Light acupuncture and five-element music therapy for nurses\u27 mental health and well-being during and post-COVID-19: protocol for a randomised cross-over feasibility study

INTRODUCTION: Australian nurses have experienced higher levels of anxiety during the COVID-19 pandemic compared with the prepandemic. This may have affected their long-term mental health and intention to stay in the profession resulting in a workforce shortage, which further impacts the health of the public. Management is urgently required to improve nurses\u27 well-being. However, there is limited evidence available. The proposed clinical trial aims to evaluate the feasibility and therapeutic effects of using a combination of light acupuncture and five-element music therapy to improve nurses\u27 mental health and well-being during and post-COVID-19. METHODS AND ANALYSIS: This randomised, single blinding, two-arm cross-over feasibility study involves a 1-week run-in period, 2-week intervention and 1-week run-in period in between interventions. Thirty-six eligible nurses will be recruited from the community and randomised into either a combination of light acupuncture treatment and five-element music therapy group or no treatment group for 2 weeks. After a 1-week run in period, they will be swapped to the different group. The primary outcome of this study is to evaluate the feasibility of a combination of light acupuncture treatment and five-element music therapy to improve nurses\u27 mental health and well-being. The secondary outcomes will include anxiety and depression, work productivity and activity, and quality of life assessments. Participants will be asked to complete a set of online questionnaires throughout the trial period. All analyses will be performed in R Studio V.1.1.463. ETHICS AND DISSEMINATION: Ethical approval was attained from Edith Cowan University\u27s Human Research Ethics Committee (No. 2021-02728-WANG). Research findings will be shared with hospitals and in various forms to engage broader audiences, including national and international conferences, presentations, open-access peer-reviewed journal publications, and local community workshop dissemination with healthcare professionals. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry: ACTRN12621000957897p https://www.anzctr.org.au/ACTRN12621000957897p.aspx

Suivi spatiotemporel de l’érosivité des pluies au Maroc à l’aide des données satellitaires libres

This study aims particularly to overcome the lack of rainfall measurements and to demonstrate the usefulness of open satellite data rainfall-erosivity estimation in Morocco. For this purpose, a short time series of two satellite products, namely CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission). were used and correlated to in-situ measurements of a period of 17 years (2000- 2016). This analysis revealed a relatively better correlation between monthly CHIRPS product and in-situ measurements. the coefficient of determination is arround 0.81. After its calibration using regression equations, this product were used to estimate the rainfall-erosivity over Morocco according to Renard and Freimund equation. The result showed a geographical disparity and an annually weak evolution of erosivity. Likewise, the study pointed out a significant difference in the estimated erosivity across seasons. This imply a reduction of 3% in summer and 15% in spring and a remarkable increase of 33% in autumn and 39% in winter. The prominent change of the seasonality of rainfall erosivity is very significant in the course of agricultural practices’ evolution and the adoption of adequate measures of soil protection. Keywords: Erosion, erosivity, rainfall, modelling, CHIRPS, TRMM, MoroccoLa présente étude a pour objectif principal de répondre à un besoin pressant des données pluviométriques à l’aide des données satellitaires libres et par conséquent, faciliter les prises de décision pour les gestionnaires des ressources naturelles. Par le biais des séries chronologiques couvrant la période 2000 - 2016, le suivi spatiotemporel de l’érosivité des pluies a été étudié. Les produits satellitaires CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) et TRMM (Tropical Rainfall Measuring Mission) ont été choisis pour suivre l’évolution de l’érosivité des pluies. Les études de corrélations effectuées entre les données satellitaires et les données pluviométriques mesurées ont montré que les données CHIRPS sont les mieux corrélées. La validation de ces résultats a donné un coefficient de détermination de 0,81. Les données retenues ont été ensuite calibrées avec une équation de régression. Le choix de la formule de Renard et Freimund comme modèle de calcul du facteur d’érosivité a montré une forte disparité géographique et une faible évolution de l’érosivité à l’échelle annuelle. De même, elle fait ressortir une très forte évolution de celle-ci selon les saisons avec une diminution de 3% en été et de 24% au printemps et une augmentation marquée de celle-ci pendant l’automne (33%) et l’hiver (39%). Le changement prononcé de la saisonnalité de l’érosivité des pluies est très déterminant dans l’orientation de l’évolution des pratiques agricoles et le choix des mesures adéquates de protection des sols. Mots-clés : érosion, érosivité, précipitation, modélisation, CHIRPS, TRMM, Maro

