Background: Accessibility and utilization of postnatal newborn check-ups within 2 days after delivery are critical for a child’s survival, growth, and development. However, the service delivery is not yet improved and fluctuates across the geographical locations in Ethiopia. Therefore, this study aimed to assess the spatial distribution and determinants of newborns not received postnatal check-ups within 2 days after birth in Ethiopia. Methods: A secondary data analysis of the Ethiopia Demographic and Health Survey (EDHS) 2016 was done among live births within 2 years preceding the survey. A multilevel binary logistic regression model was fitted to identify the factors associated with the outcome variable. Adjusted Odds Ratio with 95% (Confidence Interval) was calculated and used as a measure of associations and variables with a p-value 4 ANC visits. Mothers who gave birth at home and others were 80% (AOR = 0.02(0.01–0.29) and 25% (AOR = 0.76(0.59–0.99), higher than mothers delivered at hospital. Rural mothers were 1.90 times higher (AOR = 1.90(1.29–2.81) than urban mothers, and mothers live in administrative regions of Afar 66% (AOR = 0.34(0.16–0.69), Oromia 47% (AOR = 0.53(0.30–0.91), Somali 60% (AOR = 0.40 (0.22–0.74),Benishangul 50% (AOR = 0.50 (0.27–0.92), SNNPR 67% (AOR = 0.33(0.19–0.57), Gambela 70% (AOR = 0.30 (0.16–0.56), Harari 56% (AOR = 0.44 (0.25–0.78), and Dire Dawa70% (AOR = 0.30 (0.17–0.54) were higher than Addis Abeba for not receiving postnatal checkup of new born within the first 2 days, respectively. Conclusions: Low postnatal check-up utilization remains a big challenge in Ethiopia, with significant spatial variations across regional and local levels. Spatial clustering of not receiving postanal check-ups within 2 days was observed in Afar, Oromia, Gambela, Benishangul, SNNPR, Harari, and Dire Dawa regions. Residence, ANC visits, place of delivery, and administrative regions were significantly associated with not receiving postnatal check-ups. Geographically targeted interventions to improve ANC follow-up and institutional delivery should be strengthened.
The study was done in Ethiopia using theEthiopian Demographic Health Survey 2016. Ethiopia is among the oldestcountry worldwide, which is located in the Horn of Africa at 3′ and 14.8″ latitude 33′ and 48′ longitude. The countryis bordered by Sudan in the west, Somalia and Djibouti in the east, Eritrea in the north, and Kenya in the south. The country has a surface area of1,112,000 km2. It is a rugged, landlocked country split by the Great Rift Valley, with archaeological finds dating back more than 3 million years; it’s a place of ancient culture. Among the important sites in Lalibela with its rock-cut Christian churches from the 12th–13th centuries. Aksum is the ruins of an ancient city with obelisks, tombs, Our Lady Mary of Zion church, and Gondar Fasil castles. Ethiopia is the 10th largest and the 2nd-most populous country in Africa after Nigeria. Administratively Ethiopia has ten regions (Tigray, Afar, Amhara, Benshangul, Gambela, Harari, Oromia, Somali, Southern, Nations, Nationalities, and People’s Region (SNNPR), and Sidama (a recently added and two city administrations (Addis Ababa and Dire Dawa). Concerning resident, 79.2% of the Ethiopian population lives in rural, and 43.3% is under fifteen ages [19]. Ethiopia uses a three-tier healthcare system; 1) primary healthcare systemconsists of health posts, health centers, and primary hospitals, 2) secondary healthcare consists of zonal hospitals, and 3) tertiary healthcare consists of comprehensive specialized hospitals [20]. Across-sectional retrospective study design was conducted to assess the geospatial variation and determinants of newborns not receiving postnatal check-ups within 2 days after birth. The data used in this article were obtained from the Ethiopia Demography and Health Survey (EDHS) 2016, which was accessed at the MEASURE DHS website after securing a formal request to the MEASURE DHS program. The survey was carried out by the central statistics agency of Ethiopia, and the Ethiopia Public Health institute Ethiopia with the technical assistance provided by ICF International. The authors requested the measure DHS trough briefly stating the objectives of this analysis and access was granted to use the data on the (http://dhsprogram.com) website [21]. The source population of this study was all mothers with newborns born in the last 2 years preceding the survey. Multi-stage stratified cluster sampling was used to select the study participants. In the first stage, 645 clusters or enumerations areas were selected randomly, and stratified into urban and rural. In the second stage, a fixed number of 28 households in each cluster was randomly selected [21]. Geographic coordinates of each survey cluster