of agricultural land, a quantity of livestock), and dwelling characteristics (sources of drinking water, sanitation facilities, and materials used for constructing houses), and the scores were divided into five categories of wealth quintile (poorest, poorer, medium, richer, and richest). Under-five children whose height-for-age Z-score, weight-for-age Z-score, and weight for height Z-score are below minus two standard deviations (− 2 SD) from the reference population’s median are considered stunted underweight, and wasted, respectively. Percentage of children under age 5 with fever, diarrhoea, cough, and short rapid breathing at any time in the two weeks preceding the survey were included as independent variables. Child size at birth is the percent distribution of live births in the five years preceding the survey by mother’s estimate of baby’s size at birth (very small, smaller than average, average or larger, don’t know/missing) recoded into large, average, and small. The vaccination status of the child is confirmed by vaccination card or mother’s report. In those children, age 12–23 months and children age 24–35 months who received specific vaccines at any time before the survey according to vaccination card or mother’s report by appropriate age recoded into complete and incomplete vaccination [51].

Data processing and analysis

The variables of the study were extracted, cleaned, and recoded using STATA version 14. To accommodate for the complex sampling design employed in the survey. Weighted data analysis was employed. Data weights were computed using sampling weights readily provided in the dataset and post-stratification weights developed by the researchers based on the 2016 population size of the nine regions and two city administrations of the country [52]. Descriptive statistics were performed with weighted data to explain the background characteristics of the individuals and communities. Four models (the intercept only (null model), individual-level factors (model ii), community-level factors (model iii), individual and community-level factors (model iv)) were fitted in these two levels of logistic regression analysis. During the analysis, caregivers (Level 1) were nested within their communities (Level 2) to estimate fixed effects of the individual and community-level factors and random effects between-cluster variation on treatment-seeking behaviours. Model I was the intercept-only multilevel logistic regression model (null model), which only included the outcomes of "treatment-seeking behaviour" to assess community effects on the treatment-seeking behaviour of the caregivers. In model II, the outcome and individual-level variables were fitted, whereas, with Model III, the outcome and community-level variables were included. Model IV fitted both the individual- and community-level variables.

Explanatory variables with a p-value of < 0.2 in the bivariable multilevel logistic regression model were fitted into the multivariable multilevel logistic regression model. A measure of association was reported as Adjusted Odds Ratio (AOR) with 95% CIs by controlling the effect of other predictors. A p-value<0.05 was used to identify factors significantly associated with treatment-seeking behaviours. Measures of variation (random effects) were assessed using several indicators. Variation between clusters (EAs) were assessed by computing Intra-lass Correlation Coefficient (ICC), the median odds ratio (MOR), and the proportional change in variance (PCV). The null model was used as a reference to look at the relative contribution in explaining TSB. The ICC is the proportion of variance explained by the grouping structure in the population. Whereas, PCV measures the total variation attributed to individual and community level factors in the multilevel model compared to the null model [53]. The goodness-of-fit of each model was assessed using the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) was used for model comparison. A model with a lower AIC and BIC is preferred over a larger AIC and BIC model, which means a lower value representing a closer model fit, log-likelihood, and deviance; with lower deviance (Model IV) was the best-fitted model. These four models were compared using deviance (-2LLR), and the model with the lowest deviance was selected as the best-fitted model for the data. The multicollinearity effect has been checked; this was done by using the mean of variation inflation factor (VIF) and tolerance value. Less than ten mean VIF values indicate the absence of extreme collinearity problems among the regression model’s explanatory variables. None of the variables displayed multicollinearity problems (all VIF < 10, tolerance > 0.1).

Ethical consideration

This study is a secondary data analysis from the DHS data, so it does not require ethical approval. For conducting this study, online registration and request for measure DHS were conducted. The dataset was downloaded from DHS online archive (http://www.dhsprogram.com) after getting approval to access the data.

Results

Socio-demographic and health-related characteristics

Overall, this study revealed that 491 (36.26%) (95% CI: 33.74, 38, 86) caregivers sought treatment for their febrile children. This result shows that there are still a substantial number of febrile children under the age of five years that are not taken to health care facilities.

In this study, a total of 1354 febrile children were included from 10,006 alive children. Of the 1354 children, 534 (35.75%) have had diarrhoea in the past two weeks preceding the survey. The majority of caregivers, 898 (60.08%), had no education, and 1480 (98.98%) were married. Besides, the majority of the caregivers were not working 1,030 (68.93%) and not covered with health insurance 1,439 (96.25%). Regarding the age of the caregivers, 371(27.40%) were between 25–29 years of age, and the mean age was 28.90(SD = 6.52) years. Regarding the age of children, 393(26.31%) were between 12–23 months of age, and the mean age was 28.5(SD = 17.45) months (Table 1).

Table 1

Weighted socio-demographic and health-related characteristics of the study participants in Ethiopia, 2016(n = 1354; weighted sample = 1495).

Respondent’s characteristic	Categories	Weighted Sample
		Frequency	Percentage
Maternal age in years	15–19	43	2.85
	20–24	337	22.54
	25–29	432	28.91
	30–34	351	23.50
	35–39	215	14.35
	40–44	83	5.58
	45–49	34	2.27
Childs age in months	0–11	370	24.77
	12–23	394	26.31
	24–35	287	19.22
	36–47	228	15.26
	48–59	216	14.44
Region	Large central region	1,382	92.42
	Metropolitan region	43	2.89
	Small peripheral region	70	4.69
Sex of household head	Male	1284	85.91
	Female	211	14.09
	Total	1495	100
Sex of child	Male	768	51.40
	Female	727	48.60
Child is twin	Single birth	1448	96.88
	1st of multiple	24	1.60
	2nd of multiple	23	1.52
Marital status	Unmarried	15	1.02
	Married	1,480	98.98
Maternal educational level	No education	898	60.08
	Primary	481	32.20
	Secondary	79	5.24
	Higher	37	2.49
Maternal currently working	No	1,030	68.93
	Yes	465	31.07
size of child at birth	Large	472	31.58
	Average	545	36.44
	Small	478	31.98
Mass media exposure	No	915	61.20
	Yes	580	38.80
Wealth index combined	Poorest	313	20.94
	Poorer	317	21.19
	Middle	306	20.47
	Richer	322	21.53
	Richest	237	15.88
Had diarrhea	No	961	64.25
	Yes	534	35.75
Had to cough	No	443	29.60
	Yes	1,052	70.40
Had short rapid breathing	No	784	52.47
	Yes	711	47.53
Antenatal care	no visit	719	48.08
	1–3 visit	363	24.30
	4+	413	27.63
Postnatal care	No	1,382	92.44
	Yes	113	7.56
Underweight	Yes	1,084	72.54
	No	411	27.46
Stunting	Yes	988	66.08
	No	507	33.92
	Total	1495	100
Wasting	Yes	1,332	89.08
	No	163	10.92
Had anemia	Severe	41	2.73
	Moderate	419	28.02
	Mild	372	24.90
	Not anemic	663	44.36
Place of delivery	Home	1,007	67.35
	Institution	488	32.65
Parity	Prim parous	266	17.78
	Multi parous	618	41.31
	Grand multipara	611	40.91
Vaccination	Incomplete	1,199	80.18
	Complete	296	19.82
vitamin A in last 6 months	No	826	55.28
	Yes	669	44.72
Community development index	Low	288	19.27
	Moderate	682	45.61
	Good	525	35.12
distance to a health facility	big problem	795	53.15
	not a big problem	700	46.85
Place of residence	Urban	192	12.84
	Rural	1,303	87.16
covered by health insurance	No	1,439	96.25
	Yes	56	3.75

Random effect analysis and model comparison

As shown in Table 2, in the null model, the ICC indicated that about 12.2% of the total variability of treatment-seeking behaviour was due to differences between clusters/EA, with the remaining unexplained 87.8% was attributable to individual differences. Besides, the median odds ratio in the first model, which implies the caregivers within a cluster of having a higher risk for treatment-seeking behaviour had a 1.54 times higher chance of having treatment-seeking behaviour as compared with the caregiver within a cluster of having lower risk if caregivers were selected randomly from two different enumeration areas. Regarding PCV, about 54.0% of the variability in treatment-seeking behaviour was explained by the full model. Besides, Model IV was selected as the best-fitted model (lowest deviance = 1,553.86).

Table 2

Multilevel analysis of factors associated with treatment-seeking behaviour among caregivers with febrile children in Ethiopia, 2016.

Respondent’s characteristic	Categories	Null Model (I)	Model II AOR (95% AOR (95% CI) CI)	Model III AOR (95% AOR (95% CI) CI)	Model IV AOR (95% AOR (95% CI) CI)
Individual-level factors
Maternal age in years	15–19		1		1
	20–24		0.69(0.348, 1.401)		0.74(0.365, 1.503)
	25–29		0.716(0.344, 1.491)		0.76(0.364, 1.608)
	30–34		0.45(0.204, 0.981)*		0.48(0.217, 1.069)
	35–39		0.54(0.234, 1.231)		0.57(0.247, 1.327)
	40–44		0.55(0.211, 1.415)		0.61(0.232, 1.595)
	45–49		0.44(0.108, 1.751)		0.54(0.131, 2.213)
Maternal educational level	No education		1		1
	Primary		1.31(0.951, 1.799)		1.35(0.977, 1.853)
	Secondary		1.26(0.724, 2.203)		1.10(0.626, 1.937)
	Higher		2.64(1.204, 5.795)*		2.09(0.941, 4.653)
Children’s age in months	0–11		1		1
	12–23		0.83(0.564, 1.207)		0.80(0.549, 1.177)
	24–35		1.08(0.721, 1.635)		1.095(0.726, 1.649)
	36–47		0.99(0.631, 1.547)		0.941(0.600, 1.476)
	48–59		1.11(0.697, 1.779)		1.05(0.658, 1.691)
Child is twin	Single birth		1		1
	1st of multiple		0.58(0.165, 2.031)		0.63(0.183, 2.186)
	2nd of multiple		0.51(0.160, 1.643)		0.59(0.1836, 1.898)
Sex of child	Male		1		1
	Female		0.81(0.626, 1.044)		0.83(0.643, 1.075)
Had to cough	No				1
	Yes		1.43(1.047, 1.952)*		1.44(1.054, 1.977)*
Had short, rapid breathing	No		1		1
	Yes		1.54(1.145, 2.079)*		1.679(1.242,2.269)*
ANC	no visit		1		1
	1–3 visit		1.37(0.964, 1.945)		1.41(0.993, 2.009)
	4+		1.22(0.842, 1.769)		1.211(0.834, 1.757)
Wasting	No		1		1
	Yes		1.63(1.071, 2.469)*		1.55(1.022, 2.359)*
Stunting	No		1		1
	Yes		0.98(0.699, 1.361)		0.99(0.715, 1.396)
Underweight	No		1		1
	Yes		0.86(0.593, 1.235)		0.89(0.615, 1.285)
Maternal currently working	No		1		1
	Yes		1.12(0.842, 1.489)		1.08(0.810, 1.434)
size of child at birth	Large		1		1
	Average		1.01(0.744, 1.381)		1.03(0.752, 1.399)
	Small		0.89(0.638, 1.249)		0.91(0.649, 1.271)
Mass media exposure	No		1		1
	Yes		1.05(0.765, 1.428)		1.08(0.787, 1.471)
PNC	No		1		1
	Yes		1.05(0.676, 1.620)		1.05(0.679, 1.629)
Diarrhea	No		1		1
	Yes		1.55(1.183, 2.037)*		1.53(1.166, 2.009)*
Delivery place	Home		1		1
	Institution		1.63(1.169, 2.260)*		1.44(1.027, 2.015)*
Parity	Prim parous		1		1
	Multi parous		1.26(0.845, 1.878)		1.22(0.814, 1.818)
	Grand multipara		1.33(0.795, 2.231)		1.33(0.793, 2.246)
wealth index combined	Poorest		1		1
	Poorer		1.32(0.879, 1.969)		1.60(1.046, 2.453)*
	Middle		1.578(1.032, 2.414)*		1.89(1.207,2.971)*
	Richer		1.93(1.241, 2.991)*		2.37(1.49, 3.747)*
	Richest		2.88(1.781, 4.670)*		1.83(0.995, 3.379)
Vaccination	Incomplete		1		1
	Complete		1.22(0.857, 1.736)		1.23(0.863, 1.755)
vitamin a in last 6 months	No		1		1
	Yes		0.99(0.758, 1.296)		0.99(0.756, 1.295)
Community-level factors
Community development index	Low			1	1
	Moderate			0.87(0.571, 1.341)	0.96(0.612, 1.504)
	Good			1.24(0.792, 1.928)	1.29(0.811, 2.066)
Place of residence	Urban			1	1
	Rural			0.32(0.214, 0.488)*	0.61(0.336, 1.119)
covered by health insurance	No			1	1
	Yes			1.70(0.885, 3.279)	1.40(0.707, 2.788)
Region	Large central			1	1
	Metropolitan			1.49(0.962, 2.319)	2.04(1.296, 3.226)*
	Small peripheral			1.07(0.786, 1.443)	1.69(1.191, 2.406)*
Community-level variance		0.456(0.17)	0.273(0.15)	0.300(0.138)	0.213(0.142)
ICC		0.122	0.076	0.083	0.061
MOR		1.54	1.296	1.329	1.224
PCV (%)		Reference	40.1	34.0	54.0
Deviance		1759.78	1,582	1666.82	1,553.8599
Log-likelihood		-879.89	-791.00	-833.41	-776.92995
AIC		1763.776	1662.009	1682.823	1645.86
BIC		1774.197	1870.442	1724.51	1885.558

Note: AOR = Adjusted Odds Ratio; CI = Confidence Interval; 1.00 = Reference Group * = P<0.05; ICC = Intra-class Correlation Coefficient: MOR: Median Odds Ratio: PCV: Proportional Change in Variance.

Factors associated with treatment-seeking behaviour

In the final model, region, place of delivery, wealth index, having a child with diarrhoea, cough, short rapid breathing, and wasting were significantly associated with treatment-seeking behaviour of caregivers who had under-five febrile children (p<0.05). The odds of treatment-seeking behaviour were 2.04 (AOR = 2.04, 95% CI:1.296, 3.226) and 1.69 (AOR = 1.69, 95% CI: 1.191, 2.406) times more likely in the metropolitan region and small peripheral region than the large central region, respectively. Those caregivers who had wasted children were 1.55 (AOR = 1.55, 95%CI: 1.022, 2.359) times more likely to seek treatment than their counterparts. The odds of treatment-seeking behaviour was 1.44 (AOR = 1.44, 95%CI;1.054, 1.977) times higher in caregivers who had children with a history of cough than caregivers with under-five children that had no history of cough. Caregivers with poorer wealth index quantile had 1.60 (AOR = 1.60, 95%CI: 1.046, 2.453)) times more likely to seek treatment than caregivers with the poorest wealth index quantile by considering the other predictor variable constant. As compared to the poorest wealth index quantile, the odds of having treatment-seeking behaviour was 1.60 (AOR = 1.60, 95%CI:1.046, 2.453),1.89 (AOR = 1.89, 95%CI:1.207, 2.971), and 2.37 (AOR = 2.37, 95%CI: 1.49, 3.747) times higher for poorer, middle, and richer wealth index quintiles, respectively. Regarding short rapid breathing in the last two weeks, the odds of treatment-seeking behaviour among caregivers with children with a history of short rapid breathing was 1.68 (AOR = 1.68, 95%CI:1.242,2.269) times higher than their counterparts. Caregivers having under-five children with a history of diarrhoea had 1.53 (AOR = 1.53, 95% CI:1.166, 2.009) times more likely to seek treatment than caregivers who had under-five children with no history of diarrhoea. Moreover, the odds of treatment-seeking behaviour were 1.44 (AOR = 1.44, 95% CI:1.027, 2.015) times more likely for caregivers delivered at the institution than caregivers delivered at home (Table 2).

