Ethical Performance of Neonatal Nurses in Neonatal Intensive Care Units

Introduction: Lack of adherence to the nursing code of ethics in neonatal wards is usually an issue in hospitals. The present study explored neonatal nurses’ adherence to the nursing code of ethics in the neonatal ward, using the Neonatal Nurses' Ethical Performance Self-Report Questionnaire and the Neonatal Nurses' Ethical Performance Observation Checklist.Methods: In this descriptive study, 132 Nurses from 5 neonatal wards from two pediatric hospitals in Tehran were recruited by census sampling.Results: The results showed that the mean score of the self-report questionnaire (86.12+10.34) and observational checklist (80.98+10.34) was within the desired performance range. The domain of improving the quality of care had the highest score (94.25+3.40) in the self-report questionnaire, and the domain of justice had the highest score (95.00+0) in the observational checklist. The domain of respect for autonomy had the lowest mean score in the self-report questionnaire (64.31+22.22) and the observational checklist (67.50+6.19). There was no significant relationship between the ethical performance score reported by nurses and nurses' demographic variables (P>0.05), and the specific condition of the neonates affected the ethical performance of nurses in neonates' demographic variables (P-value =0.002).Conclusions: The finding showed that, overall, the performance of neonatal nurses regarding ethical codes is desirable

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Skin Injuries and its Related Factors in the Neonatal Intensive Care Unit

Background: Skin is the most important defense mechanism of the neonate's body. The admission to the Neonatal Intensive Care Unit (NICU) is a risk factor for neonatal skin injuries. Therefore, to prevent these complications, it is essential to identify the risk factors. The present study aimed to investigate the incidence of skin injuries and its related factors in neonates admitted to the NICU.Methods: This cohort study was conducted in two NICUs in one perinatal hospital in Tehran, Iran, from January 2018 to June 2018. The sampling was performed using the census method. The data were collected through a demographic characteristics form, a risk factor assessment checklist, and the European Pressure Ulcer Advisory Panel (EPUAP) tool. The data were analyzed in SPSS software (version 19) through Fisher's exact test and chi-square test.Results: Out of 368 neonates, 126 cases had skin injuries, and the others were healthy. The mean values of weight and age of the neonates with skin injuries were 796.68±1606.82 g and 5.18±30.82 days, which was significantly lower than those of the infants without skin injury (p <0.05). The results of the risk factors analysis also showed that the second-grade injuries were the most frequent. Moreover, the drug leakage (14.2%, n=33) and nasal continuous positive airway pressure (12.06%, n=28) had the highest prevalence. The results of the effect of risk factors on the wound grade also showed that drug leakage, diaper rash, and surgical injuries had a significant effect on the wound grade.Conclusion: The results showed that in addition to neonatal conditions, equipment, and neonatal care play a significant role in the incidence of skin injuries. Skin is the most important defense barrier of the neonate's body and it is vitally important to take care of it. Therefore, it is necessary to identify and prevent such injuries

Comparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over 30 years old, who have participated in the screening of diabetes plan in Mashhad city that it was done by Mashhad University of Medical Sciences from October to December 2010. According to the fasting blood sugar criteria, 5414 individuals were identified as healthy and 1046 individuals were identified as pre-diabetic. Age, gender, body mass index, systolic blood pressure, diastolic blood pressure and waist-to-hip ratio were measured for every participant. The data was entered into the Microsoft Excel 2013 (Microsoft Corp., Redmond, WA, USA) and then analysis of the data was done in R Project for Statistical Computing, Version R 3.1.2 (www.r-project.org). Ordinary logistic regression model was fitted on the data. The outliers were identified. Then Mallow, WBY and BY robust logistic regression models were fitted on the data. And then, the robust models were compared with each other and with ordinary logistic regression model according to goodness of fit and prediction ability using Pearson's chi-square and area under the receiver operating characteristic (ROC) curve respectively. Results: Among the variables that were included in the ordinary logistic regression model and three robust logistic models, age, body mass index and systolic blood pressure were statistically significant (P 0.1). There were 552 outliers with misclassification error in the ordinary logistic regression model. Pearson's chi-square value and area under the ROC curve value in the Mallow model were almost the same as for ordinary logistic regression model. But it was relatively higher in BY and WBY models. Conclusion: Based on results of this study age, overweight and hypertension are risk factors of prediabetes. Also, WBY and BY models were better than ordinary logistic regression model, according to goodness of fit criteria and prediction ability

Cytomegalovirus Infection among Kidney Transplant Recipients One Year after Transplantation

Introduction: In spite of recent advances in therapeutic approaches, viral infection management continues to be a big challenge. Cytomegalovirus (CMV) infection is considered an important cause of high morbidity and mortality in kidney transplantations. In this study, we aimed to evaluate the frequency and risk factors of CMV infection in kidney recipients during the first year following kidney transplantation in Mashhad, North Eastern Iran. &nbsp; Materials & Methods: We retrospectively evaluated medical records of 247 patients undergoing kidney transplantation in Montaserie Organ Transplantation Hospital of Mashhad city during 2012 - 2015 .The patients were followed for 1-year. The data were reported by descriptive statistics (mean and standard deviation) and viral infection associated risk factors were analyzed by Logistic regression. &nbsp; Findings: During the first year following transplantation, 64 (21.9%) episodes of CMV infections were found. 28 episodes (51.8%) occurred in the first 3 months following the transplantation, 17 (31.5%) during the second 3 months, and 9 (16.7%) after the sixth month. Among the evaluated risk factors for CMV infection (gender, age, medical comorbidities and causes of renal failure), diabetes was the only statistically significant factor identified as a cause for comorbidity and renal failure, (P value<0.05). &nbsp; Discussion & Conclusion: According to our results and conclusions, allograft kidney recipients were more sensitive to develop active CMV. Appropriate prophylaxis intake, vaccination for CMV and post-transplant viral monitoring are suggested to decrease the possibility of infections in kidney recipients.

Determination of cephalic index and shape of head in adult female students in Birjand city during 2013

Background and Aim: Cephalometery, as one of the most significant section of anthropometry examines the facial and cephalic indeces. Since the measurements are affected by geographical factors, gender, age, and race, the obtained results are based on the community under study. Regarding lack of investigations on cephalometry in Birjand, the present study aimed at determining cephalic indexes and standard classification of neurocranium in 15-17 year old female students in the city in 2013. Materials and Methods: This descriptive cross-sectional study was conducted on 437 healthy female 15-17 year old students in Birjand during 2013, who were selected through cluster sampling. Then, cephalic indices of the subjects were determined using standard Martin Scale Cephalometer, according to Gray's Anatomy Cephalometric Method. The obtained data were collected according were analyzed using SPSS soft ware (V:13). Results: Mean maximum head length and breadth in the subjects were found to be 177.6±7.54mm and 141.6±5.84mm, respectively. Total neurocranium index was determined 79.88±4.64 mm. The head shape of 43% of the individuals was mesocephalic, %33/4 brachycephalic, %12/12 hyperbrachycephalic and %11/44 dolichocephalic. Conclusion: The present study on cephalometry revealed that adult female students in Birjand city mostly have a mesocephalic phenotype

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic