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Background Childhood acute respiratory infection remains the commonest global cause of morbidity and mortality among under-five children. In Ethiopia, it remains the highest burden of the health care system. The problem varies in space and time, and exploring its spatial distribution has supreme importance for monitoring and designing effective intervention programs. Methods A two stage stratified cluster sampling technique was utilized along with the 2016 Ethiopian Demographic and Health Survey (EDHS) data. About 10,006 under-five children were included in this study. Bernoulli model was used to investigate the presence of purely spatial clusters of under-five acute respiratory infection using SaTScan.ArcGIS version 10.1 was used to visualize the distribution of pneumonia cases across the country. Mixed-effect logistic regression model was used to identify the determinants of acute respiratory infection. Result In this study, acute respiratory infection among under-five children had spatial variations across the country (Moran’s I: 0.34, p < 0.001). Acute respiratory infection among under-five children in Tigray (p < 0.001) and Oromia (p < 0.001) National Regional States clustered spatially. History of diarrhoea (Adjusted Odds Ratio (AOR) = 4.71, 95% CI: (3.89–5.71))), 45–59 months of age (AOR = 0.63, 95% CI: (0.45–0.89)), working mothers (AOR = 1.27, 95% CI: (1.06–1.52)), mothers’ secondary school education (AOR = 0.65; 95% CI: (0.43–0.99)), and stunting (AOR = 1.24, 95% CI: (1.00–1.54)) were predictors of under-five acute respiratory infection. Conclusion and recommendation In Ethiopia, acute respiratory infection had spatial variations across the country. Areas with excess acute respiratory infection need high priority in allocation of resources including: mobilizing resources, skilled human power, and improved access to health facilities.
A community-based cross-sectional study was conducted from January 18 to June 27, 2016. The study was conducted in Ethiopia (3o-14o N and 33o – 48°E), situated in the eastern tip of Africa (Fig 1). The country covers an area of 1.1 million km2 (square kilometre) with geographical diversity, ranging from 4,550 meter (m) above sea level down to the Afar depression 110 m below sea level. There are nine regional states and two city administrations sub-divided into 68 zones, 817 districts, and 16,253 kebeles (lowest local administrative units of the country) in the administrative structure of the country [19]. The source population was all under-five children (10,006) included in the 2016 EDHS.A stratified two-stage cluster sampling procedure was employed where enumeration areas (EAs) were the sampling units for the first stage and households were the second stage. In the 2016 EDHS, a total of 645 EAs (202 urban and 443 rural) were selected with a probability proportional to EAs size (based on the 2007 housing and population census) and independent selection in each sampling stratum. Of these, 18,008 households and 16,583 eligible women were included. The detailed sampling procedure was presented in the full EDHS report [19]. Acute respiratory infection among under-five children two weeks prior to data collection were used as a dependent variable. The independent variables were classified as socio-demographic factors including sex, age of child, residence, maternal education, maternal occupation, number of under-five children, religion, and wealth index; environmental factors were source of water, types of toilet facility, and types of cooking fuel. Clinical factors including vaccination history and drug for intestinal parasites, nutritional factors: breast feeding duration, stunting, wasting, and vitamin A supplement, and co-morbidity conditions including history of diarrhoea, HIV status, anaemia status, and measles. Acute respiratory infection was defined as children that had history of cough, accompanied by short rapid breathing and/ or difficulty of breathing reported by mothers or caregivers within two weeks preceding the survey [20, 21].Co-morbidity was defined as the presence of one or more additional diseases co-occurring with a primary disease, pneumonia in under- five children [18, 22]. A letter of approval for the use of the data was secured from the Measure DHS and the data set was downloaded from the website www.measuredhs.com(https://dhsprogram.com/data/available-datasets.cfm). We used EDHS 2016 child data set and extracted the outcome and explanatory variables. Location data (latitude and longitude coordinates) was also taken from selected enumeration areas (clusters). A structured and pre-tested questionnaire was used as a tool for data collection. The 2016 EDHS interviewers used tablet computers to record responses during interviews. The tablets were equipped with Bluetooth technology to enable remote electronic transfer of files (transfer assignment sheets from team supervisors to interviewers and transfer of completed copies from interviewers to supervisors) [19]. Prior to the actual data collection, interviewers were trained and a pre-test was performed. Interviews were performed using local languages [19]. Cross tabulations and summary statistics were used to describe the study population. Descriptive and summary statistics were done using STATA version 14 software. In EDHS data, children within a cluster may be more similar to each other than children in the rest of the country. This violates the assumption of traditional regression model which are the independence of observations and equal variance across clusters. This implies that the need to consider the between-cluster variability using advanced models. Therefore, a multilevel model (both fixed and random effects) was used. As the response variable was dichotomous, logistic regression and Generalized Linear Mixed models (GLMM) were fitted. Model comparison was done based on Akakie Information Criteria (AIC), Bayesian information Criteria (BIC), and intra cluster correlation (ICC) values. The model with the lowest AIC was chosen. Variables with ≤0.2 p-values in the bi-variable analysis were fitted in the multivariable model. Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p-value <0.05 in the multivariable model were used to declare significant association with ARI. Goodness of fit was checked using deviance and ICC. Spatial autocorrelation (Global Moran’s I) statistic measure was used to evaluate whether disease patterns were dispersed, clustered, or randomly distributed in the study area. Moran’s I values close to−1 indicated disease dispersed, whereas I close to +1 indicated disease clustered, and disease distributed randomly if I value was zero. A statistically significant Moran’s I (p < 0.05) led to the rejection of the null hypothesis and indicated the presence of spatial autocorrelation. ArcGIS version 10.1 was used for doing the Moran I analysis. Spatial scan statistical analysis was employed to identify the geographical locations of statistically significant spatial clusters of ARI among under-five children using Kuldorff’sSaTScan version 9.4 software [23]. Spatial scan statistic used a scanning window that moves across the study area. Children with ARI were taken as cases and those without the disease as controls to fit the Bernoulli model. The number of cases in each location had Bernoulli distribution and the model required data with or without the disease. The default maximum spatial cluster size of <50% of the population was used as an upper limit, allowing both small and large clusters to be detected, and ignored clusters that contained more than the maximum limit with circular shape of window. A Likelihood ratio test statistic was used to determine whether the number of observed ARI cases within the potential cluster were significantly higher than the expected or not. Primary and secondary clusters were identified using p-values and likelihood ratio tests on the basis of the 999 Monte Carlo replications [24]. Ethical clearance was obtained from the University of Gondar Ethical Review Board (IRB). Permission was obtained from Measure DHS International Program which authorized the datasets. All data used in this study were publicly available; and aggregated secondary data with no any personal identity.
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