Discussion

This study reported that 36.26% (95% CI: 33.74, 38.86) of caregivers sought treatment for their febrile children. This finding was lower than previous studies conducted in Malawi [16], Liberia [17], and Tanzania [18] in that 67.3%, 98.5%, and 56.8%, respectively, of the caregivers, had treatment-seeking behaviour for their febrile child. The possible reason for the difference might be due to financial constraints, caregivers or mothers in Ethiopia visit traditional healers before they go to health care facilities, perception of caregivers regarding the illness, most of the caregivers/mothers in this study were rural residence, most of them also have no educational background, and, in Liberia, the information gathered about treatment-seeking behaviour was after a social and behaviour change campaign, and this might expose caregivers to malaria-related messages.

The magnitude of treatment-seeking behaviour was also lower than the study conducted in an urban area of Eastern Sudan, reported 40.4% and 91.1% of housewives seeking advice from health workers for mild fever and severe fever, respectively [54]. The possible justification might be due to the sample size, measurement tool, and approaches of the outcome variable.

This study used nationally representative data with 1,354 samples collected from caregivers who had under-five children with fibril illness. Whereas in eastern Sudan, in a single centred (Kassalacity) with 350 housewives only were interviewed, and the outcome variable was categorised as available treatment options (ATP) concerning the intensity of fever (low and high). Therefore treatment-seeking behaviour varied from country to country due to evaluation of the symptoms, perceived treatment effects, the initial reaction of caretakers, and perceived intensity of fever, regardless of the illness [54]. A study conducted In Kenya reported that caregivers were started self-treatment at home and waited for some time to observe the progress of the illness [55]. This was likely to lead to a delay in the treatment of malaria and another unfortunate consequence.

Children who were reported to have had diarrhoea in the last two weeks were 1.53 times more likely to be taken for fever treatment than those who had no diarrhoea. This study was inconsistent with a study conducted in Malawi in which febrile children who had diarrhoea in the last two weeks were less likely to be brought for treatment than those who had no diarrhoea [16]. Compared to the poorest wealth index quantile, the odds of having treatment-seeking behaviour were 1.60, 1.89, and 2.37 times higher for the poorer, middle, and richer wealth index quantiles, respectively. This study was inconsistent with evidence from other sub-Saharan African countries, which reported that household wealth was not significantly related to caregivers delaying seeking care for children with fever [44, 56]. The possible justification might be that the diagnosis and treatment of fever in another country like Liberia are generally free of cost [57, 58]. Whereas in Ethiopia, the diagnosis and management are not free of charge, and due to the low economic status of the country, most caregivers are not affordable to pay for the treatment cost of febrile illness. There was no significant association between treatment-seeking behaviour and the caregiver’s age and level of education, unlike a study done in Grand Gedeh County, Liberian, and Tanzania [18, 56, 59]. In this study, media exposure (radio, television, and newspaper) was not significantly associated with treatment-seeking behavior among care givers with febrile children. This study was consistent with a study conducted in Malawi [60]. However, it was inconsistent with a study conducted in Tanzania [18].

Strength and limitations of the study

This study used nationally representative samples. Therefore, the results could be generalised to all Ethiopian caregivers/mothers. Fever was assessed by history that can lead to recall bias, but the 2-week recall period could help to reduce the bias. However, it is a cross-sectional study, did not signify causal attribution; the use of clusters or administratively defined boundaries could yield information bias for those caregivers from unfitted administrative communities; social desirability bias since self-reports on whether treatment was sought are prone to caregivers may attempt to present that they take good care of their children by, among others, seeking treatment whenever their children have a fever; and the study has not identified the influence of caregivers’ knowledge, perceptions, and attitudes on different childhood illnesses that provoke fever such as malaria.

Conclusion

This study revealed that the treatment-seeking behaviour of mothers’/care givers’ in Ethiopia was low, even if a common childhood illness remains high. Therefore, in Ethiopia, improving treatment-seeking behaviour needs more attention and emphasis. Living in metropolitan and small peripheral regions, delivery at health institutions, being poorer, middle and richer wealth quintiles, having a child with diarrhoea, cough, short rapid breathing, and wasting were positively associated with treatment-seeking behaviour of caregivers. Therefore, both individual and community variables may prove fundamental to effect improved behaviours.

(SAV)

Click here for additional data file.

(DTA)

Click here for additional data file.

(XLS)

Click here for additional data file.

2 Jun 2021

PONE-D-21-03591

Individual and community-level factors associated with treatment-seeking behavior among caregivers with febrile children in Ethiopia: A Multilevel analysis

PLOS ONE

Dear Dr. liyew,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The paper needs substantial improvements in all parts in order to be readable. Please address reviewers feedback and revised the paper accordingly.

Please submit your revised manuscript by Jul 11 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at  https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Enamul Kabir

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please amend either the title on the online submission form (via Edit Submission) or the title in the manuscript so that they are identical.

3. Thank you for submitting the above manuscript to PLOS ONE. During our internal evaluation of the manuscript, we found significant text overlap between your submission and the following previously published works, some of which you are an author.

http://eprints.gla.ac.uk/228008/1/228008.pdf

https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0202240

We would like to make you aware that copying extracts from previous publications, especially outside the methods section, word-for-word is unacceptable. In addition, the reproduction of text from published reports has implications for the copyright that may apply to the publications.

Please revise the manuscript to rephrase the duplicated text, cite your sources, and provide details as to how the current manuscript advances on previous work. Please note that further consideration is dependent on the submission of a manuscript that addresses these concerns about the overlap in text with published work.

We will carefully review your manuscript upon resubmission, so please ensure that your revision is thorough.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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: Partly

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. 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

Reviewer #2: Yes

**********

4. 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

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: introduction

line 64 : authors should look at the sentence

line 65 : fever is a sign of illness and so what

lines 66 and 67 : is not in anyway relevant at this point of the introduction

lines 67 to 70: starting from diagnosing ......and ending with effects should be deleted

lines 72 and 73: authors should indicate if which people (household, individual or community) seek either early or late treatment

lines 74 to 76: Authors should at least make a case for SSA or Ethiopia as well

Discussion

lines 270 and 271 : Authors can not justify the statement, therefore, it should be rephrased or deleted

Reviewer #2: This is an important study looking at a significant issue in an understudied context. The authors employ appropriate methods for measuring associations between a variety of factors and treatment-seeking for childhood fever. With some major revisions particularly around the framing of the study (introduction), more information about the variables, sample construction, measurement and analysis (methods), and contextualized results (discussion) this paper can make a contribution to the literature around access and healthcare seeking behavior.

Comments by section

1) Abstract: cut off – probably due to word count issues. Would like to see a little more summary and contextualization instead of lists of results/AORs – especially because certain terms/concepts haven’t been defined yet when someone is reading abstract so can’t assume reader will be able to understand meaning of results at this point.

