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Neonatal mortality outcomes for babies born small for gestational age ( , 10%) compared to babies born appropriate for gestational age. Note: 95% CI for I 2 was 0%–73.9%. doi:10.1371/journal.pmed.1001292.g004
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Low birth weight and prematurity are amongst the strongest predictors of neonatal death. However, the extent to which they act independently is poorly understood. Our objective was to estimate the neonatal mortality risk associated with preterm birth when stratified by weight for gestational age in the high mortality setting of East Africa.
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Context 1
... plots of results of meta-analyses showing ORs and 95% CIs for each study, and the pooled measure of effect and corresponding 95% CIs, are shown in Figure 2 for low birth weight, Figure 3 for preterm birth, and Figure 4 for small for gestational age. Figure 5 shows the results of neonatal mortality for preterm, stratified by weight for gestational age. All models include study-specific crude ORs, as no evidence of confounding was found for available covariates. ...
Context 2
... odds of death were twice as high for babies born small for gestational age compared to those born appropriate for gestational age (OR 2.1, 95% CI 1.3-3.5), NRM 29.3/1,000 live births (Figure 4; Table 4). ...
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... observations. Of the 915 live births in the cohort, 731 (80%) had complete information for this analysis. There were more home births amongst excluded infants (X 2 p , 0.001). One quarter of the excluded births (46/184) did not have mortality follow-up to the 28th day of life; the NMR amongst those included was 32.8/1,000 but only 7.6/1,000 amongst those excluded, although this difference did not reach statistical significance (X 2 p , 0.11) (Table 3). NMR was calculated as observed deaths within 28 d divided by observed live births. Low birth weight was defined as weight less than 2,500 g. Gestational age at birth was categorized as: term ( $ 37 completed weeks of gestation), moderately preterm (34– 36 wk), very preterm ( , 34 wk). Small weight for gestational age was defined as being below the tenth percentile of weight for gestation of a US-based reference population [19]. Babies were further categorized into six groups: (1) appropriate for gestational age and term; (2) appropriate for gestational age and 34–36 wk gestation; (3) appropriate for gestational age and , 34 wk gestation; (4) small for gestational age and term; (5) small for gestational age and 34–36 wk gestation; (6) small for gestational age and , 34 wk gestation. Taken together the four eligible studies provide strength to answer this research question, but were individually small and no previous attempt had been made to conduct this analysis. Odds ratios (ORs) and corresponding 95% CIs for neonatal mortality with relation to the exposures of low birth weight (compared to not low birth weight), moderate or very preterm (compared to term), small for gestational age (compared to appropriate for gestational age), and weight for gestational age stratified by preterm (compared to appropriate weight for gestational age and term), were calculated in each of the four datasets individually, using logistic regression. Individual measures of effect and uncertainty measures from the four studies were then included in fixed effects meta-analyses, using the metan commands in STATA. Study estimates were combined using inverse variance weighting, and pooled ORs were summarised with Mantel- Haenszel methods. Between study heterogeneity was investigated using the I 2 statistic [20] and CIs around the l 2 statistic were calculated using the user generated i2ci.ado command in STATA [21]. To inform whether ORs included within meta-analyses should be crude or adjusted, sub-group investigation for confounding of the relationship with neonatal mortality was carried out within each of the four included studies using available covariates (sex, twin, delivery characteristics, and maternal characteristics including malaria positivity at delivery). Additionally, in order to investigate the influence of exclusions due to incomplete data, sensitivity analyses comparing characteristics of included and excluded infants were conducted. Contin- uous variables were compared using t -tests, and proportions using Chi-squared tests. All analyses were carried out using STATA version 11 (Stata Corporation). The proportional distribution of newborn deaths for preterm stratified by weight for gestational age was calculated. Finally, we estimated the proportion of neonatal deaths associated with risk factors [(overall NMR - NMR for babies without the risk factor)/ overall NMR], and the mortality that could be attributed to different weight for gestational age and preterm outcomes, i.e. the attributable risk percent [1-(NMR amongst babies without the risk factor/NMR amongst babies with the risk factor)]. Both of these estimates rest on the assumption that avoiding the risk factors preterm birth or low birth weight percentile for gestational age would not affect any other factor. From these four studies 5,727 live births were observed, of whom 4,843 (85%) had a complete set of outcome data and were included in the analysis (Figure 1). The mortality estimate for all those included (18.0 per 1,000 live births [95% CI 14.2–21.7]) was less than half that of all those excluded (41.1 per 1,000 live births [95% CI 27.3–54.9]), indicating a downward bias in point estimates of neonatal mortality rates (Table 3). In the individual studies, between 6.9% and 11.3% of babies