Measuring the health-related Sustainable Development Goals in 188 countries : a baseline analysis from the Global Burden of Disease Study 2015

Background In September, 2015, the UN General Assembly established the Sustainable Development Goals (SDGs). The SDGs specify 17 universal goals, 169 targets, and 230 indicators leading up to 2030. We provide an analysis of 33 health-related SDG indicators based on the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015). Methods We applied statistical methods to systematically compiled data to estimate the performance of 33 health-related SDG indicators for 188 countries from 1990 to 2015. We rescaled each indicator on a scale from 0 (worst observed value between 1990 and 2015) to 100 (best observed). Indices representing all 33 health-related SDG indicators (health-related SDG index), health-related SDG indicators included in the Millennium Development Goals (MDG index), and health-related indicators not included in the MDGs (non-MDG index) were computed as the geometric mean of the rescaled indicators by SDG target. We used spline regressions to examine the relations between the Socio-demographic Index (SDI, a summary measure based on average income per person, educational attainment, and total fertility rate) and each of the health-related SDG indicators and indices. Findings In 2015, the median health-related SDG index was 59.3 (95% uncertainty interval 56.8-61.8) and varied widely by country, ranging from 85.5 (84.2-86.5) in Iceland to 20.4 (15.4-24.9) in Central African Republic. SDI was a good predictor of the health-related SDG index (r(2) = 0.88) and the MDG index (r(2) = 0.2), whereas the non-MDG index had a weaker relation with SDI (r(2) = 0.79). Between 2000 and 2015, the health-related SDG index improved by a median of 7.9 (IQR 5.0-10.4), and gains on the MDG index (a median change of 10.0 [6.7-13.1]) exceeded that of the non-MDG index (a median change of 5.5 [2.1-8.9]). Since 2000, pronounced progress occurred for indicators such as met need with modern contraception, under-5 mortality, and neonatal mortality, as well as the indicator for universal health coverage tracer interventions. Moderate improvements were found for indicators such as HIV and tuberculosis incidence, minimal changes for hepatitis B incidence took place, and childhood overweight considerably worsened. Interpretation GBD provides an independent, comparable avenue for monitoring progress towards the health-related SDGs. Our analysis not only highlights the importance of income, education, and fertility as drivers of health improvement but also emphasises that investments in these areas alone will not be sufficient. Although considerable progress on the health-related MDG indicators has been made, these gains will need to be sustained and, in many cases, accelerated to achieve the ambitious SDG targets. The minimal improvement in or worsening of health-related indicators beyond the MDGs highlight the need for additional resources to effectively address the expanded scope of the health-related SDGs.Peer reviewe

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61.7 years (95% uncertainty interval 61.4-61.9) in 1980 to 71.8 years (71.5-72.2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11.3 years (3.7-17.4), to 62.6 years (56.5-70.2). Total deaths increased by 4.1% (2.6-5.6) from 2005 to 2015, rising to 55.8 million (54.9 million to 56.6 million) in 2015, but age-standardised death rates fell by 17.0% (15.8-18.1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14.1% (12.6-16.0) to 39.8 million (39.2 million to 40.5 million) in 2015, whereas age-standardised rates decreased by 13.1% (11.9-14.3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42.1%, 39.1-44.6), malaria (43.1%, 34.7-51.8), neonatal preterm birth complications (29.8%, 24.8-34.9), and maternal disorders (29.1%, 19.3-37.1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Oral hygiene teaching in clinical activities at the department of dentistry of Dakar (Senegal)

The aim of this study was to assess the attitudes and practices of students related to oral hygiene teaching by mean of a questionnaire submitted to patients attending the clinics of the Department of Dentistry of Dakar.Method: A KPC study (Knowledge, Practices and Coverage) focusing on dental students was conducted and consists on a interview of 295 patients. The socio-demographic and brushing method variables involved the patients. While those related to attitudes and practices regarding oral hygiene teaching concerned only students. The tests at the univariate and multivariate analysis by logistic regression were significant when P . 0.05.Results: The study population consisted of 48.5% of men, 76.6% were adults. 32.9% of patients came from the Conservative Dentistry and Endodontic (CDE) clinic and 5.4% of them from Orthodontic clinic. For 52.2% of patients, no prophylactic measure was laid and the link with the proper brushing technique of patients was significant (OR = 4.4). Using supports at time of oral hygiene teaching was respected for 17.4% of cases. It was associated with proper brushing technique even after adjustment.Conclusion: The attitudes and practices of students in Dentistry Department of Dakar in relation to oral hygiene teaching deserves more vigilant in watching over the prophylactic care and using supports during oral hygiene teaching

Remote Sensing-Based Assessment of Dry-Season Forage Quality for Improved Rangeland Management in Sahelian Ecosystems

International audienceResidents of the Sahel depend on livestock, but harsh environmental conditions during the dry season limit rangeland forage, which is the main source of livestock feed. Al-though operational tools exist for assessing and monitoring forage quantity during the dry season, assessments of forage quality are lacking. We addressed this gap by developing satellite-based monitoring of forage quality across Sahelian rangelands during the dry season. Acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP) content (%) were measured in forage samples collected from 11 sites across the Senegalese rangelands in 2021. Multilinear (MML) regression and support vector machine (SVM) models were calibrated with spectral indices to estimate these parameters of forage quality. The vegetation variables assessed were herbaceous mass (HQ), woody foliage mass (LQ), and total fo-rage mass (HLQ). The MML regression provided the most accurate estimates for CP (HQ: R2 = 0.81, LQ: R2 = 0.72, and HLQ: R2 = 0.70), ADF (HQ: R2 = 0.70, LQ: R2 = 0.77, and HLQ: R2 = 0.61), and NDF (HQ: R2 = 0.47, LQ: R2 = 0.83, and HLQ: R2 = 0.60). Temporal analysis revealed a slight decrease in CP and an increase in fiber during the dry season. Spatial analysis indicated that CP was higher in the steppe zone than in the savanna zone, and a decrease correlated with the rainfall gradient. The HQ alone was insufficient to meet livestock needs during the dry season, highlighting the importance of woody plants as an additional forage source. These findings will improve feed balance calculations in Sahelian countries, enable more sustainable use of rangelands, and contribute to the resilience of Sahelian communities to climate change. (c) 2024 The Author(s). Published by Elsevier Inc. on behalf of The Society for Range Management. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/

A framework for national-scale predictions of forage dry mass in Senegal: UAVs as an intermediate step between field measurements and Sentinel-2 images

International audienceMonitoring available feed for livestock is a key factor for developing pastoralism in the Sahel, and satellite images has proven useful in monitoring dry mass on large spatial scales. This approach requires field measurements of dry mass (herbaceous and woody plants) to calibrate such models based on Earth observation data. However, the need for representative field measurements can be a challenge when considering the low spatial resolution of available satellite data. Unmanned Aerial Vehicles (UAV) can produce very high-resolution images, so we tested UAVs as an intermediate step between field measurements and satellite images, to bridge the difference in spatial scale. We used 43 orthomosaics from a red-green-blue (RGB) UAV sensor in combination with field measurements of herbaceous and woody dry biomass at sites located primarily in the northern/central and southernmost parts of Senegal. We developed a dry mass model trained with filed observed measurements to be applied on the UAV orthomosaics. The dry mass information obtained from these UAV maps was subsequently related to vegetation indices derived from Sentinel-2 data to produce a national-scale 10 m spatial resolution baseline map of herbaceous and woody dry mass for Senegal in 2020. We obtained a high correlation between dry mass derived from UAV and Sentinel-2 indices (R-2 = 0.91), suggesting a robust basis for national-scale mapping. Lastly, our map was compared with a state-of-the-art annual reference map based on satellite remote sensing. This comparison showed a difference of 21 million tons of dry mass at national level. We concluded that bridging the spatial gap between field and satellite observations using spatially representative UAV data collection is a cost-effective approach for accurate mapping of dry mass at national level using freely available Sentinel-2 satellite data

Image3_Dry season forage assessment across senegalese rangelands using earth observation data.jpg

Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.</p

Dry season forage assessment across senegalese rangelands using earth observation data

International audienceStrengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R 2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R 2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems

Image4_Dry season forage assessment across senegalese rangelands using earth observation data.jpg

Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.</p