2) Introduction:

a. First paragraph: Awkward language especially in first sentence. What does the inclusion of evidence from Europe and US add to the study? US numbers not clear 4.4 -7.5% and 30%? Could be because of missing information/typos but it seems like Ethiopia has the highest treatment seeking rates (87.8%) for children of the countries listed so the following sentence about “enhancing treatment-seeking” is less compelling for Ethiopia (although obviously still important for the remaining 12.2% of cases). This could be made clearer and more compelling.

b. Overall flow of the introduction could be improved. It also seems to fall short of setting up what the study is really about. The authors provide compelling evidence that fever is linked to Malaria and that early treatment is important for morbidity and mortality, but the introduction is lacking logical flow and clear delineation between content in paragraphs. I would suggest that it be reorganized to set up study, not just in context of rates of fever and why fever matters, but more focus on treatment -seeking behavior.

i. For example, this section should introduce the predictors you seek to measure (generally) and help the reader understand why you are measuring certain individual and community level factors. What are the hypothesized relationships? Is there a conceptual framework or theoretical model that these are based on?

3) Methods and Materials:

a. Section 2.1 should state how child information was collected (e.g. by caregiver report). This comes up again in section 3.2 where it says “of the 10,006 alive children who responded.” I’m assuming that the babies and children are not the ones responding, but this should be very explicit. The total children (10,006) is not mentioned at all in this section. It should be clear where the subsample of 1,354 is drawn from. Also, it seems like caregivers who are not the mother are excluded (grandmothers, aunts, sisters etc.) but it should be explicit that the respondents are the mother to the child for whom the data is reported (not just the caregiver who is a mother).

i. What is the distribution of the selected subsample (1,354 children with fever)? We know about EDHS, but does this group have the same characteristics or are they concentrated in certain regional states etc.? This comes up later in discussion section – is this subsample representative? Perhaps as an appendix, it would be helpful to have a comparison of these children and caregiver characteristics compared to the whole sample.

b. Section 2.2 lists “private sector” “market” as a place to seek care. What is meant by this location?

c. Section 2.2 – would suggest adding another section on analysis and separating from the variables/measures

d. Section 2.2: The Independent variables listed do not align with what is included in the tables (9+ variables in tables not included in this section). Need more information on the variables. Would like to understand more about why certain predictors were included. What are the hypothesized relationships? Do these come from the literature? Are they context specific or more globally applicable factors? How were specific indices constructed (not just what variables were included in them) and are these based on particular measures from a source (WHO? UNICEF? USAID? Article?) Not all of this needs to go in the methods section, some could be in the introduction, but overall, more information is needed about what is being measuring and why.

e. Section 2.2: need to include information on the clusters (“communities”) how are they constructed. Why are they used instead of other levels?

f. How were the levels determined? Fever incidents are nested within children who are nested within caregivers, who are nested in families, who are nested in households, who are nested in communities, cities, states, regions, rural /urban etc. How were certain levels either eliminated (e.g. by selecting only 1 fever incident per child, only 1 child per caregiver, only 1 caregiver per household?) or otherwise accounted for? Of particular importance here, given the variables in your model, is children within caregiver and caregivers within a household.

g. Why did you use a cutoff of <=.26 for variable selection? This goes along with more information about the specific variables and why they were included, what they tell us about the research question.

h. How are missing data handled?

4) Results

a. 3.1 Background Characteristics: Major typo in first sentence that has serious implications if not fixed – “1006 alive children “Further reading showed this should be “10,006” . Another typo in that it references “unmarried as 1,479 (98.98%)” per table this is “married.” Also a minor point on language – “majority” typically means >50% so wouldn’t be used as it is in this paragraph to refer to 27.4% and 26.31%.

b. Table 1: Would be good to include totals so a reader can quickly see the sample size/total frequencies (and assess if they are consistent). As stated before – more information on the variables is needed. There are several variables here that were never mentioned before appearing in the table. While some are more obvious (sex of child), others are somewhat unclear (sex of household?, had diarrhea, had to cough, had anemia) – how were these operationalized, who is being measured, what is the time frame? Why do they matter? Obviously, not all of this information goes in the table, but some could be included here, whereas other info can be in the introduction and methods sections.

c. 3.3 Random effect…: More information about the clusters needs to be included in the methods section before reporting on the results by clusters. It is not clear how the clusters are constructed – what is the definition of “community.” How many communities are there? Is there sufficient power for the number of clusters and units within each cluster? The last sentence of this paragraph states that the Model IV was selected because it had the lowest deviance – but the number reported from Table 2 is different than the actual number listed in Table 2 for that model. Which is it?

d. 3.4 Factors associated…: Why state that variables had a statistically significant association at the level of <.20 when in previous sections (methods) you said you were using a cutoff of <0.05 (which is standard) ? In multiple sections/tables the coughing variable is not clearly explained. The text sometimes states that the children were coughing, sometimes the caregiver, sometimes the caregivers “had to cough febrile children.” This should be clear and I believe in the DHS the variable is measuring whether the children had a cough. Same thing applies to diarrhea and rapid breathing. It should be very clear who had the diarrhea and when (same time as fever?) It is assumed this is the child and simultaneous to the fever, but the text/tables are missing important descriptions or stating it in confusing/inconsistent ways (e.g. line 257 “among caregivers that were having short, rapid breathing was 1.68 times…” line 258 “mothers having diarrhea…” line 259 “caregivers who have no diarrhea”)

e. Table 2: needs proper formatting such as consistent column width.

5) Discussion/limitations/conclusion:

a. The Malawi numbers listed are inconsistent with what is in the introduction (49.9% vs. 67.3%) assuming these studies are measuring different things but this should be clear.

b. Discussion of sample size seems to be more about the sample construction, not the size? Unless there were issues with too few observations for the multilevel approach. This should be specifically addressed in methods and limitations section. (size, distribution, representativeness).

c. Would like to see more discussion about the specific predictors not just how they compare to prior studies, but also what they mean in context. Did the hypothesized relationships/effects play out? How do the different predictors relate to eachother (or not).

d. It is not clear that this can be generalized to all Ethiopian caregivers/mothers (also those are different groups) because we don’t know if this small subsample drawn from a representative sample is representative. Are mothers (or caregivers) of children under 5 who had a fever in the last 2 weeks prior to the survey representative of all mothers? Would like to see more in the limitations sections about any potential issues in the methodological and conceptual approaches.

e. Conclusion the mention of diarrheal disease derail treatment-seeking behavior is confusing and doesn’t seem to be in line with results.

f. Expected to see these sections linked back to the introduction more in terms of the focus on malaria, implications for the children etc.

g. The contribution of this study (which I believe in) isn’t coming through clearly . Being very precise about what was learned, what it means, and how these results can be used/interpreted is key.

6) General: Should be copy-edited for typos, awkward language, grammar issues.

**********

6. 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

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

20 Jul 2021

Authors’ Response for Reviewers’ Comments

Manuscript ID: PONE-D-21-03591

Title: Individual and community-level factors associated with treatment-seeking behavior among caregivers with febrile children in Ethiopia: A Multilevel analysis

Dear editor(s) and reviewers

First for all the authors would like to thank the editor(s) and reviewers for your precious time, thoughtful comments and constructive suggestions, which help to improve the quality of this manuscript. We have responded to each critique/ comment and believe that the manuscript is much improved with the changes we made as suggested by the editor and reviewers. The corresponding changes and refinements made in the revised manuscript are summarized in our response below.

Response=> Authors response for editor and/ reviewers comments

Reviewer #1

Evaluation (review comments for the authors)

Introduction

�  line 64 : authors should look at the sentence

Response: We thank the reviewer for raising this important point. We rewrote the introduction for greater focus. This section has been edited for conciseness by presenting the crucial points of what is known and not known, and why we carried out the study.

Q: line 65: fever is a sign of illness and so what

Response: After your suggestions, we have revised again the document. We have also noted repetitive sentences, contextual spelling and sentence structure problems. Then we have re-arranged and re-edited the vague sentences. We have provided the document for professional language copy editor expert to advanced language edition. Hence clarity problems are resolved and highlighted by red color in the revised manuscript.

Q: lines 66 and 67: is not in any way relevant at this point of the introduction

Response: This comment is crucial for this manuscript which was our gap. Now we have reviewed works of literature regarding the guidelines of physical restraint use in different countries including Ethiopia on the targeted and other population. Information regarding the issue and the rationale is provided in the introduction section of the revised manuscript and highlighted by red color

Q: lines 67 to 70: starting from diagnosing ......and ending with effects should be deleted

Response: thank you for your suggestion. The suggested correction has been made and highlighted in track change.

Q: lines 72 and 73: authors should indicate if which people (household, individual or community) seek either early or late treatment

Response: We appreciate the positive feedback from the reviewer .It is corrected and highlighted by track change in the main revised manuscript.

Q: lines 74 to 76: Authors should at least make a case for SSA or Ethiopia as well

Response: More detail information has been provided on these aspects in the introduction section.

Discussion

Q: lines 270 and 271: Authors cannot justify the statement; therefore, it should be rephrased or deleted

Response: We thank the reviewer for the suggestions. we have revised again the document. We have also noted repetitive sentences, unnecessary capitals and smalls, contextual spelling and sentence structure problems. Then we have re-arranged and re-edited the vague sentences. We have provided the document for professional language copy editor/proofreader expert to advanced language edition. Hence clarity problems are resolved and highlighted by red color in the revised manuscript.

Reviewer #2

Evaluation (review comments for the authors)

This is an important study looking at a significant issue in an understudied context. The authors employ appropriate methods for measuring associations between a variety of factors and treatment-seeking for childhood fever. With some major revisions particularly around the framing of the study (introduction), more information about the variables, sample construction, measurement and analysis (methods), and contextualized results (discussion) this paper can make a contribution to the literature around access and healthcare seeking behavior.

Response: We appreciate the positive feedback from the reviewer.

Q:1) Abstract: cut off – probably due to word count issues. Would like to see a little more summary and contextualization instead of lists of results/AORs – especially because certain terms/concepts haven’t been defined yet when someone is reading abstract so can’t assume reader will be able to understand meaning of results at this point.

Response: thank you for your suggestion. The suggested correction has been made and highlighted in track change.

Q:2)Introduction:

a. First paragraph: Awkward language especially in first sentence. What does the inclusion of evidence from Europe and US add to the study? US numbers not clear 4.4 -7.5% and 30%? Could be because of missing information/typos but it seems like Ethiopia has the highest treatment seeking rates (87.8%) for children of the countries listed so the following sentence about “enhancing treatment-seeking” is less compelling for Ethiopia (although obviously still important for the remaining 12.2% of cases). This could be made clearer and more compelling.

Response: We thank the reviewer for raising this important point. We rewrote the introduction for greater focus. This section has been edited for conciseness by presenting the crucial points of what is known and not known, and why we carried out the study.

Q:b. Overall flow of the introduction could be improved. It also seems to fall short of setting up what the study is really about. The authors provide compelling evidence that fever is linked to Malaria and that early treatment is important for morbidity and mortality, but the introduction is lacking logical flow and clear delineation between content in paragraphs. I would suggest that it be reorganized to set up study, not just in context of rates of fever and why fever matters, but more focus on treatment -seeking behavior.

i. For example, this section should introduce the predictors you seek to measure (generally) and help the reader understand why you are measuring certain individual and community level factors. What are the hypothesized relationships? Is there a conceptual framework or theoretical model that these are based on?

Response: Thank you for your view. This comment is crucial for this manuscript which was our gap. Now we have reviewed works of literature regarding predictors of treatment seeking behaviors in different countries including Ethiopia on the targeted population. Information regarding the issue and the rationale is provided in the introduction section of the revised manuscript and highlighted by track change.

Q:3) Methods and Materials:

a. Section 2.1 should state how child information was collected (e.g. by caregiver report). This comes up again in section 3.2 where it says “of the 10,006 alive children who responded.” I’m assuming that the babies and children are not the ones responding, but this should be very explicit. The total children (10,006) are not mentioned at all in this section. It should be clear where the subsample of 1,354 is drawn from. Also, it seems like caregivers who are not the mother are excluded (grandmothers, aunts, sisters etc.) but it should be explicit that the respondents are the mother to the child for whom the data is reported (not just the caregiver who is a mother).

Response: Thank you for your view. The 2016 EDHS 84915 enumeration areas (EAS) served as a sampling frame, 645 clusters selected in the first stage, and from those clusters 202 were urban and 443 from rural areas. A total of 16,650 households were surveyed in the second stage. The 2016 EDHS interviewed a total 15,683 women between ages of 15–49 year. The data for the present study were extracted as follows: First, women who gave births in the last 5 years were identified. Next, caregivers/mothers who had child with fever in the 2 weeks preceding the survey period were identified. It is customary to get more than one under-five children per a household. However, for data quality, if there were more than one under-five children per household, data were collected from children with the last (recent) birth. Finally, 10,006 were alive children and a total of 1,354 (weighted=1495) mothers/caregivers who had under-five children with fever were included for the analysis. Caregivers were defined as a mother aged between 15-59 years with a child/child under age 5 years, who had responded to the survey. The caregivers of under-five children either mothers or others who are responsible to raise the child and give care was the one who provided the maternal and child related information. Therefore, our study participants were those caregivers who had under five children with febrile illness to assess their health seeking behavior. Therefore, a total of 1354 under five children had history of fever within two weeks. Overall, the study units were the caregivers of under five children with febrile illness and then we went to assess the proportion of mothers who seek health care and the potential individual and community level factors associated with the health seeking behavior of the care givers.

Q:i. What is the distribution of the selected subsample (1,354 children with fever)? We know about EDHS, but does this group have the same characteristics or are they concentrated in certain regional states etc.? This comes up later in discussion section – is this subsample representative? Perhaps as an appendix, it would be helpful to have a comparison of these children and caregiver characteristics compared to the whole sample.

Response: Thank you the reviewer for the comments. As you know some of the regions were oversampled and some of the regions were under sampled, and in addition because of the non-response rate, the EDHS data were weighted for design and non-response to make it nationally representative and to draw valid conclusion. Therefore, for this study we used the weighted data, which was weighted for weighting variable (v005), strata (v021) and primary sampling unit (v023). Besides, considering the hierarchical nature of EDHS data and the child and the caregivers within the cluster/EAs were nested, shares similar characteristics, it could violate the standard logistic regression model assumptions such as independence of observation and homogeneity of variance assumptions. Therefore, we used a multilevel binary logistic regression analysis to get reliable standard error to draw valid conclusions. Due to the non-proportional allocation of the sample across the regions, sampling weights were used to ensure the representativeness of the finding. A sample is a group of people who have been selected for a survey. In the EDHS, the sample is designed to represent the national population age 15-49. However, doing so requires a minimum sample size per area. For the 2016 EDHS, the survey sample is representative at the national and regional levels, and for urban and rural areas. The data were weighted using sampling weight during any statistical analysis to adjust for unequal probability of selection due to the sampling design used in DHS data. Hence, the representativeness of the survey results was ensured.

Q:b. Section 2.2 lists “private sector” “market” as a place to seek care. What is meant by this location?

Response: those locations are a place where health care services delivered and pharmaceutical supplies delivered in the Ethiopian context. Generally, they are health care facilities either governmental or private.

Q:c. Section 2.2 – would suggest adding another section on analysis and separating from the variables/measures

Response: With great thanks the suggested correction has been made and highlighted in track change.

Q: d. Section 2.2: The Independent variables listed do not align with what is included in the tables (9+ variables in tables not included in this section). Need more information on the variables. Would like to understand more about why certain predictors were included. What are the hypothesized relationships? Do these come from the literature? Are they context specific or more globally applicable factors? How specific indices were constructed (not just what variables were included in them) and are these based on particular measures from a source (WHO? UNICEF? USAID? Article?) Not all of this needs to go in the methods section, some could be in the introduction, but overall, more information is needed about what is being measuring and why.