were also collected using Global Positioning System (GPS) [21]. Mothers aged 15–49 and children born within the 2 years preceding the survey in each selected household were subjected to our study. The study was conducted among (3832 un-weighted and 4036 weighted frequency) newborns to assess the postnatal health checkups within 2 days after delivery (Fig. 1). Shows the number of clusters in Ethiopia EDH S data 2016(n = 645 clusters) The outcome variable for this study is postnatal check up within the first 2 days after birth. The under five data sets (KR) files, EDHS 2016 were used for this analysis, by computing the selected function of postnatal care for new born within the first 2 days after birth and relating variables. The computing variables were Cord examined (m78a) + Temperature measured (m78b) + Counseling on danger signs (m78c) + Counseling on breastfeeding (m78d) + Observation of breastfeeding (m78e). It was recorded as “No(0)” for new born not receiving a postnatal check within 2 days after birth and “yes [1]” new born receiving a postnatal check within 2 days after birth. The independent variables used in this study were as nested into; 1) individual levelfactorssuch as marital status, maternal age, religion, maternal occupation, maternal education, residence, sex of newborn, wealth index, number of ANC, place of delivery, size of the newborn at birth and number of living children inthe family. 2) Community level factors such as region,distance to the health facility, community illiteracy level, health insurance, media exposure, access to electricity, access to safe water, and community poverty. Some of the community-level factors were aggregated from the individual-level factors. We performed a secondary analysis of the EDHS 2016, using the Kids Records (KR) dataset. STATA version 14 and Microsoft Excel 16 were used for data cleaning and coding, and the spatial analysis, and mapping were done using ArcGIS version 10.8. Descriptive statistics such as frequency and percentage of different variables were computed and presented using texts, tables, and graphs. After preparing the data we imported the data to ArcGIS version 10.8. Joining the outcome with the GPS data and projections of the Geographically coordinated data to the projected coordinatedata were conducted before the analysis. Spatial autocorrelation analysis was done to test whether there is spatial variation across the study area. The spatial autocorrelation test signifies whether there is clustering or dispersion of postnatal check-ups within 2 days after birth. The value of the Morans Index is standardized into Z-score. Positive Morans Index value with positive Z-score (> 1.96, P-value< 0.05) indicates clustering/ hot spots areas. Negative Morans Index value with a negative Z-score (<− 1.96, P-value1.96 (P < 0.05) was considered a cold spot area. GetisOrd Gi* statistic was applied to detect hotspot area or spatial clustering of newborns not receiving postnatal check-ups. Ordinary kriging interpolation was used to estimate/ predict the spatial distributions of not receiving a postnatal check within 2 days after delivery. A multilevel binary logistic regression model was applied for each independent variable and p-value < 0.2 were entered into the multivariable multilevel logistic regressions model. The adjusted odds ratio was calculated and used as the measure of association between the dependant and independent variables, and variables having a p-value < 0.05, 95% CI were considered statistically significant. In the EDHS data, the newborn is nested within a cluster, newborns within the same cluster were more similar to each other than within different clusters. Therefore, this violates the standard regression model assumptions, which are independence of observation and equal variance across the cluster assumptions. This implies they need to take into account between-cluster variables by using an advanced model. Therefore, a multilevel random intercept logistic regression model was fitted to estimate the association between individual-level and community-level factors and the likelihood of new-born not receiving postnatal care within 2 days after birth. Models were compared based on deviance (−2log likelihood) since the models were nested. Log-likelihood and intracellular correlation coefficient (ICC) was computed to measure the variation between clusters. The ICC indicates the degree of heterogeneity of new- born not receiving postnatal care within 2 days after birth. A multilevel binary logistic regression analysis was performed to examine the effects of individual and community level factors on newborns not receiving postnatal check-ups within 2 days after birth to identify individual and community level factors.
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