Low-Tech Innovations to Prevent Neonatal Mortality: Perspectives from Public Health, Neonatology, and Biomedical Engineering

2020 ◽  
pp. 113-126
Author(s):  
Pavani Kalluri Ram ◽  
Sara K. Berkelhamer ◽  
Anirban Dutta
Keyword(s):  
Public Health ◽  
Neonatal Mortality ◽  
Biomedical Engineering

PP159 Telemedicine In Paraguay: Contributions Of The Institute Of Health Sciences Research, National University Of Asunción

2020 ◽  
Vol 36 (S1) ◽  
pp. 18-18
Author(s):  
Ronald Rivas ◽  
Pedro Galván
Keyword(s):  
Public Health ◽  
Biomedical Engineering ◽  
List Type ◽  
Telemedicine System ◽  
Cellular Telephony ◽  
Imaging Department ◽  
Number Of Patients ◽  
Computed Tomography Scans ◽  
National University ◽  
Development And Validation

IntroductionThe modalities of telemedicine that have been developed and applied so far by the Department of Biomedical Engineering and Imaging at the National University of Asunción (IICS-UNA) are as follows: (i) telediagnosis: the remote sending of data, signals, and images for diagnostic purposes; (ii) general telediagnostic imaging; (iii) telemonitoring (including telemetry): remote monitoring of vital parameters to provide automatic or semi-automatic surveillance or alarm services in emergencies, epidemiology, or tele-public health; and (iv) tele-education: the use of telematic networks to provide virtual platforms for educating and training health professionals.MethodsWe conducted a comprehensive review of the scientific works developed by the IICS-UNA in order to evaluate the systematic implementation of Telemedicine in Paraguay. Documents, pilot projects (satellite telegraphy), telediagnostic research, telematics, tele-education, published articles, and statistical data (number of patients attending or studies performed, etcetera) relating to the implementation of the National Telemedicine System by the Ministry of Public Health and Social Welfare since 1999 were reviewed.ResultsImplementation of the telemedicine system has meant that 472,038 patients have attended referral centers nationwide, with 297,999 electrocardiographs, 165,323 computed tomography scans, and 8,697 electroencephalograms being conducted. Projects developed within the framework of the Telemedicine Research Line have included the following: (i)Development and validation of a clinical telemicroscopy system based on cellular telephony;(ii)Implementation of a telemetry system for temperature monitoring of the collection of biological samples from a biomedical research center; and(iii)Production and development of a virtual campus at the National University of Asunción.ConclusionsGiven the current healthcare environment, developing a line of research based on telemedicine is a proactive step, since telemedicine provides an alternative solution to the problem of access to the health system. That is why the IICS-UNA Biomedical Engineering and Imaging Department has developed telemedicine as one of its main lines of research.

scholarly journals NeMoR: a New Method Based on Data-Driven for Neonatal Mortality Rate Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Natália Martins Arruda ◽  
Cátia Sepetauskas ◽  
Everton Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Neonatal Mortality ◽  
Regression Models ◽  
Mortality Rates ◽  
Data Driven ◽  
Health Policies ◽  
Neonatal Mortality Rate ◽  
Policy Makers ◽  
Public Health Policies

ABSTRACTReduction in child mortality is one of the United Nations Sustainable Development Goals for 2030. In Brazil, despite recent reduction in child mortality in the last decades, the neonatal mortality is a persistent problem and it is associated with the quality of prenatal, childbirth care and social-environmental factors. In a proper health system, the effect of some of these factors could be minimized by the appropriate number of newborn intensive care units, number of health care units, number of neonatal incubators and even by the correct level of instruction of mothers, which can lead to a proper care along the prenatal period. With the intent of providing knowledge resources for planning public health policies focused on neonatal mortality reduction, we propose a new data-driven machine leaning method for Neonatal Mortality Rate forecasting called NeMoR, which predicts neonatal mortality rates for 4 months ahead, using NeoDeathForecast, a monthly base time series dataset composed by these factors and by neonatal mortality rates history (2006-2016), having 57,816 samples, for all 438 Brazilian administrative health regions. In order to build the model, Extra-Tree, XGBoost Regressor, Gradient Boosting Regressor and Lasso machine learning regression models were evaluated and a hyperparameters search was also performed as a fine tune step. The method has been validated using São Paulo city data, mainly because of data quality. On the better configuration the method predicted the neonatal mortality rates with a Mean Square Error lower than 0.18. Besides that, the forecast results may be useful as it provides a way for policy makers to anticipate trends on neonatal mortality rates curves, an important resource for planning public health policies.Graphical AbstractHighlightsProposition of a new data-driven approach for neonatal mortality rate forecast, which provides a way for policy-makers to anticipate trends on neonatal mortality rates curves, making a better planning of health policies focused on NMR reduction possible;a method for NMR forecasting with a MSE lower than 0.18;an extensive evaluation of different Machine Learning (ML) regression models, as well as hyperparameters search, which accounts for the last stage in NeMoR;a new time series database for NMR prediction problems;a new features projection space for NMR forecasting problems, which considerably reduces errors in NRM prediction.

