Longitudinal analysis of respiratory outcomes among bauxite exposed workers in Western Australia.

INTRODUCTION: Occupational exposure to bauxite is common in the aluminium industry but little is known about the associated health effects. This study investigates respiratory health in relation to respirable bauxite dust exposure longitudinally over a 13 year period. METHODS: An inception cohort study recruited 91 male bauxite miners and 363 male alumina refinery workers. Annual measurements of respiratory symptoms and lung function were made. Cumulative exposure to bauxite was derived from job histories and air monitoring data. Mixed-effects modeling was used. RESULTS: No associations were found between cumulative bauxite exposure and respiratory symptoms or lung function. However, when analysis was restricted to the first three rounds, FEV1 was significantly lower in all exposure groups than in those unexposed but with no significant trend. CONCLUSION: Increasing exposure to bauxite dust in the aluminum industry was not associated with respiratory symptoms or consistent decrements in lung function.

Is potroom asthma due more to sulphur dioxide than fluoride? An inception cohort study in the Australian aluminium industry.

OBJECTIVES: Although an asthma-like syndrome has been recognised in aluminium smelter workers for over 70 years, the causal agent has been difficult to identify. METHODS: An inception cohort study was conducted at two Australian aluminium smelters where 446 employees participated over a period of 9 years. Cumulative exposures between interviews were estimated from job histories using a task exposure matrix based on measurements in the smelters. Participants completed an MRC respiratory questionnaire, spirometry and methacholine challenge test. Data were analysed with generalised estimating equations to allow for repeated measurements of each participant. RESULTS: Wheeze and chest tightness, the two symptoms most closely related to asthma, showed associations with occupational exposures. SO(2) exposure was significantly associated with these symptoms, bronchial hyper-responsiveness (BHR) to methacholine (a feature of asthma), airflow limitation (reduced forced expiratory volume in 1 second/forced vital capacity ratio) and longitudinal decline in lung function. Fluoride exposure was associated with the same outcomes, but less strongly. Inhalable dust and the benzene soluble fraction (BSF) were associated with symptoms of asthma and BHR. Although many of the exposures were highly correlated, further modelling suggested that of the known respiratory irritants, SO(2) was more likely than fluoride to be primarily responsible for the symptoms observed. Fluoride, inhalable dust and SO(2) were the most important airborne contaminants associated with effects on lung function. CONCLUSIONS: The observed effects were detected at contaminant levels within occupational exposure standards, so further reductions are required, particularly in SO(2) exposures.

Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study.

BACKGROUND: Traditional methods for analyzing clinical and epidemiological cohort study data have been focused on the first occurrence of a health outcome. However, in many situations, recurrent event data are frequently observed. It is inefficient to use methods for the analysis of first events to analyse recurrent event data. METHODS: We applied several semi-parametric proportional hazards models to analyze the risk of recurrent myocardial infarction (MI) events based on data from a very large randomized placebo-controlled trial of cholesterol-lowering drug. The backward selection procedure was used to select the significant risk factors in a model. The best fitting model was selected using the log-likelihood ratio test, Akaike Information and Bayesian Information Criteria. RESULTS: A total of 8557 persons were included in the LIPID study. Risk factors such as age, smoking status, total cholesterol and high density lipoprotein cholesterol levels, qualifying event for the acute coronary syndrome, revascularization, history of stroke or diabetes, angina grade and treatment with pravastatin were significant for development of both first and subsequent MI events. No significant difference was found for the effects of these risk factors between the first and subsequent MI events. The significant risk factors selected in this study were the same as those selected by the parametric conditional frailty model. Estimates of the relative risks and 95% confidence intervals were also similar between these two methods. CONCLUSIONS: Our study shows the usefulness and convenience of the semi-parametric proportional hazards models for the analysis of recurrent event data, especially in estimation of regression coefficients and cumulative risks.

Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study.

AIMS: The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD. METHODS AND RESULTS: Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration. CONCLUSION: A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.

Overview of risk prediction models in cardiovascular disease research.

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.

Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia.

