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. 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]. 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]. 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). 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.