Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? results from the multinational coronary ct angiography evaluation for clinical outcome: An international multicenter registry (confirm)

Although metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD), and prognosis of patients with metabolic syndrome to those with individual metabolic syndrome components. The study cohort consisted of 27125 consecutive individuals who underwent ≥64-detector row coronary CT angiography (CCTA) at 12 centers from 2003 to 2009. Metabolic syndrome was defined as per NCEP/ATP III criteria. Metabolic syndrome patients (n=690) were matched 1:1:1 to those with 1 component (n=690) and 2 components (n=690) of metabolic syndrome for age, sex, smoking status, and family history of premature CAD using propensity scoring. Major adverse cardiac events (MACE) were defined by a composite of myocardial infarction (MI), acute coronary syndrome, mortality and late target vessel revascularization. Patients with 1 component of metabolic syndrome manifested lower rates of obstructive 1-, 2-, and 3-vessel/left main disease compared to metabolic syndrome patients (9.4% vs 13.8%, 2.6% vs 4.5%, and 1.0% vs 2.3%, respectively; p0.05). At 2.5 years, metabolic syndrome patients experienced a higher rate of MACE compared to patients with 1 component (4.4% vs 1.6%; p=0.002), while no difference observed compared to individuals with 2 components (4.4% vs 3.2% p=0.25) of metabolic syndrome. In conclusion, Metabolic syndrome patients have significantly greater prevalence, severity, and prognosis of CAD compared to patients with 1 but not 2 components of metabolic syndrome

Is metabolic syndrome predictive of prevalence, extent, and risk of coronary artery disease beyond its components? results from the multinational coronary CT angiography evaluation for clinical outcome : an international multicenter registry (CONFIRM)

Although metabolic syndrome is associated with increased risk of cardiovascular disease and events, its added prognostic value beyond its components remains unknown. This study compared the prevalence, severity of coronary artery disease (CAD), and prognosis of patients with metabolic syndrome to those with individual metabolic syndrome components. The study cohort consisted of 27125 consecutive individuals who underwent 6564-detector row coronary CT angiography (CCTA) at 12 centers from 2003 to 2009. Metabolic syndrome was defined as per NCEP/ATP III criteria. Metabolic syndrome patients (n=690) were matched 1:1:1 to those with 1 component (n=690) and 2 components (n=690) of metabolic syndrome for age, sex, smoking status, and family history of premature CAD using propensity scoring. Major adverse cardiac events (MACE) were defined by a composite of myocardial infarction (MI), acute coronary syndrome, mortality and late target vessel revascularization. Patients with 1 component of metabolic syndrome manifested lower rates of obstructive 1-, 2-, and 3-vessel/left main disease compared to metabolic syndrome patients (9.4% vs 13.8%, 2.6% vs 4.5%, and 1.0% vs 2.3%, respectively; p0.05). At 2.5 years, metabolic syndrome patients experienced a higher rate of MACE compared to patients with 1 component (4.4% vs 1.6%; p=0.002), while no difference observed compared to individuals with 2 components (4.4% vs 3.2% p=0.25) of metabolic syndrome. In conclusion, Metabolic syndrome patients have significantly greater prevalence, severity, and prognosis of CAD compared to patients with 1 but not 2 components of metabolic syndrome

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions. Funding: Bill & Melinda Gates Foundation

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

Not Available

Not AvailableThe project was implemented between August 2009 and March 2014 in the Western Yamuna Canal (WYC) command of Haryana-one of the oldest canal systems in the country. The command is divided into 8 water service circles with gross cultivated area (GCA) of 13,534 km2 covering 5 districts in full (Karnal, Panipat, Sonipat, Rohtak and Jhajjar), 7 districts in part (Yamuna Nagar, Kurukshetra, Jind, Hisar, Bhiwani, Rewari and Gurgaon) and 2205 villages. The sub-project was aimed at application of ICT for development of database and DSS program for making informed decision for improving crop yield, income and livelihoods of small farmers in six saline environments. Therefore, the sub-project has adopted the advanced Geo-IT with holistic and participatory approaches for database generation, distributed modelling and development of DSS program for generating and implementing site-specific BMP based interventions at farmers’ fields to address the issue of head-tail productivity difference, low crop yield and income in saline environments in the WYC command. The sub-project was unique in many ways by adopting a consortia mode for project development and implementation by utilizing institutions’ expertises, bottom-up approach to problem solving, state-of-the-art Geo-IT, distributed modelling, and stakeholders’ servicing to infuse confidence on the developed Database and DSS program through demonstrations, workshops and hands-on trainings to address the issues of head-tail productivity difference and sustainability of high yield in saline/sodic conditions under Butana and Jhajjar distributaries. A series of consultations with stakeholders from two selected distributaries was conducted by applying focused group discussions, brainstorming workshops, PRA, and field days. The output of these activities was supported by baseline surveys conducted in the selected villages in both distributaries to pave the way to implementing site specific BMP based interventions generated through DSS program. Since the sub-project was a multi-institute and multi-disciplinary addressing complex issue of head-tail difference in productivity and livelihoods in Butana and Jhajjar distributaries having diverse bio-physical and socio-economic constraints in saline environment, it was necessary to develop common strategies by all project partners. This was achieved through interactive meetings, discussions, and workshops during the initial and implementation phases of the sub-project. The achievements and outcome of the sub-project were grouped into eight major themes and are presented as follows. Development and Online Dissemination of Irri-agro Informatics Spatial Database An Irri-agro Informatics Geodatabase on bio-physical and socio-economic resources of the WYC command was developed using ArcGIS v10 from secondary source maps and data, satellite remote sensing data and GPS field surveys and is comprised of 14 key thematic layers viz. canal network with system and inflow characteristics, rainfall pattern, groundwater quality, salt-affected soils, soil texture, cropping system, terrain, waterlogging, land use, infrastructure, geology, socio-economic data, satellite data derived current land use, and digital cadastral data. The geodatabase, updated annually for the rabi crop, soil and water salinity, canal inflow and remote sensing data from 2009-2013, has characterized the bio-physical resources at entire command, district, tehsil, distributary, village, watercourse and farm levels. The characteristics of the WYC command are of 3 levels in canal network (main canal to watercourse) with system and inflow characteristics; 4 rainfall departure classes (excess, normal, deficient and scanty) during 2006-13; 6 rainfall zones - 18.6% area (900 mm); 5 groundwater quality classes- good (38.3% area), marginal (15.2%), saline (5.3%), sodic (4.2%) and saline-sodic (37%); 2 salt-affected soils (SAS)- saline (4.0%), and sodic (14.5%); 4 soil texture classes- sand (2.4%), loamy sand (6%), sandy loam (78.6%), and loam (13%); and 5 cropping systems (rice-wheat, bajra-wheat/mustard, sorghum-wheat, cotton-wheat, and sugarcane-wheat). The Geodatabase can be queried for single or multiple attributes/ features using criteria such as monsoonal rainfall, adequacy of canal supplies, groundwater quality, saline/sodic soils, soil texture, village and other information and the district-wise crop production constraints and their spatial extent were identified. These constraints have prevailed in the large parts of Jind, Sonipat, Rohtak and Jhajjar districts which are input to crop-water-salinity-yield response model to predict the crop yield loss in six saline environments. The geodatabase has also delineated the area of low productivity district-wise in the WYC command adopting a GIS protocol using data of canal supply, GW quality, SAS and NDVI. About 7.24% of the WYC command was affected with low productivity (988.9 km2), mainly in Rohtak, Jind and Sonipat districts. The database originally developed in ESRI’s ArcGIS proprietary format was migrated to an open source platform (Quantum GIS v1.7.4) which has allowed free distribution of the database and GIS software to the stakeholders for querying and generating value added maps. The database was further migrated to PostGIS v2.0.3 and GeoServer v2.3.0 for online dissemination and a web map service of the database was developed for online visualization and querying of multi-thematic