In epidemiologic studies, researchers often need to establish a nonlinear exposure-response relation between a continuous risk factor and a health outcome. Furthermore, periodic interviews are often conducted to take repeated measurements from an individual. The authors proposed to use fractional polynomial models to jointly analyze the effects of 2 continuous risk factors on a health outcome. This method was applied to an analysis of the effects of age and cumulative fluoride exposure on forced vital capacity in a longitudinal study of lung function carried out among aluminum workers in Australia (1995-2003). Generalized estimating equations and the quasi-likelihood under the independence model criterion were used. The authors found that the second-degree fractional polynomial models for age and fluoride fitted the data best. The best model for age was robust across different models for fluoride, and the best model for fluoride was also robust. No evidence was found to suggest that the effects of smoking and cumulative fluoride exposure on change in forced vital capacity over time were significant. The trend 1 model, which included the unexposed persons in the analysis of trend in forced vital capacity over tertiles of fluoride exposure, did not fit the data well, and caution should be exercised when this method is used.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study.

BACKGROUND: Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals. METHODS: We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. RESULTS: A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR = 0.71, 95% CI 0.60-0.83). However, the treatment effect was not significant in women due to smaller sample size (HR = 0.75, 95% CI 0.51-1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p = 0.24 for men and p = 0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6-4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4-13.6). LIMITATIONS: The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors. CONCLUSIONS: The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Negative beliefs about low back pain are associated with high pain intensity and high level disability in community-based women.

BACKGROUND: Although previous studies have investigated beliefs about back pain in clinical and employed populations, there is a paucity of data examining the beliefs of the broader community. We aimed to characterize the beliefs that community-dwelling women have about back pain and its consequences, and to determine whether those with varying levels of pain intensity and disability differ in their beliefs. METHODS: 542 community-dwelling women, aged 24 to 80 years, were recruited from a research database. Participants completed a self-administered questionnaire that included detailed demographic information, the Chronic Pain Grade Questionnaire (CPG) and the Back Beliefs Questionnaire (BBQ). The CPG examined individuals' levels of pain intensity and disability, and the BBQ investigated their beliefs about back pain and its consequences. RESULTS: 506 (93.4%) women returned the study questionnaire. The mean (SD) BBQ score for the cohort was 30.7 (6.0), indicating generally positive beliefs about back pain. However, those women with high intensity pain and high level disability had a mean (SD) score of 28.5 (5.7) and 24.8 (5.7) respectively, which reflects greater negativity about back pain and its consequences. There was an association between negative beliefs and high pain intensity (OR = 0.94 (95% CI: 0.90, 0.99), p = 0.01) and high level disability (OR = 0.93 (95% CI: 0.89, 0.97), p = 0.001), after adjusting for confounders. CONCLUSION: This study highlights that although women living in the community were generally positive about back pain, subgroups of women with high pain intensity and high level disability were identified who had more pessimistic views. While a causal relationship cannot be inferred from these cross-sectional data, the results suggest that negative beliefs individuals have about back pain may be predictive of chronic, disabling spinal pain.

Familial and genomic analyses of postural changes in systolic and diastolic blood pressure.

The physiological adaptation to the erect posture involves integrated neural and cardiovascular responses that might be determined by genetic factors. We examined the familial- and individual-specific components of variance for postural changes in systolic and diastolic blood pressure in 767 volunteer nuclear adult families from the Victorian Family Heart Study. In 274 adult sibling pairs, we made a genome-wide scan using 400 markers for quantitative trait loci linked with the postural changes in systolic and diastolic pressures. Overall, systolic pressure did not change on standing, but there was considerable variation in this phenotype (SD=8.1 mm Hg). Familial analyses revealed that 25% of the variance of change in systolic pressure was attributable to genetic factors. In contrast, diastolic pressure increased by 6.3 mm Hg (SD=7.0 mm Hg) on standing and there was no evidence of contributory genetic factors. Multipoint quantitative genome linkage mapping suggested evidence (Z=3.2) of linkage of the postural change in systolic pressure to chromosome 12 but found no genome-wide evidence of linkage for the change in diastolic pressure. These findings suggest that genetic factors determine whether systolic pressure decreases or increases when one stands, possibly as the result of unidentified alleles on chromosome 12. The genetics of postural changes in systolic blood pressure might reflect the general buffering function of the baroreflex; thereby, the predisposition to sudden decreases or increases in systolic pressure might cause postural hypotension or vessel wall disruption, respectively.