were low birth weight, being 9.2% (95% CI 8.4–10.0) for all studies combined. Between 2.7% and 5.7% of babies were preterm, being 4.0% (95% CI 3.5–4.6) for all studies combined. Between 9.9% and 26.4% of babies were small for gestational age, being 20.4% (95% CI 19.3–21.6) for all studies combined (Table 3). Amongst low birth weight babies, 26.1% (95% CI 22.0–30.4) were preterm, 85.0% (95% CI 81.7–88.5) were small for gestational age, and 98.8% (95% CI 97.3–99.6) were either preterm or small for gestational age. Forest plots of results of meta-analyses showing ORs and 95% CIs for each study, and the pooled measure of effect and corresponding 95% CIs, are shown in Figure 2 for low birth weight, Figure 3 for preterm birth, and Figure 4 for small for gestational age. Figure 5 shows the results of neonatal mortality for preterm, stratified by weight for gestational age. All models include study-specific crude ORs, as no evidence of confounding was found for available covariates. Fixed effects meta-analysis models are presented due to the relatively low heterogeneity, as summarised by the I 2 values (although it should be noted that with only four studies there was very low power to detect heterogeneity and the 95% CIs around the l values range from 0% (no heterogeneity) to 87% (high heterogeneity) [21]. The odds of death in the first 28 d of life were seven times higher for babies born low birth weight compared to those with normal birth weight (OR 7.6, 95% CI 4.8–12.1), and low birth weight infants experienced a NRM of 80.9/1,000 live births (Figure 2; Table 4). The odds of death were over six times higher for babies born moderately preterm compared to those born term (OR 6.2, 95% CI 3.0–12.8), and almost 60 times higher for babies born very preterm compared to those born term (OR 58.7, 95% CI 28.4–121.4), with almost half of very preterm babies dying in the first 28 d of life, NRM 473.6/1,000 live births (Figure 3; Table 4). The odds of death were twice as high for babies born small for gestational age compared to those born appropriate for gestational age (OR 2.1, 95% CI 1.3–3.5), NRM 29.3/1,000 live births (Figure 4; Table 4). Using babies born with weight appropriate for gestational age and at term as reference, the odds for neonatal mortality were three times higher for those born appropriate for gestational age at 34–36 wk (OR 3.2, 95% CI 0.9–10.7; NRM 18.8/1,000 live births), but 75 times higher for those born appropriate for gestational age at , 34 wk (OR 74.9, 95% CI 32.6–171.7: NRM 466.7/1,000 live births) (Figure 5; Table 4). Again using babies born appropriate for gestational age and term as reference, the odds for mortality were doubled for babies born small for gestational age at term (OR 2.2, 95% CI 1.2–4.1; NRM 20.3/ 1,000 live births), were 20 times greater for babies born small for gestational age at 34–36 wk (OR 19.9, 95% CI 8.3–47.4; NRM 145.8/1,000 live births), and were 57 times greater for babies born small for gestational age at , 34 wk gestation (OR 57.0, 95% CI 11.1–291.7; NRM 428.6/1,000 live births). After stratifying by method of assessing gestational age, ultrasound or neonatal assessment, the direction of these findings remains consistent, although the CIs are wide (Table 5). In particular, the finding of elevated odds for neonatal mortality amongst babies born small for gestational age at 34–36 wk persists (OR 24.6, 95% CI 5.1–117.8 for those measured by ultrasound; OR 18.0, 95% CI 6.3–51.4 for those measured by neonatal assessment). 23% (1,125/4,843) of the live births, but 53% (45/87) of the newborn deaths were amongst newborns born either small for gestational age or preterm. Less than 1% (37/4,843) of live births, but 20% (17/87) of deaths, were amongst very preterm infants ( , 34 wk). Just 1% (48/4,843) of live births, but 8% (7/87) of deaths, were amongst those born moderately preterm (34–36 wk) and small for gestational age. Overall, 28% of neonatal mortality was associated with being born preterm [(18 – 12.9)/18 * 100], and 39% of neonatal mortality was associated with being born either preterm or small for gestational age [(18.0 2 11.0)/18 * 100], assuming that all babies would have the same risk of neonatal death if they were born term and appropriate for gestational age (Table 4). 98% of the mortality risk (the attributable risk percent) of babies born appropriate for gestational age at , 34 wk was attributed to them having been born very preterm [(466.7 2 11.0)/466.7 * 100]. Over 90% of the neonatal mortality risk of all small for gestational age and preterm babies ( , 37 wk) was attributed to them being born small for gestational age and preterm (Table 4). 99% of low birth weight babies were either small for gestational age or preterm. Just 23% of babies were born either small for gestational age or preterm but they contributed 52% of the neonatal deaths. The 4% of babies who were born preterm were at highest likelihood of death, accounting for 30% of the neonatal deaths, with over 90% of their mortality risk being attributed to being preterm. However this analysis of data from East Africa revealed that weight for gestational age played an important role for moderately preterm babies. The odds of neonatal mortality of babies born 34–36 wk gestation and appropriate weight for gestational age was just three times higher than term babies of appropriate weight, but was 20 times higher amongst babies born 34–36 wk gestation and small for gestational age. Preterm birth is a direct cause of mortality but also aggravates the effect of other risk factors; small for gestational age may arise because of intra-uterine growth retardation which has been shown to increase the risk of mortality and ...
Context 4
... conduct this analysis. Odds ratios (ORs) and corresponding 95% CIs for neonatal mortality with relation to the exposures of low birth weight (compared to not low birth weight), moderate or very preterm (compared to term), small for gestational age (compared to appropriate for gestational age), and weight for gestational age stratified by preterm (compared to appropriate weight for gestational age and term), were calculated in each of the four datasets individually, using logistic regression. Individual measures of effect and uncertainty measures from the four studies were then included in fixed effects meta-analyses, using the metan commands in STATA. Study estimates were combined using inverse variance weighting, and pooled ORs were summarised with Mantel- Haenszel methods. Between study heterogeneity was investigated using the I 2 statistic [20] and CIs around the l 2 statistic were calculated using the user generated i2ci.ado command in STATA [21]. To inform whether ORs included within meta-analyses should be crude or adjusted, sub-group investigation for confounding of the relationship with neonatal mortality was carried out within each of the four included studies using available covariates (sex, twin, delivery characteristics, and maternal characteristics including malaria positivity at delivery). Additionally, in order to investigate the influence of exclusions due to incomplete data, sensitivity analyses comparing characteristics of included and excluded infants were conducted. Contin- uous variables were compared using t -tests, and proportions using Chi-squared tests. All analyses were carried out using STATA version 11 (Stata Corporation). The proportional distribution of newborn deaths for preterm stratified by weight for gestational age was calculated. Finally, we estimated the proportion of neonatal deaths associated with risk factors [(overall NMR - NMR for babies without the risk factor)/ overall NMR], and the mortality that could be attributed to different weight for gestational age and preterm outcomes, i.e. the attributable risk percent [1-(NMR amongst babies without the risk factor/NMR amongst babies with the risk factor)]. Both of these estimates rest on the assumption that avoiding the risk factors preterm birth or low birth weight percentile for gestational age would not affect any other factor. From these four studies 5,727 live births were observed, of whom 4,843 (85%) had a complete set of outcome data and were included in the analysis (Figure 1). The mortality estimate for all those included (18.0 per 1,000 live births [95% CI 14.2–21.7]) was less than half that of all those excluded (41.1 per 1,000 live births [95% CI 27.3–54.9]), indicating a downward bias in point estimates of neonatal mortality rates (Table 3). In the individual studies, between 6.9% and 11.3% of babies were low birth weight, being 9.2% (95% CI 8.4–10.0) for all studies combined. Between 2.7% and 5.7% of babies were preterm, being 4.0% (95% CI 3.5–4.6) for all studies combined. Between 9.9% and 26.4% of babies were small for gestational age, being 20.4% (95% CI 19.3–21.6) for all studies combined (Table 3). Amongst low birth weight babies, 26.1% (95% CI 22.0–30.4) were preterm, 85.0% (95% CI 81.7–88.5) were small for gestational age, and 98.8% (95% CI 97.3–99.6) were either preterm or small for gestational age. Forest plots of results of meta-analyses showing ORs and 95% CIs for each study, and the pooled measure of effect and corresponding 95% CIs, are shown in Figure 2 for low birth weight, Figure 3 for preterm birth, and Figure 4 for small for gestational age. Figure 5 shows the results of neonatal mortality for preterm, stratified by weight for gestational age. All models include study-specific crude ORs, as no evidence of confounding was found for available covariates. Fixed effects meta-analysis models are presented due to the relatively low heterogeneity, as summarised by the I 2 values (although it should be noted that with only four studies there was very low power to detect heterogeneity and the 95% CIs around the l values range from 0% (no heterogeneity) to 87% (high heterogeneity) [21]. The odds of death in the first 28 d of life were seven times higher for babies born low birth weight compared to those with normal birth weight (OR 7.6, 95% CI 4.8–12.1), and low birth weight infants experienced a NRM of 80.9/1,000 live births (Figure 2; Table 4). The odds of death were over six times higher for babies born moderately preterm compared to those born term (OR 6.2, 95% CI 3.0–12.8), and almost 60 times higher for babies born very preterm compared to those born term (OR 58.7, 95% CI 28.4–121.4), with almost half of very preterm babies dying in the first 28 d of life, NRM 473.6/1,000 live births (Figure 3; Table 4). The odds of death were twice as high for babies born small for gestational age compared to those born appropriate for gestational age (OR 2.1, 95% CI 1.3–3.5), NRM 29.3/1,000 live