Geospatial distribution and predictors of postnatal care utilization during the critical time in Ethiopia using EDHS 2019: A spatial and geographical weighted regression analysis
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| Publicat a: | PLoS One vol. 19, no. 12 (Dec 2024), p. e0309929 |
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| Altres autors: | , , , , |
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Public Library of Science
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| 001 | 3150323805 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0309929 |2 doi | |
| 035 | |a 3150323805 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Muluken Chanie Agimas | |
| 245 | 1 | |a Geospatial distribution and predictors of postnatal care utilization during the critical time in Ethiopia using EDHS 2019: A spatial and geographical weighted regression analysis | |
| 260 | |b Public Library of Science |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a IntroductionPostnatal care within 2 days after delivery is classified as early postnatal care. Maternal and neonate mortality during the early postnatal period is a global health problem. Sub-Saharan Africa contributes the highest maternal and newborn mortality rates. To reverse this problem, early postnatal care is the best strategy, but there is no study to show the spatial distribution and application of geographical weighted regression to show the effect of each predictor on early postnatal care across the geographic areas in Ethiopia using the recent EDHS 2019 data.ObjectiveTo assess the geospatial distribution and predictors of postnatal care utilization during the critical period in Ethiopia using EDHS 2019.MethodA secondary data analysis of a cross-sectional study was used among 2105 women. The data for this analysis was taken from the 2019 EDHS, and missing data was managed by imputation. The spatial variation of postnatal care during the critical time was assessed using the Getis-Ord Gi* statistic; Moran’s I statistics were conducted to test the autocorrelation; and Sat Scan statistics were also used to show the statistically significant clusters of early PNC utilization in Ethiopia. The ordinary least squares method was used to select factors explaining the geographical variation of postnatal care during the critical time. Finally, the geographical weighted regression was used to show the spatial variation of the association between predictors and outcomes. Predictors at 95% CI with a p-value <0.05 were statistically significant factors for PNC during the critical time.ResultsThe overall prevalence of PNC utilization during critical time was 713 (34%, 95%CI: 31.5%–36.5%). The spatial distribution of postnatal care utilization during critical times was not randomly distributed across the area of Ethiopia. The hotspot areas of postnatal care utilization during the critical period in Ethiopia were found to be in Benishangul, Gumuz, and the western part of Tigray. Whereas, the cold spot area was in the western part of the southern nation and nationality of Ethiopia. Women with antenatal care visits, facility delivery, no education, and media exposure were the predictors of postnatal care utilization during the critical time in the hotspot areas of Ethiopia.Conclusion and recommendationIn Ethiopia, one-third of women utilize the PNC during critical times. Postnatal care utilization during critical times was not randomly distributed across the regions of Ethiopia. Antenatal care visits, facility delivery, lack of education, and media exposure were the predictors of postnatal care utilization during the critical time in Ethiopia. Therefore, encouraging facility delivery, awareness creation by expanding media access, and literacy are highly recommended to improve the utilization of PNC services during this critical time in Ethiopia. | |
| 651 | 4 | |a Ethiopia | |
| 653 | |a Geographical distribution | ||
| 653 | |a Statistical tests | ||
| 653 | |a Sea level | ||
| 653 | |a Least squares method | ||
| 653 | |a Regression analysis | ||
| 653 | |a Health facilities | ||
| 653 | |a Births | ||
| 653 | |a Geographical variations | ||
| 653 | |a Mortality | ||
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Data analysis | ||
| 653 | |a Statistics | ||
| 653 | |a Geography | ||
| 653 | |a Spatial distribution | ||
| 653 | |a Spatial data | ||
| 653 | |a Missing data | ||
| 653 | |a Education | ||
| 653 | |a Utilization | ||
| 653 | |a Households | ||
| 653 | |a Postpartum period | ||
| 653 | |a Population | ||
| 653 | |a Software | ||
| 653 | |a Womens health | ||
| 653 | |a Public health | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Quality control | ||
| 653 | |a Statistical significance | ||
| 653 | |a Clustering | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Data processing | ||
| 653 | |a Spatial variations | ||
| 653 | |a Infant mortality | ||
| 653 | |a Variables | ||
| 653 | |a Data collection | ||
| 653 | |a Global health | ||
| 653 | |a Maternal mortality | ||
| 653 | |a Social | ||
| 700 | 1 | |a Tesfie, Tigabu Kidie | |
| 700 | 1 | |a Nebiyu Mekonnen Derseh | |
| 700 | 1 | |a Meron Asmamaw | |
| 700 | 1 | |a Abuhay, Habtamu Wagnew | |
| 700 | 1 | |a Getaneh Awoke Yismaw | |
| 773 | 0 | |t PLoS One |g vol. 19, no. 12 (Dec 2024), p. e0309929 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3150323805/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3150323805/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3150323805/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |