Sueño, calidad de vida y humor en profesionales de enfermería en unidades de cuidados intensivos infantiles

OBJETIVO Avaliar sono, qualidade de vida e humor em profissionais de enfermagem em Unidades de Terapia Intensiva Infantil. MÉTODO Estudo quantitativo, transversal e descritivo. Profissionais agrupados por turnos matutino, vespertino e noturno foram avaliados pelos instrumentos: Questionário de identificação Matutinidade-Vespertinidade; Índice de qualidade do sono de Pittsburgh; Escala de sonolência Epworth; Questionário genérico de avaliação de qualidade de vida - SF-36; Inventário de depressão de Beck; Inventário de ansiedade de Beck; Inventário de ansiedade Traço-Estado. RESULTADOS Amostra composta por 168 profissionais, predominando tipologia neutra, 57,49%. Não houve significância estatística quanto ao sono, apesar dos escores mostrarem qualidade ruim e sonolência diurna excessiva para os três turnos. A qualidade de vida não denotou diferença estatística, mas no domínio "aspecto social" do turno noturno, observou-se escore pior (pOBJECTIVE To assess sleep, quality of life and mood of nursing professionals of pediatric intensive care units. METHOD Quantitative, cross-sectional and descriptive study. Professionals grouped by morning, afternoon and evening shifts were assessed by means of the instruments: Morningness-Eveningness Questionnaire; Pittsburgh Sleep Quality Index; Epworth Sleepiness Scale; Generic questionnaire for the assessment of quality of life (SF-36); Beck Depression Inventory; Beck Anxiety Inventory; State-Trait Anxiety Inventory. RESULTS Sample consisted of 168 professionals, with prevalence of neutral typology (57.49%). There was no statistical significance regarding sleep, despite scores showing a poor quality of sleep and excessive daytime sleepiness for the three shifts. Quality of life did not reveal any statistical significance, but in the field "social role functioning" of the evening shift, a lower score was observed (pOBJETIVO Evaluar el sueño, la calidad de vida y el humor en profesionales de enfermería en Unidades de Cuidados Intensivos Infantiles. MÉTODO Estudio cuantitativo, transversal y descriptivo. Profesionales agrupados por turno matutino, vespertino y nocturno fueron evaluados por los instrumentos: Cuestionario de identificación Matutinidad-Vespertinidad; Índice de calidad del sueño de Pittsburgh; Escala de somnolencia de Epworth; Cuestionario genérico de evaluación de calidad de vida - SF-36; Inventario de depresión de Beck; Inventario de ansiedad de Beck; Inventario de ansiedad Trazo-Estado. RESULTADOS Muestra compuesta de 168 profesionales, predominando la tipología neutra en el 57,49%. No hubo significación estadística en cuanto al sueño, pese a que los puntajes muestren mala calidad y somnolencia diurna excesiva para los tres turnos. La calidad de vida no denotó diferencia estadística, pero en el dominio "aspecto social" del turno nocturno, se observó puntaje peor (

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

A meditação do yoga melhora a qualidade objetiva e subjetiva do sono dos profissionais de saúde