births (Figure 4; Table 4). Using babies born with weight appropriate for gestational age and at term as reference, the odds for neonatal mortality were three times higher for those born appropriate for gestational age at 34–36 wk (OR 3.2, 95% CI 0.9–10.7; NRM 18.8/1,000 live births), but 75 times higher for those born appropriate for gestational age at , 34 wk (OR 74.9, 95% CI 32.6–171.7: NRM 466.7/1,000 live births) (Figure 5; Table 4). Again using babies born appropriate for gestational age and term as reference, the odds for mortality were doubled for babies born small for gestational age at term (OR 2.2, 95% CI 1.2–4.1; NRM 20.3/ 1,000 live births), were 20 times greater for babies born small for gestational age at 34–36 wk (OR 19.9, 95% CI 8.3–47.4; NRM 145.8/1,000 live births), and were 57 times greater for babies born small for gestational age at , 34 wk gestation (OR 57.0, 95% CI 11.1–291.7; NRM 428.6/1,000 live births). After stratifying by method of assessing gestational age, ultrasound or neonatal assessment, the direction of these findings remains consistent, although the CIs are wide (Table 5). In particular, the finding of elevated odds for neonatal mortality amongst babies born small for gestational age at 34–36 wk persists (OR 24.6, 95% CI 5.1–117.8 for those measured by ultrasound; OR 18.0, 95% CI 6.3–51.4 for those measured by neonatal assessment). 23% (1,125/4,843) of the live births, but 53% (45/87) of the newborn deaths were amongst newborns born either small for gestational age or preterm. Less than 1% (37/4,843) of live births, but 20% (17/87) of deaths, were amongst very preterm infants ( , 34 wk). Just 1% (48/4,843) of live births, but 8% (7/87) of deaths, were amongst those born moderately preterm (34–36 wk) and small for gestational age. Overall, 28% of neonatal mortality was associated with being born preterm [(18 – 12.9)/18 * 100], and 39% of neonatal mortality was associated with being born either preterm or small for gestational age [(18.0 2 11.0)/18 * 100], assuming that all babies would have the same risk of neonatal death if they were born term and appropriate for gestational age (Table 4). 98% of the mortality risk (the attributable risk percent) of babies born appropriate for gestational age at , 34 wk was attributed to them having been born very preterm [(466.7 2 11.0)/466.7 * 100]. Over 90% of the neonatal mortality risk of all small for gestational age and preterm babies ( , 37 wk) was attributed to them being born small for gestational age and preterm (Table 4). 99% of low birth weight babies were either small for gestational age or preterm. Just 23% of babies were born either small for gestational age or preterm but they contributed 52% of the neonatal deaths. The 4% of babies who were born preterm were at highest likelihood of death, accounting for 30% of the neonatal deaths, with over 90% of their mortality risk being attributed to being preterm. However this analysis of data from East Africa revealed that weight for gestational age played an important role for moderately preterm babies. The odds of neonatal mortality of babies born 34–36 wk gestation and appropriate weight for gestational age was just three times higher than term babies of appropriate weight, but was 20 times higher amongst babies born 34–36 wk gestation and small for gestational age. Preterm birth is a direct cause of mortality but also aggravates the effect of other risk factors; small for gestational age may arise because of intra-uterine growth retardation which has been shown to increase the risk of mortality and morbidity [6,22,23]. Therefore being small for gestational age (especially if that was due to intra-uterine growth retardation) and preterm (even if only moderately so) may synergistically lead to the increased odds observed here. These findings have public health importance when thinking about the potential of interventions that focus on reducing intra-uterine growth retardation, or on reducing prematurity in this setting. Malaria in pregnancy interventions, for example, may have a marked impact to reduce the occurrence of severe neonatal outcomes but push a larger number of newborns into moderate categories of risk. Previous studies have reported the mortality risk associated with separate measures of birth outcome in East Africa [24], but to our knowledge, this analysis of preterm births stratified by weight for gestational age has not been presented before for an African population and thus provides much needed evidence relevant to priority setting in a high mortality setting [25]. One limitation was that the search for studies was not systematic because early discussions and searches of the literature did not reveal any studies that had addressed this problem in the African setting. The analysis does not attempt to present population level estimates of low birth weight, small for gestational age, preterm, or neonatal mortality, but rather to disentangle the relationship between ...
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Citations
... [1][2][3][4][5] Prematurity is amongst the leading cause of neonatal and under-five deaths globally, contributing to approximately 14% of under-five deaths. [4,6,7] Babies born prematurely have missed an important intrauterine growth and maturation period and are more likely to suffer from respiratory distress, intraventricular haemorrhage, poor thermoregulation, necrotizing enterocolitis, and sepsis within the first 48 hours of life. [8][9][10] Consequently, preterm babies have prolonged hospital stays, may need assisted ventilation, and have higher death rates than term babies. ...
Background
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Methods
We conducted a secondary analysis of data from the Child Health Epidemiology Reference Group, including 11 population-based pregnancy or birth cohort studies, to evaluate the appropriateness of vital event data for mortality estimation. Analyses were descriptive, summarizing study designs, populations, protocols, and internal checks to assess their impact on data quality. We calculated infant and neonatal morality rates and compared patterns with Demographic and Health Survey (DHS) data.
Results
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Conclusions
Prospective, population-based cohort studies following rigorous protocols can yield high-quality vital event data to improve characterization of detailed mortality patterns of infants in low- and middle-income countries, especially in the early neonatal period where mortality risk is highest and changes rapidly.
... Preterm birth, which accounts for 12.1% of under-five mortality in Sub-Saharan Africa, is the second most common cause of death [11]. Preterm birth and associated complications account for more than one-fifth of newborn death in Ethiopia [11][12][13][14][15][16]. ...
Introduction:
Preterm birth remains the most significant clinical and public health encounter. Preterm infant outcomes pose key evidence for clinicians and policymakers and are extensively used to set clinical and policy verdicts to improve services. It is necessary to conduct the outcomes of neonates frequently, as it varies from place to place and even from time to time in a similar place. There is limited literature in Ethiopia about preterm neonates' outcomes and their predictors.
Objective:
This study aimed to assess the neonatal outcomes of preterm neonates and their predictors in South Gondar zone public hospitals, Northwest Ethiopia, 2021.
Methods:
A prospective observational study was employed on 462 preterm neonates in South Gondar Zone Public Hospitals. The data were entered into Epidata 4.6 and analyzed using STATA version 16/MP software. A parametric log-normal survival model was used to identify possible predictors for preterm neonate death. Statistical significance was declared at a P-value less than 0.05.
Result:
The overall preterm survival rate was 71.1% (95% CI: 66.7, 75.1). Thirty-six percent of preterm neonates were diagnosed with sepsis. One-fourth of the neonates had respiratory distress syndrome. Gestational age greater than 34 weeks (β = 1.04; 95% CI: 0.53, 1.56), respiratory distress syndrome (β = 0.85; 95% CI: 0.49, 1.22), body mass index (β = -1.34; 95% CI: -1.87, -0.80), non-union marital status (β = -0.71; 95% CI: -1.34, -0.09), multiple pregnancies (β = -0.66; 95% CI: -0.99-0.32), multiparous (β = 0.35; 95% CI: 0.01, 0.69), hypothermia (β = -1.19; 95% CI: -1.76, -0.62), Kangaroo Mother Care (β = -1.9; 95% CI: -2.34, -1.41) and non-cephalic presentation (β = -1.23; 95% CI: -1.99,-0.46) were significant predictors.
Conclusion:
In this study, the preterm survival rate was low. Gestational age greater than 34 weeks, no respiratory distress syndrome, and multiparous mothers were positively associated with the survival of preterm neonates. Though, high pre-pregnancy maternal body mass index, non-union marital status of mothers, multiple pregnancies, hypothermia, Kangaroo mother care is not given, and non-cephalic presentation were negatively associated. A significant focus should be given to implementing WHO recommendations on preventing and caring for preterm births.