Response: thank you for your suggestion. The suggested correction has been made and highlighted in track change.

Q:e. Section 2.2: need to include information on the clusters (“communities”) how are they constructed. Why are they used instead of other levels?

Response: Thank you for the comments. For this data, we fitted a multilevel binary logistic regression analysis by considering factors at individual and cluster/community levels. We considered cluster/EAs as a random variable because at the beginning we consider EA/cluster and region as a random variable and fitted three-level binary logistic regression analysis but the regional level variance was not significant. Besides, to have stable estimate in multilevel binary regression analysis we have to have at least 50 clusters but as you know we have only 11 regions. That is why we fitted a two level multilevel logistic regression analysis considering EA as a random variable.

.How were the levels determined? Fever incidents are nested within children who are nested within caregivers, who are nested in families, who are nested in households, who are nested in communities, cities, states, regions, rural /urban etc. How were certain levels either eliminated (e.g. by selecting only 1 fever incident per child, only 1 child per caregiver, only 1 caregiver per household?) or otherwise accounted for? Of particular importance here, given the variables in your model, is children within caregiver and caregivers within a household.

Response: Thank you reviewer for the comments. Here, in the EDHS 2016 one reproductive age women/caregivers per household were selected and the child related characteristics were extracted about the last child/most recent birth. Therefore, we have one child per household as we have used the KR-file for our study and we do not expect that child were nested within the household rather we expected child was nested within cluster/EA and EA was nested within region. Considering this, we have fitted three-level multilevel binary logistic regression analysis model using STATA command “melogit seeking_for_fever || v001 || region:,” but unfortunately the regional level variance was not significant. Therefore, we fitted two level multilevel logistic regression analysis using individual level and cluster level explanatory variables. THE EDHS 2016 sample were taken by two stage stratified sampling in the first stage enumeration areas that were prepared by central statistical agency were selected in all regions after stratification was done into urban and rural residency. Then after in the second stage households were selected by systematic random sampling technique, and only one caregiver were taken in the household, and data were collected from children with the last (recent) birth if there is more than one under-five children per caregiver.

Response:

Q:g. Why did you use a cutoff of <=.26 for variable selection? This goes along with more information about the specific variables and why they were included, what they tell us about the research question.

Response: sorry for the typological error that we have made. We use a p-value of 0.2 as a cut-off point in bi-variable analysis as a rule of thumb to control cofounder variables.

Q:h. How are missing data handled?

Response: Thank you for the comments. We excluded study participants who had missing on the outcome variables but concerning the independent variables we included variables which have no missing and by default as the STATA software is robust it excluded the participants which have missing as it can do complete case analysis. As much as possible we used predictors which did not have missing based on the DHS recode statistics guideline. Missed data in the outcome variables were dropped/excluded, while missing data in the independent variables were managed according to DHS statistics guide (guide to DHS statistics DHS-7 version 2).

Q:4) Results

Q:a. 3.1 Background Characteristics: Major typo in first sentence that has serious implications if not fixed – “1006 alive children “Further reading showed this should be “10,006” . Another typo in that it references “unmarried as 1,479 (98.98%)” per table this is “married.” Also a minor point on language – “majority” typically means >50% so wouldn’t be used as it is in this paragraph to refer to 27.4% and 26.31%.

Response: thank you for your view. The suggested correction has been made and highlighted in track change. .

Q:b. Table 1: Would be good to include totals so a reader can quickly see the sample size/total frequencies (and assess if they are consistent). As stated before – more information on the variables is needed. There are several variables here that were never mentioned before appearing in the table. While some are more obvious (sex of child), others are somewhat unclear (sex of household?, had diarrhea, had to cough, had anemia) – how were these operational zed, who is being measured, what is the time frame? Why do they matter? Obviously, not all of this information goes in the table, but some could be included here, whereas other info can be in the introduction and methods sections.

Response: Thank you very much for your crucial comments for our manuscript. The suggested correction has been made. We mean to say that sex of household head. The operational definition, time frame and all others were found in EDHS 2O16 report; therefore, we cited it in the corrected documented.

Q: c. 3.3 Random effect…: More information about the clusters needs to be included in the methods section before reporting on the results by clusters. It is not clear how the clusters are constructed – what is the definition of “community.” How many communities are there? Is there sufficient power for the number of clusters and units within each cluster? The last sentence of this paragraph states that the Model IV was selected because it had the lowest deviance – but the number reported from Table 2 is different than the actual number listed in Table 2 for that model. Which is it?

Response: sorry for the editing error, we wrote null model result on the place of variable category, therefore, we omit it. All the alignment of table lines and other editing issues were resolved in the revised manuscript.

d. 3.4 Factors associated…: Why state that variables had a statistically significant association at the level of <.20 when in previous sections (methods) you said you were using a cutoff of <0.05 (which is standard) ? In multiple sections/tables the coughing variable is not clearly explained. The text sometimes states that the children were coughing, sometimes the caregiver, sometimes the caregivers “had to cough febrile children.” This should be clear and I believe in the DHS the variable is measuring whether the children had a cough. Same thing applies to diarrhea and rapid breathing. It should be very clear who had the diarrhea and when (same time as fever?) It is assumed this is the child and simultaneous to the fever, but the text/tables are missing important descriptions or stating it in confusing/inconsistent ways (e.g. line 257 “among caregivers that were having short, rapid breathing was 1.68 times…” line 258 “mothers having diarrhea…” line 259 “caregivers who have no diarrhea”)

Response: We very much appreciate this helpful comment. We are grateful for this comment as it points to an important point of view. This comment is crucial for this manuscript which was our gap. all type error, sentence structure, factor interpretation, language usage were resolved and copyedited in the revised document. We used p value less than 0.2 in the bi-variable analysis for selecting candidate variables for multivariable analysis, while p value 0.05 was used to declare the statistical significance in the final model that is multivariable multilevel binary logistic regression.

e. Table 2: needs proper formatting such as consistent column width.

Response: The suggested correction has been made.

5)Discussion/limitations/conclusion

a. The Malawi numbers listed are inconsistent with what is in the introduction (49.9% vs. 67.3%) assuming these studies are measuring different things but this should be clear.

Response: The suggested correction has been made and highlighted in the revised document.

b. Discussion of sample size seems to be more about the sample construction, not the size? Unless there were issues with too few observations for the multilevel approach. This should be specifically addressed in methods and limitations section.(size, distribution, representativeness).

Response:

c. Would like to see more discussion about the specific predictors not just how they compare to prior studies, but also what they mean in context. Did the hypothesized relationships/effects play out? How do the different predictors relate to each other (or not).

Response:

d. It is not clear that this can be generalized to all Ethiopian caregivers/mothers (also those are different groups) because we don’t know if this small subsample drawn from a representative sample is representative. Are mothers (or caregivers) of children under 5 who had a fever in the last 2 weeks prior to the survey representative of all mothers? Would like to see more in the limitations sections about any potential issues in the methodological and conceptual approaches.

Response: this study uses nationwide representative samples. Therefore, the findings can be generalized to the country/Ethiopian caregivers. As we mentioned the earlier comments, representativeness or generalizability issue was ensured by weighting the data throughout the analysis. Even if the history of fever were traced in the preceding 2 weeks of the caregivers interview, the data were collected for 5 years preceding 2016 EDHS.

e. Conclusion the mention of diarrheal disease derail treatment-seeking behavior is confusing and doesn’t seem to be in line with results.f. Expected to see these sections linked back to the introduction more in terms of the focus on malaria, implications for the children etc.

Response: : The suggested correction has been made

g. The contribution of this study (which I believe in) isn’t coming through clearly . Being very precise about what was learned, what it means, and how these results can be used/interpreted is key.

Response: : The suggested correction has been made

6) General: Should be copy-edited for typos, awkward language, grammar issues.

Response: We have consumed more time and energy for re-edition of the vague sentences. Based on this, we have rewritten the whole document in a more understandable manner to resolve language problems. Furthermore, we have modified, added, and changed a lot of things start from the title up to references based on other reviewers in addition to you. We are lucky, since the manuscript is assigned for three peer reviewers, and all of the three reviewers comments and questions are different which helps us to learn a lot and modify the whole document.

Submitted filename: 3rd revisedTSB author response.docx

Click here for additional data file.

21 Oct 2021

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'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Enamul Kabir

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

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

Reviewer #2: (No Response)

**********

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

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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

Reviewer #2: 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

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have addressed many of the reviewer comments and the revised manuscript is greatly improved. The introduction does a better job at setting up the paper, the methods section has some additional information, and the results are more clearly presented and contextualized in the discussion. However, there are still some concerns with the paper and several comments from the prior round of review that were not addressed. Comments below are organized by section.

Methods:

1. Several of the prior comments about variables and measures were not addressed. A new section was added and some additional information was provided, but the questions/comments about variable construction, variables used in analysis not mentioned anywhere else until showing in tables, construction of indices etc. were not addressed. Each of the subitems mentioned below was brought up in the prior comments.

a. Additional information about the index variables is needed in the manuscript (see prior comments). Did the community development index and the household wealth index use the same WASH measures? Were all items weighted equally? How exactly were the indices constructed? How were cutoffs and quintiles decided upon? Are there references for these indices? Community level cluster variable based on the WASH and electricity (have other studies used this?)

b. Some issues remain with inconsistencies between what variables are listed in different sections of the paper. For example, the first sentence on independent variables says “size of child at birth” is the measure, but then later in the same paragraph, the authors list under five height for age Z score, weight for age Z score and weight for height Z-score as the measures.

c. No information is given on several measures or why they may be important to include (some of this could go in intro if more appropriate) – examples include but are not limited to mass media exposure, vaccination, place of residence (is this the same as region? ), health insurance (is this yes/no? are there different types of insurance? Do you caregivers need insurance for sick child visits?), How is distance measured (km?, time to get there, ? Does it take main modes of transportation for the study population into account) – Some information can be ascertained from the tables, but the importance of these measures to the study and why they are included (and operationalized in certain ways) is missing for many of them.

d. The authors have not adequately addressed what the “community” level is. Judging by line 187 and line 228, the community is defined as the EDHS EAs, but it would be very helpful to state this explicitly and give the number of clusters etc. in the methods section (beyond the data section that describes how the sampling was done). Also, why is health insurance a community level variable?

e. This is not a major issue – but it is indicative of overlooking specific items brought up by the reviewers: In the author response to a reviewer comment about using a cutoff of p<0.26, they said this was a typo and that it was fixed – line 181 still lists the same cutoff with the typo.

f. The authors responded to a comment about missing data in the response to reviewers, but didn’t mention how they handled missing data in the manuscript – complete case analysis (with % missing)

2. Methods: Because it is such a critical part of the study, I would suggest having a clearer sentence stating your outcome variable – example: “ The outcome variable for this study was treatment-seeking behavior (Yes/ No), defined as whether or not a caregiver sought advice or treatment from a health facility for a living child under-five who had a fever at any time in the 2 weeks preceding the survey.”

Results

1. Why not show your main outcome variable in Table 1?

2. I might also suggest adding a bivariate table. It might be helpful to see some of the bivariate associations/percentages and numbers of observations. You could truncate Table 1 and also not show every variable or every level of every variable in Table 2 if space was a concern.

Editing/Grammar/Formatting:

1. The writing/grammar is much better and really improves readability, however, the manuscript still needs some copyediting. A few notes on this are below.

2. There are some typos and inconsistencies between the different versions of the paper (clean revised manuscript and the tracked changes version). I note some specific inconsistencies below, but this should be thoroughly reviewed. Having two different versions of the revised manuscript made it very difficult to re-review this paper. Which one is the most updated/accurate?

o Introduction: in the clean version, the second sentence of the Intro (lines 55-57) includes a line from the prior sentence (major typo). This seems to be ok in the track changes version. In the clean version there is a sentence on lines 73-73 “In Ethiopia healthcare-seeking behavior is poor….” This sentence does not appear in the tracked changes version

o Paragraph delineation: Examples - in the tracked changes version, on the first page of the introduction there is a new paragraph starting with “World Health Organization.” In the clean version this is lumped in with the prior paragraph (line 68). In the clean version line 81, there is a new sentence starting with “Due to the inadequacy…” but this is an entirely new paragraph in the tracked changes version (also missing a period at the end of the prior sentence).

3. There are different fonts, spacing, sizing used throughout

4. Some capitalization typos – examples include “Children” on lines 134 and 135 (additional typos/grammar issues in the line 134 sentence), Benishangul (line149)

5. The tables need significant formatting work to be more readable – Table 1 is 4.5 pages long (unnecessarily). A reviewer previously suggested adding totals (this does not need to be for every variable – could be overall). Table 2 – one suggestion, you can delete all the lines for the reference group and just note the reference (if not obvious) in first or second column

**********

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: Yes: Ebenezer Kwesi Armah-Ansah

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

3 Dec 2021

Authors’ Response for Reviewers’ Comments

Manuscript ID: PONE-D-21-03591

Title: Individual and community-level factors associated with treatment-seeking behavior among caregivers with febrile children in Ethiopia: A Multilevel analysis

Dear editor(s) and reviewers

First for all we would like to thank you for helping us by reviewing our paper entitled, “Individual and community-level factors associated with treatment-seeking behavior among caregivers with febrile children in Ethiopia: A Multilevel analysis” by giving your precious time. Thus, your comments and questions are constructive and interesting for us. Based on your questions and suggestions we have already modified accordingly.

Reviewers question and/ comments

Response=> Authors response based on the reviewer(s) questions and comments

Reviewer #1

Thank you very much for your review. We appreciate the positive feedback from the reviewer

Reviewer #2

Q1: The authors have addressed many of the reviewer comments and the revised manuscript is greatly improved. The introduction does a better job at setting up the paper, the methods section has some additional information, and the results are more clearly presented and contextualized in the discussion. However, there are still some concerns with the paper and several comments from the prior round of review that were not addressed. Comments below are organized by section.

Response: Thank you very much for your comments and suggestions. Our revised responses based on questions and comments which may help to clarify the paper and its findings are as follows:

Methods:

1. Several of the prior comments about variables and measures were not addressed. A new section was added and some additional information was provided, but the questions/comments about variable construction, variables used in analysis not mentioned anywhere else until showing in tables, construction of indices etc. were not addressed. Each of the subitems mentioned below was brought up in the prior comments.

Response: we had been addressed the comments.

a. Additional information about the index variables is needed in the manuscript (see prior comments). Did the community development index and the household wealth index use the same WASH measures? Were all items weighted equally? How exactly were the indices constructed? How were cutoffs and quintiles decided upon? Are there references for these indices? Community level cluster variable based on the WASH and electricity (have other studies used this?)

Response: The wealth index was computed by principal component analysis based on asset based data for urban and rural areas. While the community development index was a composite variable formed from the availability of three basic services in the community: improved water supply, electricity city, and improved sanitation services. It is classified as: Low: community with none of those three services; Medium: community with one or two services; High: community with three services.

b. Some issues remain with inconsistencies between what variables are listed in different sections of the paper. For example, the first sentence on independent variables says “size of child at birth” is the measure, but then later in the same paragraph, the authors list under five height for age Z score, weight for age Z score and weight for height Z-score as the measures.

