Background Tobacco smoking is a leading cause of cardiovascular disease (CVD) morbidity and mortality. Evidence on the relation of smoking to different subtypes of CVD, across fatal and non-fatal outcomes, is limited. Methods A prospective study of 188,167 CVD- and cancer-free individuals aged ≥ 45 years from the Australian general population joining the 45 and Up Study from 2006 to 2009, with linked questionnaire, hospitalisation and death data up to the end of 2015. Hazard ratios (HRs) for hospitalisation with or mortality from CVD among current and past versus never smokers were estimated, including according to intensity and recency of smoking, using Cox regression, adjusting for age, sex, urban/rural residence, alcohol consumption, income and education. Population-attributable fractions were estimated. Results During a mean 7.2 years follow-up (1.35 million person-years), 27,511 (crude rate 20.4/1000 person-years) incident fatal and non-fatal major CVD events occurred, including 4548 (3.2) acute myocardial infarction (AMI), 3991 (2.8) cerebrovascular disease, 3874 (2.7) heart failure and 2311 (1.6) peripheral arterial disease (PAD) events. At baseline, 8% of participants were current and 34% were past smokers. Of the 36 most common specific CVD subtypes, event rates for 29 were increased significantly in current smokers. Adjusted HRs in current versus never smokers were as follows: 1.63 (95%CI 1.56–1.71) for any major CVD, 2.45 (2.22–2.70) for AMI, 2.16 (1.93–2.42) for cerebrovascular disease, 2.23 (1.96–2.53) for heart failure, 5.06 (4.47–5.74) for PAD, 1.50 (1.24–1.80) for paroxysmal tachycardia, 1.31 (1.20–1.44) for atrial fibrillation/flutter, 1.41 (1.17–1.70) for pulmonary embolism, 2.79 (2.04–3.80) for AMI mortality, 2.26 (1.65–3.10) for cerebrovascular disease mortality and 2.75 (2.37–3.19) for total CVD mortality. CVD risks were elevated at almost all levels of current smoking intensity examined and increased with smoking intensity, with HRs for total CVD mortality in current versus never smokers of 1.92 (1.11–3.32) and 4.90 (3.79–6.34) for 4–6 and ≥ 25 cigarettes/day, respectively. Risks diminished with quitting, with excess risks largely avoided by quitting before age 45. Over one third of CVD deaths and one quarter of acute coronary syndrome hospitalisations in Australia aged < 65 can be attributed to smoking. Conclusions Current smoking increases the risk of virtually all CVD subtypes, at least doubling the risk of many, including AMI, cerebrovascular disease and heart failure. Paroxysmal tachycardia is a newly identified smoking-related risk. Where comparisons are possible, smoking-associated relative risks for fatal and non-fatal outcomes are similar. Quitting reduces the risk substantially. In an established smoking epidemic, with declining and low current smoking prevalence, smoking accounts for a substantial proportion of premature CVD events. Electronic supplementary mater...

BackgroundHospitalisation for heart failure is common and post-discharge outcomes, including readmission and mortality, are often poor and are poorly understood. The purpose of this study was to examine patient- and hospital-level variation in the risk of 30-day unplanned readmission and mortality following discharge from hospital with a diagnosis of heart failure.MethodsProspective cohort study using data from the Sax Institute’s 45 and Up Study, linking baseline survey (Jan 2006-April 2009) to hospital and mortality data (to Dec 2011). Primary outcomes in those admitted to hospital with heart failure included unplanned readmission, mortality and combined unplanned readmission/mortality, within 30 days of discharge. Multilevel models quantified the variation in outcomes between hospitals and examined associations with patient- and hospital-level characteristics.ResultsThere were 5074 participants with a heart failure admission discharged from 251 hospitals; 1052 (21%) had unplanned readmissions, 186 (3.7%) died, and 1146 (23%) had either/both outcomes within 30 days of discharge. Crude outcomes varied across hospitals, but between-hospital variation explained little of the total variation in outcomes (intraclass correlation coefficients (ICC) after inclusion of patient factors: 30-day unplanned readmission ICC = 0.0125 (p = 0.24); death ICC = 0.0000 (p > 0.99); unplanned readmission/death ICC = 0.0266 (p = 0.07)). Patient characteristics associated with a higher risk of unplanned readmission included: being male (male vs female, adjusted odds ratio (aOR) = 1.18, 95% CI: 1.00–1.37); prior hospitalisation for cardiovascular disease (aOR = 1.44, 1.08–1.91) and for anemia (aOR = 1.36, 1.14–1.63); comorbidities at admission (severe vs none: aOR = 1.26, 1.03–1.54); lower body-mass-index (obese vs normal weight: aOR = 0.77, 0.63–0.94); and lower social interaction scores. Similarly, risk of 30-day mortality was associated with patient- rather than hospital-level factors, in particular age (≥85y vs 45–< 75y: aOR = 3.23, 1.93–5.41) and comorbidity (severe vs none: aOR = 2.68, 1.82–3.94).ConclusionsThe issue of high readmission and mortality rates in people with heart failure appear to be system-wide, with the variation in these outcomes essentially attributable to variation between patients rather than hospitals. The findings suggest that there are limitations in using these outcomes as hospital performance measures in this patient population and support the need for patient-centred strategies to optimise heart failure management and outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2152-0) contains supplementary material, which is available to authorized users.