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Background
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Methods
A retrospective cohort study on all live births, during 1998–2017, was conducted in Sleman and Sardjito hospitals, Yogyakarta, Indonesia. Based on the reference local curve, the eligible subjects were categorized into SGA and AGA infants. The analyses were based on preterm/full-term and SGA/AGA, thus resulting in 4 categories: preterm-SGA, preterm-AGA, full-term-SGA and full-term-AGA. Analysis was made with Unadjusted Hazard Ratio (HR) by Simple Cox Regression and Adjusted HR was calculated by Multiple Cox Regression, survival analysis to calculate CMI, and analysis mortality for 5-year period ( 1998–2002, 2003–2007, 2008–2012, 2013–2017).
Result
There were 35,649 live births eligible for the study. Respiratory distress was the highest risk with HR 9,46, followed by asphyxia with HR 5,08, mother’s death with HR 227, extra-health facility with HR 1,97, symmetrical SGA with HR 1,97, preterm-AGA with HR 1,75, low birth weight (LBW) with HR 1,64, primary health facility with HR 1,33, and boys with HR 1,16 consecutively. Early neonatal mortality in 4 categories by survival analysis revealed the highest CMI in preterm SGA. Similar result was found in neonatal mortality. Analysis of 5-year period unveiled the highest CMI during 1998–2002. The highest CMI based on the four categories, however, was found in preterm-SGA.
Conclusion
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... Preterm death accounts for 35% of neonatal deaths worldwide, or 1.1 million deaths per year (Walani, 2020). Asia and Africa are responsible for an estimated 85% of global preterm neonatal delivery, increasing the possibility of neonatal death due to lack of access, utilization, and poor healthcare systems (Marchant et al., 2012). ...
Background: Throughout the World, preterm mortality is a major public health problem and a tragic event. Especially for a baby's family. However, it can sometimes be prevented with the proper treatment of neonatal problems. Objective: Our Meta-analysis was intended to measure country-level prevalence and the top factors associated with preterm newborn mortality in Ethiopia. Methods: Popular databases such as Science Direct, Hinari, Google Scholar, Embase, the Cochrane Library, and PubMed were thoroughly searched. Two Studies delineating the prevalence of preterm mortality and associated factors were included to determine the pooled prevalence. A standardized data extraction tool prepared in Microsoft Excel was used for data extraction, and the STATA 14 statistical software package was used for analysis. The Cochrane Q test statistics and the I 2 test were used to assess non-uniformity. In this meta-analysis fixed-effect model and random-effect model were used to estimate the pooled magnitude and associated factors of preterm neonatal deaths respectively. Result: The cumulative analysis of 17 studies reported that the pooled prevalence of preterm neonatal death was determined to be 12.97% (95% CI, 6.83-24.6) in the Democratic Republic of Ethiopia. Among those studies, the top magnitude was 24.70% which was conducted in Bahirdar town Amhara Regional state of Ethiopia. Preterm mortality was considerably associated with neonatal sepsis [OR = 2.28, 95% CI: (1.24, 4.20)], respiratory distress syndrome [OR = 3.24, 95% CI: 2.35, 4.46], and perinatal asphyxia [OR = 3.27, 95% CI: 1.38, 7.74] with their respective odds ratios and confidence levels. However, utilization of Kangaroo mother care can prevent 76% of preterm neonatal mortality as identified in this meta-analysis. Conclusion: In Ethiopia, preterm neonatal mortality was relatively high when compared to other countries. Neonatal sepsis and respiratory distress syndromes were found to be significantly associated with preterm neonatal mortality; however, Kangaroo Mother Care utilization was associated with a significant reduction in preterm neonatal deaths. The Ethiopian Ministry of Health needs to give attention to improving the quality of the preterm neonatal health care system and encourage the implementation of kangaroo mother care and community-based health care system strategies.
... Prematurity not only contributes directly to neonatal deaths globally but also increases the risk of death from other causes (6). These causes are multifactorial, including infant biological and medical conditions, such as birthweight, gestational age, (7)(8)(9)(10), congenital anomalies, birth complications (2,3), respiratory problems, jaundice, sepsis, necrotizing enterocolitis, intrapartum asphyxia, seizures, bleeding disorders among others (11,12). Preterm babies are more affected by these factors because they are physiologically and metabolically less mature than term infants (13). ...
Background
Preterm birth continues to be a leading cause of death for children under the age of 5 globally. This issue carries significant economic, psychological, and social costs for the families affected. Therefore, it is important to utilize available data to further research and understand the risk factors for preterm death.
Objective
The objective of this study was to determine maternal and infant complications that influence preterm deaths in a tertiary health facility in Ghana.
Methods
A retrospective analysis of data on preterm newborns was conducted at the neonatal intensive care unit of Korle Bu Teaching Hospital (KBTH NICU) in Ghana, covering the period January 2017 to May 2019. Pearson's Chi-square test of association was used to identify factors that were significantly associated with preterm death after admission at the NICU. The Poisson regression model was used to determine the risk factors of preterm death before discharge after admission to the NICU.
Results
Of the 1,203 preterm newborns admitted to the NICU in about two and half years, 355 (29.5%) died before discharge, 7.0% (n = 84) had normal birth weight (>2.5 kg), 3.3% (n = 40) had congenital anomalies and 30.5% (n = 367) were born between 34 and 37 gestational week. All 29 preterm newborns between the 18–25 gestational week died. None of the maternal conditions were significant risk factors of preterm death in the multivariable analysis. The risk of death at discharge was higher among preterm newborns with complications including hemorrhagic/hematological disorders of fetus (aRRR: 4.20, 95% CI: [1.70–10.35], p = 0.002), fetus/newborn infections (aRRR: 3.04, 95% CI: [1.02–9.04], p = 0.046), respiratory disorders (aRRR: 13.08, 95% CI: [5.50–31.10], p < 0.001), fetal growth disorders/restrictons (aRRR: 8.62, 95% CI: [3.64–20.43], p < 0.001) and other complications (aRRR: 14.57, 95% CI: [5.93–35.77], p < 0.001).
Conclusion
This study demonstrate that maternal factors are not significant risk factors of preterm deaths. Gestational age, birth weight, presence of complications and congenital anomalies at birth are significantly associated with preterm deaths. Interventions should focus more on child health conditions at birth to reduce the death of preterm newborns.
... Previous studies identified respiratory distress syndrome (RDS) (19,22), asphyxia (18,22,23), sex (24)(25)(26)(27), maternal residency (19,28), gestational age (25,29,30), birth weight (18,27,31), neonatal sepsis (19,23,26), jaundice (18,23), hyaline membrane disease (18,23), hypothermia at admission (23), hypoglycemia (18), maternal chronic disease (19,22), and parity (22) as a predictor of mortality for preterm neonates. ...
... This finding was in line with that of a study conducted at the University of Gondar, Ethiopia (18). Also, this finding was supported by studies conducted in Ethiopia (23), Iran (29), and East Africa (30). Clinical evidence also supports this finding. ...
Background
In the year 2015, more than one-third of neonatal deaths caused by prematurity was recorded worldwide. Despite different kinds of efforts taken at the global and local levels to reduce neonatal mortality, it remains high with low reduction rates, especially in low- and middle-income countries like sub-Saharan Africa and South Asia. Therefore, this study aims to assess the survival status and predictors of mortality among preterm neonates.
Methods
A retrospective follow-up study was conducted on randomly selected 561 preterm neonates. Data were extracted from patient records using a pretested checklist. Data entry and analysis were done using Epi-Data Version 4.4.2.1 and Stata version 14, respectively. The Cox proportional hazard regression model was fitted to identify the predictors of mortality. A hazard ratio with a 95% confidence interval (CI) was estimated and p -values < 0.05 were considered statistically significant.
Result
The proportion of preterm neonatal deaths was 32.1% (180) with an incidence of 36.6 (95% CI: 31.6–42.4) per 1,000 person days. The mean survival time was 18.7 (95% CI: 17.7–19.9) days. Significant predictors for time to death of preterm neonates were respiratory distress syndrome [adjusted hazard ratio (AHR): 2.04; 95% CI: 1.48–2.82], perinatal asphyxia (AHR: 2.13; 95% CI: 1.32–3.47), kangaroo mother care (AHR: 0.14; 95% CI: 0.08–0.24), and gestational age (AHR: 0.85; 95% CI: 0.80–0.90).
Conclusion
Preterm neonatal death is still a major public health concern. Respiratory distress syndrome, perinatal asphyxia, kangaroo mother care, and gestational age were independent significant predictors for time to death, as found in this study. Hence, priority must be given to neonates with the above illnesses and strengthen the management and care of preterm neonates.
... Birthweight is linked with fetal, neonatal, and postnatal mortality; and long-term growth and development [6]. LBW is the principal contributing factor to neonatal mortality [7]. Globally, nearly 15-20% of all births are LBW [8,9], a majority being in resource-poor countries [8]. ...
... Merging preterm birth and SGA as LBW may inhibit the advancement of protective mechanisms [17]. A limited number of studies have scrutinized the mortality risk among preterm births and SGA neonates of LBW [7] and the risk and timing of death have not been as well studied in developing countries like Ethiopia [18]. Mortality rates stratified by birth size-for-gestational age are also important in assessing perinatal services and in counseling parents [19]. ...
... We noted that greater than half of the neonatal deaths (55.23%) occurred among newborn babies who were born both SGA and preterm. It is analogous to findings from East African countries [7] and India [40]. ...
Background
Low birth weight (LBW) and preterm birth are leading causes of under-five and neonatal mortality globally. Data about the timing of death and outcomes for LBW and preterm births are limited in Ethiopia and could be used to strengthen neonatal healthcare. This study describes the incidence of neonatal mortality rates (NMR) stratified by newborn size at birth for gestational age and identifies its predictors at five public hospitals in Ethiopia.
Methods
A prospective follow-up study enrolled 808 LBW neonates from March 2017 to February 2019. Sex-specific birthweight for gestational age percentile was constructed using Intergrowth 21 st charts. Mortality patterns by birthweight for-gestational-age-specific survival curves were compared using the log-rank test and Kaplan-Meier survival curves. A random-effects frailty survival model was employed to identify predictors of time to death.
Results
Among the 808 newborns, the birthweight distribution was 3.2% <1000 g, 28.3% <1500 g, and 68.1% <2000 g, respectively. Birthweight for gestational age categories were 40.0% both preterm and small for gestational age (SGA), 20.4% term SGA, 35.4% appropriate weight for gestational age, and 4.2% large for gestational age (LGA). The sample included 242 deaths, of which 47.5% were both preterm and SGA. The incidence rate of mortality was 16.17/1000 (95% CI 14.26–18.34) neonatal-days of observation.
Neonatal characteristics independently related to increased risk of time-to-death were male sex (adjusted hazards ratio [AHR] 3.21 95% CI 1.33–7.76), born preterm (AHR 8.56 95% CI 1.59–46.14), having been diagnosed with a complication (AHR 4.68 95% CI 1.49–14.76); some maternal characteristics and newborn care practices (like lack of effective KMC, AHR 3.54 95% CI 1.14–11.02) were also significantly associated with time-to-death.
Conclusions
High mortality rates were measured for low birthweight neonates–especially those both preterm and SGA births–even in the context of tertiary care. These findings highlight the need for improved quality of neonatal care, especially for the smallest newborns.
... Globally the leading cause for neonatal intensive care (NICU) admission is preterm and more than 60% of prematurity occurs in low-and middle-income countries (LMICs) [4,5]. Studies conducted on preterm birth recommends that focused and continuous studies should be conducted to fill the information gap in many developing countries [6]. ...
Background
Respiratory distress syndrome (RDS) is caused by a deficiency of a molecule called surfactant. It occurs in newborns born before 37 weeks of gestation. It is a main cause of morbidity and mortality in the early neonatal period.
Therefore, this study aims to assess median time to recovery and predictors of preterm neonates with respiratory distress syndrome admitted in University of Gondar comprehensive specialized hospital Northwest Ethiopia 2020.
Methods
Institution based retrospective follow up study was conducted on 386 preterm neonates with hyaline membrane disease who were admitted in the neonatal intensive care unit from January, 2016 to December 2018. The data were entered in to EPI info version 7.0 and transferred to Stata version 14.0 for analysis. Both bi-variable and multi variable Weibull parametric model were fitted to identify predictors with 95% confidence interval of hazard ratio (HR) and p-value. P-value less than 0.05 in the multivariable model showed the presence of significant association between covariates and the dependent variable.
Results
The overall median length of recovery were 11 day with an interquartile range of (7, 16) neonate-days. Being a product of multiple pregnancy (AHR 1.67; 95%CI (1.25, 2.23)), vaginal mode of delivery (AHR 1.6; 95%CI (1.13, 2.26)), and neonatal hypothermia at admission (AHR 1.6; 95%CI (1.13, 2.26)) were found to be significant predictors of time to recovery.
Conclusion
In this study the median time to recovery of preterm neonates with respiratory distress syndrome was slower than the clinical recommendations. Receiving bag and mask ventilation and hypothermia decreased the recovery whereas, vaginal delivery gestational age at birth, being multiple pregnancy, birth weight ≥2000grams were enhance the recovery of preterm neonates with RDS.