Response: size of child at birth is to indicate birth weight whereas under five heights for age Z score, weight for age Z score and weight for height Z-score were used as index for assessing the nutritional status during life after birth.

c. No information is given on several measures or why they may be important to include (some of this could go in intro if more appropriate) – examples include but are not limited to mass media exposure, vaccination, place of residence (is this the same as region? ), health insurance (is this yes/no? are there different types of insurance? Do you caregivers need insurance for sick child visits?), How is distance measured (km?, time to get there, ? Does it take main modes of transportation for the study population into account) – Some information can be ascertained from the tables, but the importance of these measures to the study and why they are included (and operationalized in certain ways) is missing for many of them.

Response: the health insurance variable is yes/no type variable and it is community health insurance. In addition, the caregivers might need health insurance because some of the services like lab investigation and medication might not fully available to the public facility. In this case the might utilize the service in private and secured their budget from their insurance. This study utilizes the DHS data from Ethiopia which measures the distance to health facility and categorized as big problem and not a big problem.

d. The authors have not adequately addressed what the “community” level is. Judging by line 187 and line 228, the community is defined as the EDHS EAs, but it would be very helpful to state this explicitly and give the number of clusters etc. in the methods section (beyond the data section that describes how the sampling was done). Also, why is health insurance a community level variable?

Response: community in our case is to mean a group of individuals that shared similar characteristics. As you said the community in EDHS data is EAs and also we had mentioned it on the manuscript its total size and the urban and rural EAs which was 645, 202, and 443 EAs respectively. We used the health insurance variable as community level because the type of health insurance that were collected by the EDHS data was community based health insurance.A community was defined as a group of households sharing a common primary sampling unit/cluster within the dataset. Community level were constructed by aggregating them from individual-level factors.

e. This is not a major issue – but it is indicative of overlooking specific items brought up by the reviewers: In the author response to a reviewer comment about using a cutoff of p<0.26, they said this was a typo and that it was fixed – line 181 still lists the same cutoff with the typo.

Response: sorry for reaped typological error but we have edited on the revised document.

f. The authors responded to a comment about missing data in the response to reviewers, but didn’t mention how they handled missing data in the manuscript – complete case analysis (with % missing)

Response: as we said in the previous comment, the missing data’s were handled by complete case analysis after checking the% of missing because the variables with missed data had missing value < 5%.

2. Methods: Because it is such a critical part of the study, I would suggest having a clearer sentence stating your outcome variable – example: “ The outcome variable for this study was treatment-seeking behavior (Yes/ No), defined as whether or not a caregiver sought advice or treatment from a health facility for a living child under-five who had a fever at any time in the 2 weeks preceding the survey.”

Response: thank you for your constructive comment, we accept it and change by your ideas

Results

1. Why not show your main outcome variable in Table 1?

Response: We thank the reviewer for the suggestions. We have included the suggested changes and track changed in the revised manuscript.

2. I might also suggest adding a bivariate table. It might be helpful to see some of the bivariate associations/percentages and numbers of observations. You could truncate Table 1 and also not show every variable or every level of every variable in Table 2 if space was a concern.

Response: We appreciate the positive feedback from the reviewer, based on your comment we have modified this section in understandable and precise way.

Editing/Grammar/Formatting:

1. The writing/grammar is much better and really improves readability, however, the manuscript still needs some copyediting. A few notes on this are below.

2. There are some typos and inconsistencies between the different versions of the paper (clean revised manuscript and the tracked changes version). I note some specific inconsistencies below, but this should be thoroughly reviewed. Having two different versions of the revised manuscript made it very difficult to re-review this paper. Which one is the most updated/accurate?

Response: After reviewer(s) suggestions, we have revised again the document and we also have found grammatical, contextual unclarity and sentence structure problems. Then we have consumed more time and energy for re-edition of the vague sentences. Based on this, we have rewritten the whole document in more understandable to resolve language problems.

Introduction: in the clean version, the second sentence of the Intro (lines 55-57) includes a line from the prior sentence (major typo). This seems to be ok in the track changes version. In the clean version there is a sentence on lines 73-73 “In Ethiopia healthcare-seeking behavior is poor….” This sentence does not appear in the tracked changes version

Response: Thank you for your comments, the suggested correction has been made.

o Paragraph delineation: Examples - in the tracked changes version, on the first page of the introduction there is a new paragraph starting with “World Health Organization.” In the clean version this is lumped in with the prior paragraph (line 68). In the clean version line 81, there is a new sentence starting with “Due to the inadequacy…” but this is an entirely new paragraph in the tracked changes version (also missing a period at the end of the prior sentence).

Response: It has been rewritten again by incorporating necessary information in this regard.

3. There are different fonts, spacing, sizing used throughout

Response: The font size and type has been checked and it is consistent in the whole document. The used font type and size is “Times New Roman” by “12”.

4. Some capitalization typos – examples include “Children” on lines 134 and 135 (additional typos/grammar issues in the line 134 sentence), Benishangul (line149)

Response: the correction has been made.

5. The tables need significant formatting work to be more readable – Table 1 is 4.5 pages long (unnecessarily). A reviewer previously suggested adding totals (this does not need to be for every variable – could be overall). Table 2 – one suggestion, you can delete all the lines for the

Response: the correction has been made.

Finally, we thank the reviewers’ editors for their kind comments, constructive criticisms and useful suggestions for which we have used to improve our manuscript. We have re-read, edit and rewrite the whole manuscript once again and made any necessary editorial corrections. Thank you for the helpful review once again.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

16 Feb 2022

Individual and community-level factors of treatment-seeking behavior among caregivers with febrile children in Ethiopia: A Multilevel analysis

PONE-D-21-03591R2

Dear Dr. liyew,

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.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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,

Enamul Kabir

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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

Reviewer #2: (No Response)

**********

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

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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

Reviewer #2: 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

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Discussion & Conclusion:

How do you explain the findings? What are the implications? The discussion could be strengthened by linking the results to previous studies. I think the authors can provide more specific policy implications based on these interesting findings.

Please kindly discuss this matter.

Reviewer #2: The authors have addressed the reviewer comments and the manuscript is acceptable for publication. I'm only including a few minor formatting/copy editing suggestions here to be helpful to the authors in their final reviews, but these do not impact the overall content of the manuscript.

1. Spacing: Spaces between text and citations – sometimes there is a space, sometimes there is not between the text and the citation. We suggest going through and adding spaces consistently throughout the manuscript. Similarly, there is inconsistent spacing in the results when they are listed in parentheses. Sometimes there is a space and sometimes not. Suggest adding spaces throughout (examples in lines 235-247). This issue is also present with the symbol % - sometimes a space, sometimes not. Suggest no spaces between the number and % (e.g. 54.0%, but add a space after the % symbol, e.g. 95% CI instead of 95%CI). Examples lines 249-270.

2. Line 190 has a minor typo. Should be a comma not a period: “To accommodate for the complex sampling design employed in the survey, weighted data analysis was employed.”

3. Starting on line 196: sometimes the authors capitalize “Model” when referring to a specific model (e.g. Model III), sometimes they do not “model II.”

4. Tables 1 &2: Capitalization in tables is inconsistent (in respondent’s characteristic column). We also suggest adding an “s” and making characteristics plural. Justification in the columns is also inconsistent (some are centered some are left).

5. Line 235 – typos in the OR, has a comma instead of a period for “38, 86” instead of “38.86”

**********

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

Reviewer #2: No

8 Mar 2022

PONE-D-21-03591R2

Individual and community-level factors of treatment-seeking behaviour among caregivers with febrile children in Ethiopia: A Multilevel analysis

Dear Dr. Liyew:

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. Enamul Kabir

Academic Editor

PLOS ONE