scholarly journals Management of Blood Sugar Degrees in Hyperglycemia in Pregnancy (Hip) Reduces Perinatal, Infant Morbidity & Mortality as a Result of a Large Prospective Cohort Learn From Up, India

2018 ◽  
Vol 3 (4) ◽  
Keyword(s):  
Public Health ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Blood Sugar ◽  
Neonatal Mortality ◽  
Perinatal Mortality ◽  
National Health ◽  
Uttar Pradesh ◽  
Sugar Levels ◽  
In Pregnancy

Background: Gestational diabetes mellitus (GDM) is a glucose intolerance that occurs or is identified for the first time all through pregnancy. Perinatal & Neonatal morbidity mortality is significant in pregnant women in GDM with extra hazard of growing diabetes later in life. Uttar Pradesh is a largest state of India with one of the highest rate of the infant as well as maternal mortality which might be, at least partially due to GDM. Thus, Careful evaluation, administration & Training of HCPs for GDM can improve the outcomes in National health Mission supported Govt funded Program, supported by World Diabetes foundation, Denmark. Aims & Objectives: Primary objective of this study to be determine the Maternal-Fetal outcomes of GDM and management of Hyperglycemia in Pregnancy HIP reduces Neonatal & Perinatal Mortality as per the NHM, GOI Guidelines for GDM, As this will go long way help us in reduction of Perinatal & infant mortality. Thus, this study was once undertaken to recognize the extent of the burden on the healthcare and formulating further policy for Implementation of Gestational Diabetes Program in the largest state of Uttar Pradesh. Materials and Methods: A prospective cohort study was done for 2 year from October 1, 2016, to September 31, 2018, at 828 GDM screening units as a part of the Gestational Diabetes Prevention and Control Project, Uttar Pradesh approved by the Indian Government in the state of Uttar Pradesh, India, largest state with second Highest MMR & IMR, A total of 515,532 pregnant women were screened during their 16–32th weeks of pregnancy by impaired oral glucose test (OGTT) as per NHM Guidelines for GDM, 12784 GDM & 7287 Non GDM maternal and perinatal outcomes were followed up in both GDM and non‑GDM categories in the 2 year (2016-2018) after blood sugar management (September 2016-October 2018) was executed at 828 (DHs, CHCs & PHCs healthcare) facilities, 515532 Pregnant Women have been screened at 16-20 Weeks & 24th-28 weeks of pregnancy as per Guidelines of National health Mission, GOI Guideline. Results: Perinatal mortality increased significantly from 2.6% to 9.1% when blood sugar levels increased from 120 mg/dl to 199 mg/dl and above. Perinatal mortality in GDM cases were significantly to the control of blood sugar levels (P < 0.0001). Relative Risk of Stillbirth, Perinatal & neonatal mortality have been respectively 2.5, 2.3 & 2.5 times greater in GDM compare Non GDM (Table 1). Most of the GDM used to be identified in primigravida (52%). It was also found in our study those GDM who were strictly controlled with Hyperglycemia in pregnancy (HIP) to <120 mg/dl, Post Prandial blood sugar, have lowest risk for perinatal and neonatal mortality compare to those GDM pregnant women Blood sugars were not controlled, Risk for Perinatal mortality increases steadily and reaches 9.1% beyond blood sugar> 200 mg/dl. Conclusion: All the Pregnant women need screening in Public health facilities & Implementation of National health Mission, GOI Guidelines for GDM has to be followed to improve outcome for Mother and Newborn, As the lack of information about GDM amongst pregnant women is high, to decrease the risk, increase awareness & full Implementation of NHM GDM Guidelines is key to Perinatal and neonatal mortality reduction in Public health care facilities where large number of ANC visit for Maternal and fetal health care.

scholarly journals Incidence of neonatal mortality and its predictors among live births in Ethiopia: Gompertz gamma shared frailty model

2020 ◽  
Vol 46 (1) ◽  
Author(s):  
Zemenu Tadesse Tessema ◽  
Getayeneh Antehunegn Tesema
Keyword(s):  
Public Health ◽  
Antenatal Care ◽  
Neonatal Mortality ◽  
Frailty Model ◽  
Live Births ◽  
Large Size ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
Male Sex ◽  
Size At Birth

Abstract Background Neonatal mortality remains a serious public health concern in developing countries including Ethiopia. Ethiopia is one of the countries with the highest neonatal mortality in Africa. However, there is limited evidence on the incidence and predictors of neonatal mortality at the national level. Therefore, this study aimed to investigate the incidence of neonatal mortality and its predictors among live births in Ethiopia. Investigating the incidence and predictors of neonatal mortality is essential to design targeted public health interventions to reduce neonatal mortality. Methods A secondary data analysis was conducted based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 11,022 live births was included in the analysis. The shared frailty model was applied since the EDHS data has hierarchical nature, and neonates are nested within-cluster, and this could violate the independent and equal variance assumption. For checking the proportional hazard assumption, Schoenfeld residual test was applied. Akakie Information Criteria (AIC), Cox-Snell residual test, and deviance were used for checking model adequacy and for model comparison. Gompertz gamma shared frailty model was the best-fitted model for this data since it had the lowest deviance, AIC value, and the Cox-Snell residual graph closet to the bisector. Variables with a p-value of less than 0.2 were considered for the multivariable Gompertz gamma shared frailty model. In the multivariable Gompertez gamma shared frailty model, the Adjusted Hazard Ratio (AHR) with a 95% confidence interval (CI) was reported to identify significant predictors of neonatal mortality. Results Overall, the neonatal mortality rate in Ethiopia was 29.1 (95% CI: 26.1, 32.4) per 1000 live births. In the multivariable Gompertz gamma shared frailty model; male sex (AHR = 1.92, 95% CI: 1.52, 2.43), twin birth (AHR = 5.22, 95% CI: 3.62, 7.53), preceding birth interval less than 18 months (AHR = 2.07, 95% CI: 1.51, 2.85), small size at birth (AHR = 1.64, 95% CI: 1.24, 2.16), large size at birth (AHR = 1.53, 95% CI: 1.16, 2.01) and did not have Antenatal Care (ANC) visit (AHR = 2.10, 95% CI: 1.44, 3.06) were the significant predictors of neonatal mortality. Conclusion Our study found that neonatal mortality remains a public health problem in Ethiopia. Shorter birth interval, small and large size at birth, ANC visits, male sex, and twin births were significant predictors of neonatal mortality. These results suggest that public health programs that increase antenatal care service utilization should be designed to reduce neonatal mortality and special attention should be given for twin births, large and low birth weight babies. Besides, providing family planning services for mothers to increase birth intervals and improving accessibility and utilization of maternal health care services such as ANC is crucial to improve neonatal survival.

scholarly journals Machine Learning to Predict Neonatal Mortality Using Public Health Data from São Paulo - Brazil

2020 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Everton Silva ◽  
Rodrigo Bresan ◽  
Natália Arruda ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Infant Mortality ◽  
Neonatal Mortality ◽  
Sao Paulo ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
São Paulo ◽  
Health Quality ◽  
Health Features

AbstractInfant mortality is one of the most important socioeconomic and health quality indicators in the world. In Brazil, neonatal mortality accounts to 70% of the infant mortality. Despite its importance, neonatal mortality shows increasing signals, which causes concerns about the necessity of efficient and effective methods able to help reducing it. In this paper a new approach is proposed to classify newborns that may be susceptible to neonatal mortality by applying supervised machine learning methods on public health features. The approach is evaluated in a sample of 15,858 records extracted from SPNeoDeath dataset, which were created on this paper, from SINASC and SIM databases from São Paulo city (Brazil) for this paper intent. As a results an average AUC of 0.96 was achieved in classifying samples as susceptible to death or not with SVM, XGBoost, Logistic Regression and Random Forests machine learning algorithms. Furthermore the SHAP method was used to understand the features that mostly influenced the algorithms output.

scholarly journals Determinants of neonatal mortality in Ethiopia: an analysis of the 2016 Ethiopia Demographic and Health Survey