Estimation of reporting delay and suicide incidence in Hong Kong.

This paper uses a semi-parametric method to examine the reporting delay distribution in suicides in Hong Kong reporting system. The data arise from a rightly truncated situation in which only suicide cases registered before a specific time are known to have occurred; otherwise they are not recorded in the known death files even if they have occurred. It is shown that the poisoning-related suicide deaths have a longer reporting delay than other suicide methods. By modelling the reporting delay function, a Horvitz-Thompson-type estimator is suggested to adjust for reporting delay and to provide a more timely estimate of the suicide incidences for monitoring the suicide problem in Hong Kong. Based on these analyses, we recommended a suitable cut-off date to collect suicide cases occurring in the previous year and reported before this date in Hong Kong.

Regressive logistic and proportional hazards disease models for within-family analyses of measured genotypes, with application to a CYP17 polymorphism and breast cancer.

Various statistical methods have been proposed to evaluate associations between measured genetic variants and disease, including some using family designs. For breast cancer and rare variants, we applied a modified segregation analysis method that uses the population cancer incidence and population-based case families in which a mutation is known to be segregating. Here we extend the method to a common polymorphism, and use a regressive logistic approach to model familial aggregation by conditioning each individual on their mother's breast cancer history. We considered three models: 1) class A regressive logistic model; 2) age-of-onset regressive logistic model; and 3) proportional hazards familial model. Maximum likelihood estimates were calculated using the software MENDEL. We applied these methods to data from the Australian Breast Cancer Family Study on the CYP17 5'UTR T-->C MspA1 polymorphism measured for 1,447 case probands, 787 controls, and 213 relatives of case probands found to have the CC genotype. Breast cancer data for first- and second-degree relatives of case probands were used. The three methods gave consistent estimates. The best-fitting model involved a recessive inheritance, with homozygotes being at an increased risk of 47% (95% CI, 28-68%). The cumulative risk of the disease up to age 70 years was estimated to be 10% or 22% for a CYP17 homozygote whose mother was unaffected or affected, respectively. This analytical approach is well-suited to the data that arise from population-based case-control-family studies, in which cases, controls and relatives are studied, and genotype is measured for some but not all subjects.

Antihypertensive treatments obscure familial contributions to blood pressure variation.

The linkage and association between inherent blood pressure and underlying genotype is potentially confounded by antihypertensive treatment. We estimated blood pressure variance components (genetic, shared environmental, individual-specific) in 767 adult volunteer families by using a variety of approaches to adjusting blood pressure of the 244 subjects (8.2%) receiving antihypertensive medications. The additive genetic component of variance for systolic pressure was 73.9 mm Hg(2) (SE, 8.8) when measured pressures (adjusted for age by gender within each generation) were used but fell to 61.4 mm Hg(2) (SE, 8.0) when treated subjects were excluded. When the relevant 95th percentile values were substituted for treated systolic pressures, the additive genetic component was 81.9 mm Hg(2) (SE, 9.5), but individual adjustments in systolic pressure ranged from -53.5 mm Hg to +64.5 mm Hg (mean, +17.2 mm Hg). Instead, when 10 mm Hg was added to treated systolic pressure, the additive genetic component rose to 86.6 mm Hg(2) (SE, 10.1). Similar changes were seen in the shared environment component of variance for systolic pressure and for the combined genetic and shared environmental (ie, familial) components of diastolic pressure. There was little change in the individual-specific variance component across any of the methods. Therefore, treated subjects contribute important information to the familial components of blood pressure variance. This information is lost if treated subjects are excluded and obscured by treatment effects if unadjusted measured pressures are used. Adding back an appropriate increment of pressure restores familial components, more closely reflects the pretreatment values, and should increase the power of genomic linkage and linkage disequilibrium analyses.

Bivariate variance-component analysis, with application to systolic blood pressure and total cholesterol levels in the Framingham Heart Study.

BACKGROUND: The correlations between systolic blood pressure (SBP) and total cholesterol levels (CHOL) might result from genetic or environmental factors that determine variation in the phenotypes and are shared by family members. Based on 330 nuclear families in the Framingham Heart Study, we used a multivariate normal model, implemented in the software FISHER, to estimate genetic and shared environmental components of variation and genetic and shared environmental correlation between the phenotypes. The natural logarithm of the phenotypes measured at the last visit in both Cohort 1 and 2 was used in the analyses. The antihypertensive treatment effect was corrected before adjustment of the systolic blood pressure for age, sex, and cohort. RESULTS: The univariate correlation coefficient was statistically significant for sibling pairs and parent-offspring pairs, but not significant for spouse pairs. In the bivariate analysis, the cross-trait correlation coefficients were not statistically significant for all relative pairs. The shared environmental correlation was statistically significant, but the genetic correlation was not significant. CONCLUSION: There is no significant evidence for a close genetic correlation between systolic blood pressure and total cholesterol levels. However, some shared environmental factors may determine the variation of both phenotypes.

Genes and family environment explain correlations between blood pressure and body mass index.

The correlations between systolic blood pressure (SBP) and diastolic blood pressure (DBP), and between SBP and body mass index (BMI), might result from genetic or environmental factors that determine variation in 2 or more phenotypes and are shared by family members. In 767 adult nuclear families (n=2912 individuals, including 66 pairs of monozygotic twins and 84 pairs of dizygotic twins), we used a multivariate normal model and the software FISHER to estimate genetic and environmental components of variation and covariation. Mean phenotypes were adjusted for age, gender, and generation, and for antihypertensive treatment. Genetic and shared family environmental factors accounted for 46% and 31% of total variance in SBP, respectively. Adjustment of SBP for DBP reduced considerably both the additive genetic (86.7 to 21.0) and shared environmental (59.7 to 21.0) components of variance. Smaller reductions in genetic (86.7 to 84.9) and shared environmental (59.7 to 51.1) components were observed after adjustment of SBP for BMI. For SBP and DBP, the correlation between the effects of genes was 1.00 and between shared environmental effects was 0.52. For SBP and BMI the correlations were 0.30 for genetic and 0.22 for shared environmental effects. Our findings suggest that the same genes and many of the same family environmental factors determine variation in both SBP and DBP. In contrast, SBP and BMI share genetic and family environmental determinants to a lesser degree. These observations are relevant to multifactorial cardiovascular risk reduction based on genetic and family environmental approaches.

The BRCA2 372 HH genotype is associated with risk of breast cancer in Australian women under age 60 years.

The BRCA2 N372H nonconservative amino acid substitution polymorphism appears to affect fetal survival in a sex-dependent manner, and the HH genotype was found to be associated with a 1.3-fold risk of breast cancer from pooling five case-control studies of Northern European women. We investigated whether the BRCA2 N372H polymorphism was associated with breast cancer in Australian women using a population-based case-control design. The BRCA2 372 genotype was determined in 1397 cases under the age of 60 years at diagnosis of a first primary breast cancer and in 775 population-sampled controls frequency matched for age. Case-control analyses and comparisons of genotype distributions were conducted using logistic regression. All of the statistical tests were two-tailed. The HH genotype was independent of age and family history of breast cancer within cases and controls, and was more common in cases (9.2% versus 6.5%). It was associated with an increased risk of breast cancer, 1.47-fold unadjusted (95% confidence interval, 1.05-2.07; P = 0.02), and 1.42-fold (95% confidence interval, 1.00-2.02; P = 0.05) after adjusting for measured risk factors. This effect was still evident after excluding women with any non-Caucasian ancestry or the 33 cases known to have inherited a mutation in BRCA1 or BRCA2, and would explain approximately 3% of breast cancer. The BRCA2 N372H polymorphism appears to be associated with a modest recessively inherited risk of breast cancer in Australian women. This result is consistent with the findings for Northern European women.