We studied 107 patients aged over 65 years undergoing urgent or emergency laparotomy. Aspects of preoperative assessment, perioperative management and postoperative care were analysed by multiple logistic regression to determine the factors that predicted hospital survival. We determined which factors influenced anaesthetists' prediction that patients would survive. These predictions were made both before and immediately after operation. The factors associated with the use of invasive cardiovascular monitoring were also studied. We obtained a model that accounted for 93% of the variability in the likelihood of survival. Age and ASA status were significant predictors of survival (P < 0.05), and of anaesthetists' prediction of mortality both before and after operation. Several other factors were significant determinants of survival but were not determinants of the anaesthetist's opinion regarding survival.

A retrospective follow-up study of 21,013 workers employed at a foundry and two engine manufacturing plants was conducted to determine if these workers had an unusual mortality experience. A total of 2,235 deaths occurred during the follow-up period of 1970-1987. Mortality from all causes was lower than expected. Men experienced a 6-13% excess of lung cancer deaths, depending on the choice of the comparison group. The data displayed evidence of a positive trend between lung cancer mortality and increasing duration of employment (p = 0.008). White men experienced a statistically significant excess of deaths from stomach cancer (standardized mortality ratio [SMR] = 158; 95% confidence interval [CI] = 101-234). Black men had increased mortality from pancreatic cancer, especially among engine plant workers (SMR = 303; CI = 121-624), and an excess of prostate cancer, concentrated among foundry workers (SMR = 234; CI = 112-430).

Objectives: External cause International Classification of Diseases (ICD) codes are commonly used to ascertain adverse drug reactions (ADRs) related to hospitalisation. We quantified ascertainment of ADR-related hospitalisation using external cause codes and additional ICD-based hospital diagnosis codes. Methods:We reviewed the scientific literature to identify different ICD-based criteria for ADR-related hospitalisations, developed algorithms to capture ADRs based on candidate hospital ICD-10 diagnoses and external cause codes (Y40-Y59), and incorporated previously published causality ratings estimating the probability that a specific diagnosis was ADR related. We applied the algorithms to the NSW Admitted Patient Data Collection records of 45 and Up Study participants (2011)(2012)(2013).Results: Of 493 442 hospitalisations among 267 153 study participants during [2011][2012][2013]18.8% (n = 92 953) had hospital diagnosis codes that were potentially ADR related; 1.1% (n = 5305) had high/very highprobability ADR-related diagnosis codes (causality ratings: A1 and A2); and 2.0% (n = 10 039) had ADR-related external cause codes. Overall, 2.2% (n = 11 082) of cases were classified as including an ADR-based hospitalisation on either external cause codes or high/very high-probability ADR-related diagnosis codes. Hence, adding high/very high-probability ADR-related hospitalisation codes to standard external cause codes alone (Y40-Y59) increased the number of hospitalisations classified as having an ADR-related diagnosis by 10.4%. Only 6.7% of cases with high-probability ADR-related mental symptoms were captured by external cause codes.