2020 ◽  
Vol 20 (2) ◽  
pp. 715-723
Author(s):  
Garoma Wakjira Basha ◽  
Ashenafi Abate Woya ◽  
Abay Kassa Tekile
Keyword(s):  
Public Health ◽  
Logistic Regression ◽  
Neonatal Mortality ◽  
Health Survey ◽  
Odds Ratio ◽  
Public Health Intervention ◽  
Health Intervention ◽  
Demographic And Health Survey ◽  
Sampling Weights ◽  
Child Birth

Background: The first 28 days of life, the neonatal period, are the most vulnerable time for a child’s survival. Neonatal mortality accounts for about 38% of under-five deaths in low and middle income countries. This study aimed to identify the determinants of neonatal mortality in Ethiopia. Methods: The study used data from the nationally representative 2016 Ethiopia Demographic and Health Survey (EDHS). Once the data were extracted; editing, coding and cleaning were done by using SAS 9.4.Sampling weights was applied to en- sure the representativeness of the sample in this study. Both bivariate and multivariable logistic regression statistical analysis was used to identify determinants of neonatal mortality in Ethiopia. Results: A total of 11,023 weighted live-born neonates born within five years preceding the 2016 EDHS were included this in this study. Multiple logistic regression analysis showed that multiple birth neonates (Adjusted Odds Ratio (AOR)=6.38;95%- Confidence Interval (CI):4.42-9.21), large birth size (AOR=1.35; 95% CI: 0.28-1.62), neonates born to mothers who did not utilize ANC (AOR=1.41; 95% CI: 1.11-1.81), neonates from rural area (AOR=1.88; 95% CI: 1.15-3.05) and neonates born in Harari region (AOR=1.45; 95% CI: 0.61-3.45)had higher odds of neonatal mortality. On the other hand, female neonates (AOR=0.60; 95% CI: 0.47-0.75), neonates born within the interval of more than 36 months of the preceding birth (AOR=0.56; 95% CI: 0.43-0.75), neonates born to fathers with secondary and higher education level (AOR=0.51; 95%CI: 0.22-0.88) had lower odds of neonatal mortality in Ethiopia. Conclusion: To reduce neonatal mortality in Ethiopia, there is a need to implement sex specific public health intervention mainly focusing on male neonate during pregnancy, child birth and postnatal period. A relatively simple and cost-effective public health intervention should be implemented to make sure that all pregnant women are screened for multiple pregnancy and if positive, extra care should be given during pregnancy, child birth and postnatal. Keywords: Neonatal mortality; logistic regression; odds ratio; Ethiopia.

scholarly journals Neonatal mortality in Nepal: A Public Health Concern

2018 ◽  
Vol 10 ◽  
pp. 37-38
Author(s):  
Hari Krishna Bhattarai
Keyword(s):  
Public Health ◽  
Neonatal Mortality ◽  
Health Concern ◽  
Public Health Concern

DOI: http://dx.doi.org/10.3126/hprospect.v10i0.5648Health Prospect Vol.10 2011, pp.37-38

Low Birth Weight and Neonatal Mortality: Meta Analysis

2020 ◽  
Author(s):  
Silvalia Rahma Pratiwi ◽  
◽  
Hanung Prasetya ◽  
Bhisma Murti ◽  
◽  
...  
Keyword(s):  
Public Health ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Neonatal Mortality ◽  
Neonatal Death ◽  
Meta Analysis ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Neonatal Deaths ◽  
Central Java

ABSTRACT Background: Low birth weight (LBW) has been used as an important public health indicator. LBW is one of the key drivers and indirect causes of neonatal death. It contributes to 60% to 80% of all neonatal deaths, annually. This study aimed to examine association between LBW and neonatal mortality using meta analysis. Subjects and Methods: This was meta-analysis and systematic review. Published articles in 2010-2020 were collected from Google Scholar, PubMed, Springer Link, Hindawi, Clinical Key, ProQuest databases. Keywords used “low birth weight” AND “mortality” OR “birth weight mortality” OR “neonatal death” AND “cross sectional” AND “adjusted odd ratio”. The inclusion criteria were full text, using cross-sectional study design, and reporting adjusted ratio. The data were analyzed by PRISMA flow chart and Revman 5.3. Results: 6 studies were met criteria. This study showed that low birth weight increased the risk of neonatal mortality (aOR= 2.23; 95% CI= 1.12 to 4.44; p= 0.02). Conclusion: Low birth weight increases the risk of neonatal mortality. Keywords: low birth weight, mortality, neonatal death Correspondence: Silvalia Rahma Pratiwi. Masters Program in Public Health. Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: [email protected]. Mobile: 082324820288. DOI: https://doi.org/10.26911/the7thicph.03.113

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