IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes.

GWAS have identified >200 risk loci for Inflammatory Bowel Disease (IBD). The majority of disease associations are known to be driven by regulatory variants. To identify the putative causative genes that are perturbed by these variants, we generate a large transcriptome data set (nine disease-relevant cell types) and identify 23,650 cis-eQTL. We show that these are determined by ∼9720 regulatory modules, of which ∼3000 operate in multiple tissues and ∼970 on multiple genes. We identify regulatory modules that drive the disease association for 63 of the 200 risk loci, and show that these are enriched in multigenic modules. Based on these analyses, we resequence 45 of the corresponding 100 candidate genes in 6600 Crohn disease (CD) cases and 5500 controls, and show with burden tests that they include likely causative genes. Our analyses indicate that ≥10-fold larger sample sizes will be required to demonstrate the causality of individual genes using this approach

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.</p

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol�which is a marker of cardiovascular risk�changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95 credible interval 3.7 million�4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world. © 2020, The Author(s), under exclusive licence to Springer Nature Limited

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities 1,2 . This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity 3�6 . Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55 of the global rise in mean BMI from 1985 to 2017�and more than 80 in some low- and middle-income regions�was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing�and in some countries reversal�of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories. © 2019, The Author(s)

The Measurement Of Nursing Home Quality: Multilevel Confirmatory Factor Analysis Of Panel Data

This study examined the validity of a measurement model of nursing home quality by using multilevel confirmatory factor analysis. Based on Mullan and Harrington\u27s (2001) facility-level quality measurement model, a two-level analysis (facility and state) of the measurement model were performed. Two research questions were asked: (1) Can the measurement model developed at the facility-level be applied to state-level nursing home quality measurement? (2) Is the measurement model of nursing home quality stable over time? Panel data of 1997 and 2001, from the national OSCAR database, were used to test the assumptions. The results show that the state-level measurement model fits the data better than the facility-level model does. When the indicator assessment was removed from the state-level measurement model, a better-fitted measurement model was found. The two-level measurement model is relatively stable over time, demonstrating the construct validity of this measurement model. © 2005 Springer Science+Business Media, Inc

Effects Of Institutional Mechanisms On Nursing Home Quality

This study explores institutional mechanisms explaining the variation in nursing home quality. A two-level panel design with the national data is conducted. Structural equation modeling is employed to examine the main and interaction effects of institutional factors on nursing home quality at both facility and state levels. The findings indicate that the quality of nursing homes is more responsive to regulatory and payment constraints than to normative and mimetic mechanisms. The potential demand for care, Medicaid reimbursement rate, and occupancy rate are positively associated with nursing home quality. An interaction effect between the regulatory mechanism and nurse staffing is statistically significant. The findings lend support to the importance of multi-level analysis of nursing home quality

The Impact Of Medicare Reimbursement Changes On Staffing And The Quality Of Care In Nursing Homes

This study assesses the impact of recent Medicare reimbursement changes on nurse staffing and quality of care in skilled nursing facilities. We utilise data from the Online Survey Certification and Reporting System and Area Resource Files for the years 1997-2003. The impacts of the reimbursement changes on licensed nurse hours per resident day and on a quality index were estimated, controlling for facility and market factors, and including interaction between payer mix and policy. We find that the 1997 Balanced Budget Act had a negative effect on staffing and quality while the 1999 Balanced Budget Reconciliation Act and the 2000 Benefits Improvement and Protection Act had positive effects on staffing and quality. The percentage of Medicaid patients was a strong contributor to lower staffing and quality, and moderated the effects of all three policies. The results should be useful for policy makers considering cuts in Medicare or Medicaid spending for nursing homes. © 2006 Inderscience Enterprises Ltd

Predictors Of Resident Outcome Improvement In Nursing Homes

The effects of contextual characteristics and nursing-related factors on the overall quality improvement of resident outcomes, measured by a weighted index in incidents of pressure ulcers, physical restraints, and catheter use in nursing homes, were investigated by autoregressive latent trajectory modeling of panel data (1997-2003). Findings show that in the initial study period, nursing homes with a smaller bed size, being for-profit, caring for more Medicare residents, having residents with lower acuity levels, being located elsewhere than the South, having a high level of nurse staffing, and certified with lower frequencies of nursing care deficiencies had better quality. The intercept factor, representing the baseline of quality, was well predicted by six of the eight contextual and facility characteristics variables, and the slope trajectory of quality was only weakly predicted by them. The improved quality in resident outcomes was associated with facilities having fewer nursing care deficiency citations than their counterparts. © 2006 Sage Publications