Study Objectives: Healthcare specialists are exposed to several occupational stressors that may contribute to a decreased quality of sleep and can negatively affect psychological functioning. We hypothesised that Yogic meditation may improve objective and subjective sleep quality of these professionals. Methods: 64 healthy professionals were enrolled in a clinical trial (age 35.8 years, 84.4% females, 51.1% physicians). Subjects were randomised into a 30-min Yogic meditation class was held weekly for eight weeks Meditation group (MG, n=32) or Control group (CG, n=32). Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), Polysomnography (PSG) were determined at baseline and after eight weeks. Results: Subjects in both groups had similar demographic characteristics. PSQI overall score was significantly lower (p=0.024) in the MG. Reported sleep latency had significant decreased (p=0.046) in the MG at eight weeks compared to baseline. MG had reported sleep latency significantly lower than the CG at eight weeks (p=0.028).The ESS did not show significant differences between the groups and no significant time effect. PSG showed a significant time effect (p=0.020) in decreasing the minutes of wake after sleep onset in the MG at time 2 compared to time 1. Heart rate decreased significantly (p= 0.001) in GM at eight weeks compared to baseline. The GM presented significantly lower heart rate than the CG at eight week (p= 0.035). Conclusion: Yogic meditation may be used as an integrative health tool to foster improvements in sleep related aspects of healthcare professionals.Objetivos do estudo: Especialistas em saúde estão expostos a vários estressores ocupacionais que podem contribuir para uma diminuição da qualidade do sono e podem afetar negativamente o funcionamento psicológico. Nossa hipótese é que a Meditação do Yoga pode melhorar a qualidade objetiva e subjetiva do sono desses profissionais.Métodos: 64 profissionais saudáveis foram incluídos em um ensaio clínico (idade 35,8 anos, 84,4% mulheres, 51,1% médicos). Os indivíduos foram aleatorizados sistematicamente para aulas de Meditação do Yoga de 30 minutos que foram realizadas semanalmente por oito semanas, sendo grupo de meditação (GM, n= 32) ou grupo controle (GC, n= 32). Os sujeitos responderam os questionários: Índice de Qualidade do Sono de Pittsburgh (PSQI) e a Escala de Sonolência de Epworth (ESS) e submetidos a polissonografia (PSG) no início e após oito semanas.Resultados: Os participantes de ambos os grupos apresentaram características demográficas semelhantes. O escore total do PSQI foi significativamente menor (p=0,024) no GM. A latência do sono relatada diminuiu significativamente (p=0,046) na GM em oito semanas em comparação com a linha de base. O GM relatou latência do sono significativamente menor que o GC às oito semanas (p= 0,028). O ESS não mostrou diferenças significativas entre os grupos e nenhum efeito de tempo significativo. PSG mostrou um efeito de tempo significativo (p= 0,020) na diminuição dos minutos de vigília após o início do sono no GM no tempo 2 em comparação com o tempo 1. A frequência cardíaca diminuiu significativamente (p= 0,001) no GM em oito semanas em comparação com a linha de base. O GM apresentou frequência cardíaca significativamente menor que o GC em oito semanas (p= 0,035). Conclusão: A Meditação do Yoga pode ser usada como ferramenta integrativa de saúde para promover melhorias nos aspectos relacionados ao sono dos profissionais de saúde.Dados abertos - Sucupira - Teses e dissertações (2019

Sleep, quality of life and mood of nursing professionals of pediatric intensive care units

Abstract OBJECTIVE To assess sleep, quality of life and mood of nursing professionals of pediatric intensive care units. METHOD Quantitative, cross-sectional and descriptive study. Professionals grouped by morning, afternoon and evening shifts were assessed by means of the instruments: Morningness-Eveningness Questionnaire; Pittsburgh Sleep Quality Index; Epworth Sleepiness Scale; Generic questionnaire for the assessment of quality of life (SF-36); Beck Depression Inventory; Beck Anxiety Inventory; State-Trait Anxiety Inventory. RESULTS Sample consisted of 168 professionals, with prevalence of neutral typology (57.49%). There was no statistical significance regarding sleep, despite scores showing a poor quality of sleep and excessive daytime sleepiness for the three shifts. Quality of life did not reveal any statistical significance, but in the field "social role functioning" of the evening shift, a lower score was observed (p<0.007). There was no statistical significance regarding levels of anxiety and depression. CONCLUSION The results suggest that these professionals may present sleeping problems, but they do not have lower scores of quality of life or mood disorders. Likely explanations for these findings may include an adaptation to their work type over time and the fact that working with children is rewarding

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundRegular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.MethodsThe Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.FindingsThe leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.InterpretationLong-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere