Egyptian neonatal mortality rate analysis between 1960 till 2016 using a suggested nonparametric Lomax density estimation

2019 ◽  
Vol 14 (3) ◽  
pp. 103-112
Author(s):  
Samah M. Abo-El-Hadid
Keyword(s):  
Mortality Rate ◽  
Neonatal Mortality ◽  
Density Estimation ◽  
Neonatal Mortality Rate ◽  
Rate Analysis

scholarly journals Density Estimation of Neonatal Mortality Rate Using Empirical Bayes Deconvolution in Central Java Province, Indonesia

2021 ◽  
Vol 179 ◽  
pp. 361-367
Author(s):  
Fevi Novkaniza ◽  
Khairil Anwar Notodiputro ◽  
I Wayan Mangku ◽  
Kusman Sadik
Keyword(s):  
Mortality Rate ◽  
Neonatal Mortality ◽  
Density Estimation ◽  
Empirical Bayes ◽  
Neonatal Mortality Rate ◽  
Central Java ◽  
Central Java Province

scholarly journals Density Estimation of Neonatal Mortality Rate Using Empirical Bayes Deconvolution with Poisson Distributed Surrogate in Central Java Province Indonesia

2020 ◽  
pp. 206-212
Author(s):  
Fevi Novkaniza ◽  
Khairil Anwar Notodiputro ◽  
I Wayan Mangku ◽  
Kusman Sadik
Keyword(s):  
Mortality Rate ◽  
Neonatal Mortality ◽  
Density Estimation ◽  
Poisson Distribution ◽  
Empirical Bayes ◽  
Prior Density ◽  
Neonatal Mortality Rate ◽  
Neonatal Deaths ◽  
Central Java ◽  
Central Java Province

This article is concerned with the density estimation of Neonatal Mortality Rate (NMR) in Central Java Province, Indonesia. Neonatal deaths contribute to 73% of infant deaths in Central Java Province. The number of neonatal deaths for 35 districts/municipalities in Central Java Province is considered as Poisson distributed surrogate with NMR as the rate of Poisson distribution. It is assumed that each number of neonatal deaths by district/municipality in Central Java Province were realizations of unobserved NMR, which come from unknown prior density. We applied the Empirical Bayes Deconvolution (EBD) method for estimating the unknown prior density of NMR based on Poisson distributed surrogate. We used secondary data from the Health Profiles of Central Java Province, Indonesia, in 2018. The density estimation of NMR by the EBD method showed that the resulting prior estimate is relatively close to the Gamma distribution based on Poisson surrogate. This is implying that the suitability of the obtained prior density estimation as a conjugate prior for Poisson distribution.

IMPACT OF NEONATAL INTENSIVE CARE

PEDIATRICS ◽  
1996 ◽  
Vol 97 (2) ◽  
pp. 215-215
Author(s):  
Student
Keyword(s):  
Intensive Care ◽  
Birth Weight ◽  
Mortality Rate ◽  
Neonatal Mortality ◽  
Neonatal Intensive Care ◽  
Neonatal Mortality Rate ◽  
Total Reduction ◽  
Weight Groups

Neonates who weighed > 1.5 kg at birth were the major contributors to the overall reduction in the neonatal mortality rate (NMR); approximately two thirds of total reduction in the NMR between 1960 and 1980 and 52.6% of the total reduction between 1980 and 1986 occurred in the > 1.5-kg birth weight groups.

The Mortality of Extremely Premature Infants and the Impact on the Overall Neonatal Mortality Rate [20C]

2016 ◽  
Vol 127 ◽  
pp. 31S
Author(s):  
Horatio S. Falciglia ◽  
William K. Brady ◽  
Ronald Merkel ◽  
Vickie Glover ◽  
Angela N. Fellner
Keyword(s):  
Mortality Rate ◽  
Neonatal Mortality ◽  
Premature Infants ◽  
Neonatal Mortality Rate ◽  
The Impact

scholarly journals Infant mortality: comparison between two birth cohorts from Southeast and Northeast, Brazil

2004 ◽  
Vol 38 (6) ◽  
pp. 773-779 ◽  
Author(s):  
Valdinar S Ribeiro ◽  
Antônio A M Silva ◽  
Marco A Barbieri ◽  
Heloisa Bettiol ◽  
Vânia M F Aragão ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Neonatal Mortality ◽  
Protective Factor ◽  
Infant Mortality Rate ◽  
Northeast Brazil ◽  
Birth Cohorts ◽  
Neonatal Mortality Rate ◽  
Live Births

