Outcomes of Patient Education in Nurse-led Clinics: A Systematic Review

Introduction: Patient education is an independent role of nurses performed in nurse-led clinics (NLCs). The measurement of patient education outcomes validates whether nursing educational interventions have a positive effect on patients, which helps determine whether changes in care are needed. Standardized nursing terminologies facilitate the evaluation of educational outcomes. We aimed to explore the outcomes of patient education in NLCs based on the Nursing Outcome Classification (NOC) system. Methods: The review was conducted according to PRISMA guidelines. We searched "Medline", "Embase", "Web of Science", and "Scopus" databases for articles published between 2000 and 2022. Based on the search strategy, 1157 articles were retrieved from PubMed, Scopus, Web of Science, and Embase databases. After excluding the duplicates, 978 articles were appraised. 133 articles remained after reading the titles and abstracts of the articles. In the next step, the articles were evaluated regarding methodology, research population, and exclusion criteria, after which 112 articles were omitted, and finally, 21 articles were included in the full-text review. We assessed all included studies using the Quality Assessment of Controlled Intervention Studies checklist. Results: A total of 21 randomized controlled trials met the inclusion criteria. "Physiologic health", "functional health", "psychosocial health", "health knowledge and behavior", and "perceived health" were the domains of nursing outcomes investigated as Patient Education Outcomes in NLCs. Conclusion: Most of the outcomes were linked to lifestyle-related chronic diseases and, further studies are needed to determine the effects of patient education provided in NLCs in terms of family/society health outcomes

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

Design and Psychometric Evaluation of Coping Scale in Recipients of Kidney Transplant

Background: Although there are different tools in a coping context, lots of them are general and not applicable in every stressful situation such as transplant. Aim: The aim of this study is to develop and psychometrically evaluate a coping tool with kidney transplant in the Iranian context and culture. Method: In this sequential exploratory study, based on theoretical and practical definitions of constructs for the concept of coping, the initial pool was extracted with 93 items. Face and content validity qualitative and quantitative were calculated. In order to assess the construct validity, exploratory factor analysis was applied. Using Cronbach's alpha and retesting, the consistency of the questionnaire was calculated Results: In the quantitative face validity, all the items whose item impact was more than 1.5 were retained. Seven items were merged during the qualitative content validity since they overlapped each other, making the number of items equal to 80 at this stage. The quantitative content validity was determined by calculating the content validity index (CVI) as 0.9 and factor analysis was performed for all the 80 items. The items decreased to 69 using factor analysis and were classified under 5 categories of understanding the necessity of self-care, intelligent acceptance of changes, conscious enduring of problems, understanding supportive encouragements and spiritual enduring. Finally, the reliability of the questionnaire equaled 0.94 using Cronbach alpha. Implications for Practice: This tool, with understanding and careful testing of the coping degree of transplant patients, could help health service providers to present their services and play their preventive, caring and therapeutic roles to patients

Effect of a self-management empowerment program on anger and social isolation of mothers of children with cerebral palsy: A randomized controlled clinical trial

Background: Cerebral palsy (CP) is the most common chronic motor disability, which can have negative impacts on social behavior of mothers as the primary caregivers. Aim: To investigate the effect of an intervention program based on the self-management empowerment model on the anger and social isolation of mothers with CP children. Method: This randomized controlled clinical trial was performed on 72 mothers of CP in Bushehr and Shiraz, Iran, 2015. We employed Buss-Perry Aggression Questionnaire and the UCLA Loneliness Scale for data collection. The intervention group received self-management empowerment in five steps and two face-to-face sessions and was followed up regularly for 1.5 months. Thereafter, anger and social isolation of both groups were reexamined. Data analysis was performed using the Chi-square test, independent t-test, paired t-test, and Mann-Whitney test in SPSS, version 18. Results: The mean ages of the intervention and control groups were respectively 28.1±6.09 and 28.1±5.8 years, which independent t-test showed to be homogenous (P=0.31). Before the intervention, there was no significant difference between the two groups in terms of aggression (P=0.58) and loneliness scores (P=0.93); however, after the intervention, independent t-test reflected significant inter-group differences in terms of both scores (P<0.001). Implications for Practice: The designed empowerment program could reduce the anger of mothers of CP children and serve as a framework for empowering these mothers in healthcare and rehabilitation centers. The efficacy of the program in alleviating the social isolation of these mothers requires further research

The association between a Fatty Acid Binding Protein 1 (FABP1) gene polymorphism and serum lipid abnormalities in the MASHAD cohort study

Introduction: Dyslipidemia is a known risk factor for cardiovascular disease and is partially determined by genetic variations in the genes involved in lipoprotein metabolism. Therefore, we aimed to assess the association between a polymorphism of the Fatty Acid Binding Protein1 (rs2241883) gene locus and dyslipidemia in an Iranian cohort. Materials and methods: This is a case-control study 2737 individuals were recruited (2203 subjects with dyslipidemia and 534 controls). Dyslipidemia was defined as total cholesterol�200 mg/dl, or TG�150 mg/dl, or LDL-C�130 mg/dl, or HDL-C<40 mg/dl in males and <50 mg/dl in females. Serum lipid profile was determined using a Alcyon Abbott biochemical auto analyzer, USA. Genotyping was made through double amplification refractory mutation system polymerase chain reaction (ARMs PCR). Result: The frequency of TT, CT, CC genotypes of rs2241883 polymorphism of FABP1 gene were 65.5, 33.4, 5.1 in subjects with dyslipidemia and 56.9, 40.4, 2.6 in subjects without dyslipidemia, respectively. Using a dominant genetic model, subjects carrying C allele (CC&CT genotypes) had a 22 lower risk of dyslipidemia (OR: 0.78, CI 95: 0.62-0.98 P, 0.03). Individuals with CT vs. TT genotypes had a significantly lower risk of a high serum TC and LDL level. Further analysis showed that there was a positive association between FABP1 genotype (CT) and isolated HTG as well as combined dyslipidemia. The change of a polar amino acid (threonine) in position T94A to a hydrophobic amino acid (alanine) can cause transformation protein. Conclusions: A CC genotype of the rs2241883 polymorphism of the FABP1 gene appears to confer a higher risk of dyslipidemia in our representative cohort of Iranian individuals

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