Myocardial infarction and mortality following joint surgery in patients with rheumatoid arthritis: a retrospective cohort study

Summary of patient level comparing those with rheumatoid arthritis to those without rheumatoid arthritis. (DOCX 14 kb

Data Linkage: A powerful research tool with potential problems

Background: Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes

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

Linking clinical and administrative data to evaluate intensive care outcomes

There is a major focus in health reform in Australia and internationally on monitoring and reporting organisational performance measures, such as standardised mortality rates, in different clinical areas. Given the increasing reliance on existing data for measuring mortality rates, it is important that the accuracy and validity of data are high. Yet, researchers have demonstrated limitations in measuring in-hospital mortality to evaluate intensive care, as it can lead to skewed measurements depending on the discharge practices of different organisations. In Australia, the intensive care clinical registry does not currently measure survival outcomes of patients after hospital discharge but there is interest in doing so. While administrative data sources have the ability to assess outcomes of intensive care patients after hospital discharge, these data may not have sufficient clinical detail to enable robust risk adjustment. Data linkage can be used to link clinical registry and administrative data to enable the measurement of long-term outcomes while using clinical variables to enhance risk-adjustment. However, linkage must be conducted in a robust fashion so that additional error introduced from sub-optimal linkage processes will not bias results. The main aim of the thesis is to assess the utility of linked administrative and clinical data compared to administrative alone and clinical data alone for monitoring long term survival outcomes of ICU patients. The objectives of the thesis are: 1) to define key attributes of linked data for assessing the quality of study results; and 2) to compare the use of linked data to administrative data alone and clinical data alone for a) predicting survival of intensive care patients at 180 days after discharge and b) assessing systematic variation between observed and expected deaths. There were two projects involved in this thesis to address aims 1 and 2, respectively. The first project involved a Delphi consensus process including Australian experts to develop standardised reporting guidelines for assessing the quality of data linkage studies. The resulting guidelines included a list of fourteen items. The guidelines were then applied by two researchers to a stratified selection of data linkage studies to assess their inter-rater reliability (k=0.6). The second project involved the linkage of the Victorian Admitted Episodes Dataset to the Australian/New Zealand Intensive Care Society clinical database of adult critical care patient episodes in the state of Victoria. The linkage procedure was validated to determine whether sources of bias were introduced into the dataset through linkage processes. The added predictive capabilities of the full linked dataset were compared to a model using the administrative data only (C=0.85 v 0.75), a model using the clinical data only (C=0.85 v. 0.84) and a model using a limited sub-set of linked data (C=0.85 v 0.83). Variable Life Adjusted Display (VLAD) charts were developed using both of the linked, administrative and clinical predictive models to determine whether the linked data enhanced the capacity for detecting systematic variation in mortality ratios. It was found that the use of data linkage can enhance the measurement of long-term mortality indicators in intensive care by improving the accuracy of data, risk prediction models and methods for displaying systematic variation in death ratios. Yet, these benefits must be considered together with the limitations of the data, which can influence the accuracy of the linkage process. Identifying and reporting these issues will help to improve data quality and linkage in the future