vector layers overlaid with Google map/earth by stakeholders for identifying resource constraints at watercourse or village level. Upscaling of Wheat Yield to Command Scale The wheat yield data from 290 crop cutting samples collected from demonstration and monitoring fields in the WYC command using GPS handset and data from Agriculture Department, Haryana were correlated with the temporal NDVI spectral profile generated from three Resourcesat-1 LISS-3 imageries (19 Dec, 5 Feb and 11 Mar) for the rabi season 2010-11. These data were analysed using GIS in tandem with spectral vegetation, salinity and waterlogging indices (NDVI, NDSI and NDWI) and were upscaled to generate the map of wheat yield variation in the WYC Command using regression technique. The wheat yield ranged from 3.51 to 4.75 t ha-1. The yield less than 4.0 t ha-1 was assessed in 56% area of the command which lies in parts of Sonipat, Jind, Rohtak, Hisar, Jhajjar and Bhiwani districts. AquaCrop and SWAP Models for Predicting Wheat Yield and Salt Dynamics Water driven AquaCrop model 4.0 with salinity option, and SWAP model were calibrated for grain yield, water productivity and rootzone salt dynamics for three salt tolerant (KRL-1-4, 19 and 210) and one high yielding (HD-2894) wheat varieties, and four water salinities (1.5, 4, 8 and 12 dS m-1) from the experimental data of Rabi 2009-10 and were validated from the data of Rabi 2010-11. The accuracy of model prediction was evaluated by model efficiency (ME), index of agreement (d) and coefficient of determination (R2) comparing between the observed and the model simulated results. The calibrated AquaCrop model resulted in ME, d and R2 of 0.99 each for grain yield; and 0.27, 0.98 and 0.99 for water productivity, respectively, for all wheat varieties and irrigation salinity levels whereas the calibrated SWAP model resulted in ME and d of 0.96 and 0.99 for grain yield; and 0.76 and 0.93 for root zone salinity, respectively. The ME, d and R2 for the validated AquaCrop model were 0.85, 0.96, and 0.94 for grain yield, respectively, for all varieties and salinity levels whereas the ME, d and R2 for the validated SWAP model were 0.75, 0.93 and 0.95 for grain yield; and 0.95, 0.98, and 0.96 for root zone salinity, respectively. Therefore, both the models could predict the wheat yield with the acceptable accuracy for all wheat varieties and irrigation salinity levels. However, SWAP could simulate the root zone soil salinity more accurately. Therefore, both the models were integrated to DSS program and can be applied for prediction of wheat grain yield and rootzone salt dynamics in the WYC Command wherever site specific input parameters for these models are available. However, AquaCrop can be preferred for limited availability of site specific input parameters. Crop Water Demand Driven Canal Schedule The spatial variability of weather, soil, crop, canal network and inflow of Jhajjar distributary command was generated from the Irri-agri informatics geodatabase using GIS. These information were input to CROPWAT model 8.0 and the irrigation requirement of wheat and rice grown in the distributary command was estimated. The analysis indicated an area of 5601 hectares was under rice-wheat cropping system in the distributary. The irrigation requirement of wheat at field level by CROPWAT was 254.6 mm and that of rice was 969.6 mm with effective rainfall of 55.8 and 461.8 mm during the wheat and rice growing seasons, respectively. Further, the comparison of crop water demand and canal supply as per the roster for twelve locations in Jhajjar distributary in WYC command showed that the canal supply as per roster for 2011-12 is far less (< 48%) than the demand in majority of location points. Moreover, the protocols developed for assessment of irrigation requirement can be upscaled from distributary to branch canal command. A Standalone DSS Program for Enhancing Productivity in Irrigated Saline Environment A standalone window based DSS program v1.1 was developed in Microsoft C# programming language on .NET framework 3.5 by integrating database, key modules, crop-water-salinity-yield module, calibrated AquaCrop and SWAP models to generate and evaluate the BMP based interventions for various resource scenarios in saline environments for enhancing productivity. The developed DSS application consists of six main modules- Crop Water Demand, Canal Supply, Groundwater, Irrigation Scheduling, Modelling, and BMPs based Strategies, and three supporting modules- Database, Farmer’s Services and Help. These main modules were validated, debugged and integrated into the main user interface. The Database module displays the eight thematic data of the Irri-agro Informatics Database for assessing the six saline scenarios/constraints. The Crop Water Demand module computes the crop ET from daily weather data for 2001-2013 using Penman-Monteith method and weekly crop coefficient. The irrigation demand at watercourse outlet is thus computed from aggregation of water demand of various crops after subtracting effective rainfall and capillary water, and adding conveyance and application losses. The Canal Supply module computes the canal supply and irrigation gap to meet full crop water demand whereas the Groundwater module computes the groundwater share with or without water quality consideration. In Irrigation Scheduling module, irrigation schedules to maximize/ optimize yield are generated for wheat and other crops from one of four options- canal supply or fresh groundwater