ABSTRACTObjectivesNational statistics for hospital admissions for acute CHD based on unlinked administrative data are inflated because of inter/intra-hospital transfers or related readmissions for further investigations or procedures. Our objective was to estimate the inflation of CHD inpatient counts using multiple approaches based initially on Western Australian data that can be applied to future National studies.
ApproachWe used a linked hospital morbidity dataset from the Western Australian Data Linkage System to determine hospitalisations for each CHD subcategory from 1990-2010. Transfers were defined as contiguous admissions separated by ≤1 day. Episodes-of-care (EOC) were defined as admissions (with/without transfers) that were within 28 days of the initial CHD admission. As the principal diagnosis may vary between hospitals involved in transfers or admissions within an EOC, we explored four approaches for allocating a diagnosis:i. Hierarchical diagnosis: selection of diagnosis based on clinical severity (ST-elevation myocardial infarction (STEMI)>non-STEMI>unstable angina>stable angina>other CHD>chest pain); ii. Hospital hierarchy: diagnosis based on highest hospital level (tertiary>private>other metropolitan non-tertiary>rural);iii/iv. Temporal order of diagnosis: diagnosis based on first or last record in transfer/EOC.
ResultsThe proportion of cases that were transferred varied according to disease severity and time: 13% (1990) to 27% (2010) for STEMI; 5% to 7% for stable angina and unchanged at 4% for chest pain. Compared to transfer-level data using the first approach, unlinked data overestimated STEMI counts by 3% (1990) to 11% (2010), stable angina by 3% to 5% and chest pain by 6% to 6%. Similarly for EOC-level data, the overestimates were 5% (1990) to 12% (2010) for STEMI, 13% to 19% for stable angina and 20% to 14% for chest pain. The four approaches for allocating a diagnosis produced differing counts with the difference being larger for more clinically severe diagnoses than for less clinically severe diagnoses. For example, using transfer-level data, the differences between approaches i and iv in 2010 were 12%, 2% and 1% for STEMI, stable angina and chest pain respectively.
ConclusionThere is a potential to overestimate counts of CHD in inpatient data if transfers and readmissions are not taken into account, and this inaccuracy can differ across disease subcategories and approach used. This has important implications where higher disease severity, such as myocardial infarction, is an indicator of population health. Transfer- or EOC-level data are more likely to reflect true CHD hospitalisation counts than unlinked-level data, and are more appropriate for epidemiological studies of CHD rates.
AbstractObjectiveTo estimate the prevalence of hand-arm vibration (HAV) in Australian workplaces.MethodsThe Australian Workplace Exposure Survey (AWES)—Hearing was a cross-sectional telephone survey of Australian workers conducted in 2016–2017. Respondents were asked about the time spent using tools or performing tasks known to be associated with HAV during their most recent working day. We created a library of HAV magnitude levels for each tool/task and estimated each worker's daily HAV exposure level using standard formulae. We categorized each worker as to whether they exceeded the daily occupational limits of 2.5 and 5.0 m/s2. Results were extrapolated to the Australian working population using a raked weighting method.ResultsIn our sample of 4991 workers, 5.4% of men and 0.7% of women exceeded the HAV action limit of 2.5 m/s2 on their most recent working day. We estimate that 3.8% of the Australian workforce exceeds the HAV limit of 2.5 m/s2 and 0.8% exceeds the 5 m/s2 limit. Men were more likely to exceed the HAV limits than women, as were those with trade qualifications, and those who worked in remote locations. Workers in the construction, farming, and automobile industries had the highest prevalence of HAV exposure. Tool groups that contributed to higher exposure levels included: compactors, rollers, and tampers; power hammers and jackhammers; and underground mining equipment.ConclusionsHAV is common in the Australian working population. Given the health risks associated with this exposure, reduction strategies and interventions should be developed, with engineering controls as the starting point for exposure reduction strategies.
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Volume 222, p. 112481
This paper presents a summary of the key findings of the special issue of Atmosphere on Air Quality in New South Wales and discusses the implications of the work for policy makers and individuals. This special edition presents new air quality research in Australia undertaken by (or in association with) the Clean Air and Urban Landscapes hub, which is funded by the National Environmental Science Program on behalf of the Australian Government's Department of the Environment and Energy. Air pollution in Australian cities is generally low, with typical concentrations of key pollutants at much lower levels than experienced in comparable cities in many other parts of the world. Australian cities do experience occasional exceedances in ozone and PM2.5 (above air pollution guidelines), as well as extreme pollution events, often as a result of bushfires, dust storms, or heatwaves. Even in the absence of extreme events, natural emissions play a significant role in influencing the Australian urban environment, due to the remoteness from large regional anthropogenic emission sources. By studying air quality in Australia, we can gain a greater understanding of the underlying atmospheric chemistry and health risks in less polluted atmospheric environments, and the health benefits of continued reduction in air pollution. These conditions may be representative of future air quality scenarios for parts of the Northern Hemisphere, as legislation and cleaner technologies reduce anthropogenic air pollution in European, American, and Asian cities. However, in many instances, current legislation regarding emissions in Australia is significantly more lax than in other developed countries, making Australia vulnerable to worsening air pollution in association with future population growth. The need to avoid complacency is highlighted by recent epidemiological research, reporting associations between air pollution and adverse health outcomes even at air pollutant concentrations that are lower than Australia's national air quality standards. Improving air quality is expected to improve health outcomes at any pollution level, with specific benefits projected for reductions in long-term exposure to average PM 2.5 concentrations.
Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs. ; Novartis ; Eli Lilly and Company ; AstraZeneca ; AbbVie ; Pfizer UK ; Celgene ; Eisai ; Genentech ; Merck Sharp and Dohme ; Roche ; Cancer Research UK ; Government of Canada ; Array BioPharma ; Genome Canada ; National Institutes of Health ; European Commission ; Ministère de l'Économie, de l'Innovation et des Exportations du Québec ; Seventh Framework Programme ; Canadian Institutes of Health Research