OBJECTIVE: To obtain population estimates and profile risk factors for infant mortality in two birth cohorts and compare them among cities of different regions in Brazil. METHODS: In Ribeirão Preto, southeast Brazil, infant mortality was determined in a third of hospital live births (2,846 singleton deliveries) in 1994. In São Luís, northeast Brazil, data were obtained using systematic sampling of births stratified by maternity unit (2,443 singleton deliveries) in 1997-1998. Mothers answered standardized questionnaires shortly after delivery and information on infant deaths was retrieved from hospitals, registries and the States Health Secretarys' Office. The relative risk (RR) was estimated by Poisson regression. RESULTS: In São Luís, the infant mortality rate was 26.6/1,000 live births, the neonatal mortality rate was 18.4/1,000 and the post-neonatal mortality rate was 8.2/1,000, all higher than those observed in Ribeirão Preto (16.9, 10.9 and 6.0 per 1,000, respectively). Adjusted analysis revealed that previous stillbirths (RR=3.67 vs 4.13) and maternal age <18 years (RR=2.62 vs 2.59) were risk factors for infant mortality in the two cities. Inadequate prenatal care (RR=2.00) and male sex (RR=1.79) were risk factors in São Luís only, and a dwelling with 5 or more residents was a protective factor (RR=0.53). In Ribeirão Preto, maternal smoking was associated with infant mortality (RR=2.64). CONCLUSIONS: In addition to socioeconomic inequalities, differences in access to and quality of medical care between cities had an impact on infant mortality rates.

Retrospective analysis of neonatal deaths secondary to infections in England and Wales, 2013–2015

2020 ◽  
pp. fetalneonatal-2020-319093
Author(s):  
Godwin Oligbu ◽  
Leila Ahmed ◽  
Laura Ferraras-Antolin ◽  
Shamez Ladhani
Keyword(s):  
Mortality Rate ◽  
Retrospective Analysis ◽  
Neonatal Mortality ◽  
Mortality Rates ◽  
Neonatal Mortality Rate ◽  
England And Wales ◽  
Neonatal Deaths ◽  
Live Births ◽  
Extremely Preterm ◽  
Related Mortality

ObjectiveTo estimate the overall and infection-related neonatal mortality rate and the pathogens responsible using electronic death registrations.DesignRetrospective analysis of national electronic death registrations data.SettingEngland and Wales.PatientsNeonates aged <28 days.Main outcome measuresOverall and infection-related mortality rate per 1000 live births in term, preterm (28–36 weeks) and extremely preterm (<28 weeks) neonates; the contribution of infections and specific pathogens; comparison with mortality rates in 2003–2005.ResultsThe neonatal mortality rate during 2013–2015 (2.4/1000 live births; 5095 deaths) was 31% lower than in 2003–2005 (3.5/1000; 6700 deaths). Infection-related neonatal mortality rate in 2013–2015 (0.32/1000; n=669) was 20% lower compared with 2003–2015 (0.40/1000; n=768), respectively. Infections were responsible for 13.1% (669/5095) of neonatal deaths during 2013–2015 and 11.5% (768/6700) during 2003–2005. Of the infection-related deaths, 44.2% (296/669) were in term, 19.9% (133/669) preterm and 35.9% (240/669) extremely preterm neonates. Compared with term infants (0.15/1000 live births), infection-related mortality rate was 5.9-fold (95% CI 4.7 to 7.2) higher in preterm (0.90/1000) and 188-fold (95% CI 157 to 223) higher in extremely preterm infants (28.7/1000) during 2013–2015. A pathogen was recorded in 448 (67%) registrations: 400 (89.3%) were bacterial, 37 (8.3%) viral and 11 (2.4%) fungal. Group B streptococcus (GBS) was reported in 30.4% (49/161) of records that specified a bacterial infection and 7.3% (49/669) of infection-related deaths.ConclusionsOverall and infection-related neonatal mortality rates have declined, but the contribution of infection and of specific pathogens has not changed. Further preventive measures, including antenatal GBS vaccine may be required to prevent the single most common cause of infection-related deaths in neonates.

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