scholarly journals P2-544 Infant mortality estimation in Brazil: results of a proactive search of deaths and live births in poor regions

2011 ◽  
Vol 65 (Suppl 1) ◽  
pp. A371-A371
Author(s):  
C. Szwarcwald ◽  
O. Morais
Keyword(s):  
Infant Mortality ◽  
Live Births ◽  
Mortality Estimation

scholarly journals Newborns and Under-5 Mortality in Ethiopia: The Necessity to Revitalize Partnership in Post-COVID-19 Era to Meet the SDG Targets

2021 ◽  
Vol 12 ◽  
pp. 215013272199688
Author(s):  
Yonas Getaye Tefera ◽  
Asnakew Achaw Ayele
Keyword(s):  
Child Mortality ◽  
Millennium Development Goals ◽  
Sustainable Development Goals ◽  
National Plan ◽  
Health Targets ◽  
The Sustainable Development ◽  
Live Births ◽  
Mortality Estimation ◽  
Development Goals ◽  
Children Under 5

The Sustainable Development Goals (SDGs) were adopted during the United Nations meeting in 2015 to succeed Millennium Development Goals. Among the health targets, SDG 3.2 is to end preventable deaths of newborns and children under 5 years of age by 2030. These 2 targets aim to reduce neonatal mortality to at least as low as 12 per 1000 live births and under-5 mortality to at least as low as 25 per 1000 live births. Ethiopia is demonstrating a great reduction in child mortality since 2000. In the 2019 child mortality estimation which is nearly 5 years after SDGs adoption, Ethiopia’s progress toward reducing the newborns and under-5 mortality lie at 27 and 50.7 per 1000 live births, respectively. The generous financial and technical support from the global partners have helped to achieve such a significant reduction. Nevertheless, the SDG targets for newborns and under-5 mortality reduction are neither attained yet nor met the national plan to achieve by the end of 2019/2020. The partnership dynamics during COVID-19 crisis and the pandemic itself may also be taken as an opportunity to draw lessons and spur efforts to achieve SDG targets. This urges the need to reaffirm a comprehensive partnership and realignment with other interconnected development goals. Therefore, collective efforts with strong partnerships are required to improve the determinants of child health and achieving SDG target reduction until 2030.

Social Dimensions of Genetic Disorders

1992 ◽  
Vol 14 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Joan Ablon
Keyword(s):  
Health Education ◽  
Infant Mortality ◽  
Genetic Disease ◽  
Genetic Factors ◽  
Genetic Disorders ◽  
Genetic Defects ◽  
Social Dimensions ◽  
Live Births ◽  
Department Of Health ◽  
Recessive Genes

Each of us carries between 4-8 recessive genes for serious genetic defects, and, hence, stands a statistical chance of passing on a serious or lethal condition to each child… 12 million Americans carry true genetic disease due wholly or partly to defective genes or chromosomes…40 percent or more of all infant mortality results from genetic factors…4.8 to 5 percent of all live births have genetic defects. (U.S. Department of Health, Education, and Welfare. "What are the Facts About Genetic Disease?" National Inst. of Gen. Med. Scs., P.H.S., N.I.H. DHEW Pub. No. (NIH), 75-370, 1975.)

Risk of Neonatal and Infant Mortality in Twins and Singletons by Gestational Age

2018 ◽  
Vol 36 (08) ◽  
pp. 798-805 ◽  
Author(s):  
Han-Yang Chen ◽  
Suneet P. Chauhan
Keyword(s):  
Infant Mortality ◽  
Gestational Age ◽  
Regression Models ◽  
Causes Of Death ◽  
Error Variance ◽  
Mortality Rates ◽  
Vital Statistics ◽  
Adjusted Risk ◽  
Live Births ◽  
Risk Ratios

Objective To compare neonatal and infant mortality rates stratified by gestational age (GA) between singletons and twins and examine the three leading causes of death among them. Study Design This was a retrospective cohort study using the U.S. vital statistics datasets. The study was restricted to nonanomalous live births at 24 to 40 weeks delivered in 2005 to 2014. We used multivariable Poisson regression models with robust error variance to examine the association between birth plurality (singleton vs. twin) and mortality outcomes within each GA, while adjusting for confounders. The results were presented as adjusted risk ratios (aRRs) with 95% confidence intervals (CIs). Results Of 26,292,747 live births, 96.6% were singletons and 3.4% were twins. At 29 to 36 weeks of GA, compared with singletons, twins had a lower risk of neonatal mortality (aRR: 0.37–0.78) and infant mortality (aRR: 0.54–0.86). When examined by GA, the three leading causes of neonatal and infant mortality varied between singletons and twins. Conclusion When stratified by GA, the risk of neonatal and infant mortality was lower at 29 to 36 weeks in twins than in singletons, though the cause of death varied.

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.

scholarly journals What does early initiation and duration of breastfeeding have to do with childhood mortality? Analysis of pooled population-based data in 35 sub-Saharan African countries

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Michael Ekholuenetale ◽  
Amadou Barrow
Keyword(s):  
Infant Mortality ◽  
Early Initiation ◽  
Childhood Mortality ◽  
Breastfeeding Initiation ◽  
African Countries ◽  
Live Births ◽  
Breastfeeding Practices ◽  
Sub Saharan ◽  
Duration Of Breastfeeding ◽  
Pooled Sample

Abstract Background Breastfeeding practices and their impact on infant health and survival are unquestionably of global interest. The aim of this study was to examine the link between breastfeeding initiation within one hour of birth, breastfeeding duration and childhood mortality in sub-Saharan Africa. Methods This study used data from the Demographic and Health Survey, which was conducted in 35 Sub-Saharan African countries between 2008 and 2017. Early initiation and duration of breastfeeding, food consumption indices, and infant mortality were all important variables. Analysis used percentage, median/interquartile range, and regression models (logistic, linear, Cox). Results Early initiation of breastfeeding within one hour after birth was lowest in Chad (23.0%) and highest in Burundi (85.0%). The pooled median duration of breastfeeding was 12 months. Female children had 3% significant lower odds of consuming tinned, powdered or fresh milk, compared with male children (OR 0.97; 95% CI 0.94, 0.99). Conversely, female children were more likely to be put to breast within one hour after birth, compared with male children (OR 1.03; 95% CI 1.01, 1.05). Results from the pooled sample showed approximately 20% (HR 0.80; 95% CI 0.67, 0.96) and 21% (HR 0.79; 95% CI 0.77, 0.80) reduction in infant mortality for children breastfed within one hour after birth and for every unit increase in the months of breastfeeding respectively. In addition, countries with the leading infant mortality rate include; Sierra Leone (92 deaths per 1000 live births), Chad (72 deaths per 1000 live births), Nigeria (69 deaths per 1000 live births), Cote d’ Ivoire (68 deaths per 1000 live births), Guinea (67 deaths per 1000 live births), Burkina-Faso (65 deaths per 1000 live births) and Mozambique (64 deaths per 1000 live births) respectively. Conclusions The findings from this study underscores the need for early breastfeeding initiation and prolong breastfeeding to be considered in programmes on improving childhood survival. Efforts should be made to improve optimal breastfeeding practices as only about half of children in the pooled sample had best practices of breastfeeding.

scholarly journals Features of machine learning in the study of the main factors of development of countries of the world

2021 ◽  
Vol 110 ◽  
pp. 02006
Author(s):  
Ludmila Borisova ◽  
Galina Zhukova ◽  
Anna Kuznetsova ◽  
Julie Martin
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Infant Mortality Rate ◽  
Live Births ◽  
Goods And Services ◽  
Machine Learning Methods ◽  
The World

The paper analyzes the socio-economic and demographic indicators of life expectancy in the countries of the world. Methods of regression analysis and machine learning are used. Statistically significant indicators that affect life expectancy around the world have been identified. When analyzing the data using machine learning methods, 13 of the 14 analyzed indicators were statistically significant. Significant indicators, in addition to those selected in the regression analysis, were 3: the under-five infant mortality rate (per 1,000 live births), the Net Barter Terms of Trade Index (2000 = 100), and Imports of goods and services (in % of GDP) (in the regression analysis, only the infant death rate was significant). In addition, it should be noted that there is a significant decrease in the under-five infant mortality rate (per 1,000 live births) for the EU, CIS and South-East Asian countries compared to the border set in the study for all countries: 4.65 vs. 34.9, a decrease in the birth rate from 2.785 to 1.85, a sharp increase in exports of goods and services: from 23.17 to 80.59, a halving in imports of goods and services, a drop in population growth from 2.105 to 0.85. The performed statistical analysis strongly supports the use of machine learning methods in identifying statistically significant relationships between various indicators that characterize the development of countries, if there are gaps in the data.

Changing Infant Mortality: The Need to Spend More While Getting Less

PEDIATRICS ◽  
1984 ◽  
Vol 73 (6) ◽  
pp. 862-866
Author(s):  
Bernard S. Bloom
Keyword(s):  
Infant Mortality ◽  
Medical Care ◽  
Medical Technology ◽  
Social Economic ◽  
High Technology ◽  
Live Births ◽  
Potential Benefits ◽  
Expectant Mothers ◽  
Unlimited Access ◽  
Low Levels

A steady decline of infant and maternal mortality has been recorded for as long as these statistics have been collected. Much of the improvement has been due to reductions in infectious diseases and to social, economic, and public health improvements over the years. The major portion of mortality reductions took place before there were any important effects of medical care. However, there is increasing evidence suggesting that important benefits, can still be gained from medical technology. But, with infant mortality at low levels (between 7 and 12/1,000 live births in high-income countries) the wide use of high technology to effect further reductions guarantees escalating medical care expenditures. With pressures mounting to control costs, what will society be willing to give up in order to make this care available? From where will funds come to utilize costly existing and new medical technology so that all expectant mothers and newborns needing it may have essentially unlimited access in order to reap potential benefits?

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