in direct or conjunctive mode, deficit irrigation, effective conjunctive mode with poor quality waters, and both water and salinity stresses. In Modelling, a crop-water-salinity-yield response module, and a module with AquaCrop and SWAP were integrated. A crop yield response module for six prevailing saline environments in the WYC command viz, Surface water stagnation, Waterlogging, Soil salinity, Soil sodicity, Saline/sodic water irrigation, and Deficit irrigation was developed to predict the relative crop yield loss in order to generate and recommend innovative BMPs for minimizing yield loss. This module was validated from the field demo data. The relative yield loss for five major crops (wheat, barley, mustard, pearlmillet and pigeon pea) in water stagnation and waterlogging can be predicted for different duration of water stagnation/depth of waterlogging and subsequently, BMPs are recommended for minimizing yield loss. The relative yield loss in soil salinity and sodicity can be predicted for rootzone salinity (ECe) and sodicity (ESP) values at sowing, mid and harvest time for five crops. The BMPs for four ranges of ECe (12 dS m-1) and three ranges of ESP (50%) were recommended for minimizing yield loss. The Soil EC converter (EC2 to ECe), and soil ESP converter (SAR, pHs and pH2 to ESP) were developed for use of data from state departments. The gypsum requirement (GR) can be computed using Schoonover’s formula or standard GR graph. Water quality for saline/Sodic water irrigation and its permissible range for direct or conjunctive application in different agro-climatic zones are assessed. The relative yield loss can be predicted for any water salinity/sodicity values for five crops and BMPs with direct/ conjunctive use are suggested for minimizing yield loss. In deficit irrigation, the phenological growth stages for five crops are assessed and a deficit irrigation strategy based on number of available irrigations is suggested. A module developed at WTC with yield production functions, AquaCrop and SWAP was integrated under Modelling menu and can estimate the crop yield under varying soil and water salinities, foliar potassium fertilization and salt deposition. SWAP can simulate crop productivity and rootzone salinity build-up wherever site specific input parameters are available. BMP based strategies for six saline environments with their quantitative impact, and useful information for farmers on soil and water sampling procedures and testing facilities, salt tolerant and high yielding crop varieties, Help and Hindi support are also provided for use of stakeholders. Wheat Demonstration at Farmers’ Fields In order to develop confidence of farmers on DSS generated BMP based interventions, field demonstrations of wheat crop at 52 farmers’ fields in mid and tail reaches of Butana distributary and Jhajjar distributary in saline environments were conducted during three rabi seasons (2010-11, 2011-12 and 2012-13) in Sonipat, Rohtak and Jhajjar districts. The DSS generated BMPs- four high yielding (HD-2967, 2891, and 2894, and DBW-17) and three salt tolerant varieties, (KRL-1-4, 19, and 210), optimum irrigation scheduling, effective conjunctive use of moderate saline, SAR saline and high RSC sodic groundwater, zero tillage, and laser land leveling were evaluated for enhancing crop yield. The wheat yield increased ranging from 17 to 33% in saline environment and improved the income of small farmers by Rs. 13,490-25,700 per hectare. The field demonstrations have infused confidence in stakeholders on DSS generated interventions and these results have also validated the DSS modules. Transfer of Database, DSS Program and Knowledge to Stakeholders Since stakeholder’s servicing was the important activity of the project with 13% budget allocation, 121 district officers/engineers from CADA, Agriculture and, Irrigation Departments, KVKs and Regional Research Stations (CCS Haryana Agricultural University, Hisar) and NGOs from 12 districts within the WYC Command were imparted skill and knowledge on Database, DSS program and their application through hands-on trainings and workshops for generating BMPs for enhancing productivity in six saline environments. Similarly, 1194 members from canal water users’ associations and farmers from Karnal, Panipat, Sonipat, Jind, Rohtak, Jhajjar, Rewari and Bhiwani districts were imparted knowledge on DSS generated BMP based interventions for growing bumper crop yield under six prevailing saline environments. Upscaling of DSS Program A feasibility assessment for upscaling of DSS program v1.1 was conducted in 7 KVKs at Panipat, Sonipat, Rohtak, Jhajjar, Rewari, Jind, and Kaithal in terms of availability of computer hardware and software, internet connectivity, manpower, and power supply with generator backup. The problems encountered were non-availability of suitable manpower at Rohtak, and irregular power supply during working hours at Panipat, Sonipat, and Jind due to rural power supply connection. The DSS program was tested at the existing computers at 3 KVKs (Panipat, Rohtak and Rewari) and the backstopping services for deployment are also being provided. Six Divisional offices of CADA at Karnal, Sonipat, Panipat, Rohtak, Jind and Kaithal were also assessed and have met all the requirements for DSS deployment. The upscaling and trainings are being continued from Institute fund and fund from CADA. The stakeholders have shown keenness on use of database and DSS program for solving their field problems.Not Availabl

Potentially modifiable factors associated with health-related quality of life among people with chronic kidney disease: baseline findings from the National Unified Renal Translational Research Enterprise CKD (NURTuRE-CKD) cohort

Background and hypothesisMany non-modifiable factors are associated with poorer health-related quality of life (HRQoL) experienced by people with chronic kidney disease (CKD). We hypothesise that potentially modifiable factors for poor HRQoL can be identified among CKD patients, providing potential targets for intervention.MethodThe NURTuRE-CKD cohort study recruited 2996 participants from nephrology centres with all stages of non-dialysis dependent CKD. Baseline data collection for sociodemographic, anthropometric, biochemical, and clinical information, including Integrated Palliative care Outcome Scale renal (IPOS), Hospital Anxiety and Depression score (HADS), and EQ-5D-5L as HRQoL measure, took place between 2017-2019. EQ-5D-5L dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) were mapped to an EQ-5D-3L value set to derive index value. Multivariable mixed effects regression models, adjusted for known factors affecting HRQoL with recruitment region as a random effect, were fit to assess potentially modifiable factors associated with index value (linear) and within each dimension (logistic).ResultsAmong the 2958/2996 (98.7%) participants with complete EQ-5D data, 2201 (74.4%) reported problems in at least one EQ-5D-5L dimension. Multivariable linear regression identified independent associations between poorer HRQoL (EQ-5D-3L index value) and obesity (body mass index30.0kg/m2, -0.037 , 95%CI -0.058 to -0.016, p=0.001), HADS depression score 8 (-0.159, -0.182 to -0.137, p=<0.001), anxiety score 8 (-0.090, -0.110 to -0.069, p=<0.001), taking 10 medications (-0.065, -0.085 to -0.046, p=<0.001), sarcopenia (-0.062, -0.080 to -0.043, p=<0.001) haemoglobin <100g/L (-0.047, -0.085 to -0.010, p=0.012) and pain (-0.134, -0.152 to -0.117, p=<0.001). Smoking and prescription of prednisolone independently associated with problems in self-care and usual activities respectively. Renin-angiotensin system inhibitor (RASi) prescription associated with fewer problems with mobility and usual activities. ConclusionPotentially modifiable factors including obesity, pain, depression, anxiety, anaemia, polypharmacy, smoking, steroid use, and sarcopenia associated with poorer HRQoL in this cohort, whilst RASi use was associated with better HRQoL in two dimensions

Potentially modifiable factors associated with health-related quality of life among people with chronic kidney disease: baseline findings from the National Unified Renal Translational Research Enterprise CKD (NURTuRE-CKD) cohort

Background and hypothesis: many non-modifiable factors are associated with poorer health-related quality of life (HRQoL) experienced by people with chronic kidney disease (CKD). We hypothesise that potentially modifiable factors for poor HRQoL can be identified among CKD patients, providing potential targets for intervention.Method: the NURTuRE-CKD cohort study recruited 2996 participants from nephrology centres with all stages of non-dialysis dependent CKD. Baseline data collection for sociodemographic, anthropometric, biochemical, and clinical information, including Integrated Palliative care Outcome Scale renal (IPOS), Hospital Anxiety and Depression score (HADS), and EQ-5D-5L as HRQoL measure, took place between 2017-2019. EQ-5D-5L dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) were mapped to an EQ-5D-3L value set to derive index value. Multivariable mixed effects regression models, adjusted for known factors affecting HRQoL with recruitment region as a random effect, were fit to assess potentially modifiable factors associated with index value (linear) and within each dimension (logistic).Results: among the 2958/2996 (98.7%) participants with complete EQ-5D data, 2201 (74.4%) reported problems in at least one EQ-5D-5L dimension. Multivariable linear regression identified independent associations between poorer HRQoL (EQ-5D-3L index value) and obesity (body mass index≥30.0kg/m2, β-0.037 , 95%CI -0.058 to -0.016, p=0.001), HADS depression score ≥8 (β-0.159, -0.182 to -0.137, p=&lt;0.001), anxiety score ≥8 (β-0.090, -0.110 to -0.069, p=&lt;0.001), taking ≥10 medications (β-0.065, -0.085 to -0.046, p=&lt;0.001), sarcopenia (β-0.062, -0.080 to - 0.043, p=&lt;0.001) haemoglobin &lt;100g/L (β-0.047, -0.085 to -0.010, p=0.012) and pain (β-0.134,-0.152 to -0.117, p=&lt;0.001). Smoking and prescription of prednisolone independently associated with problems in self-care and usual activities respectively. Renin-angiotensin system inhibitor (RASi) prescription associated with fewer problems with mobility and usual activities. Conclusion: potentially modifiable factors including obesity, pain, depression, anxiety, anaemia, polypharmacy, smoking, steroid use, and sarcopenia associated with poorer HRQoL in this cohort, whilst RASi use was associated with better HRQoL in two dimensions