Geoff Morgan

Associations between Outdoor Air Pollution and Daily Mortality in Brisbane, Australia

The results of several studies have indicated significant associations between daily mortality and air pollution, with little evidence of a threshold. In the current study, the authors examined daily mortality during the period 1987-1993 for the Brisbane region, which is the fastest-growing urban region in Australia (annual average concentration of particulate matter less than 10 microm in diameter = 27 microg/m3, maximum hourly sulfur dioxide level = 60 ppb, and maximum daily ozone hourly level = 118 ppb). The authors conducted a general estimating equation analysis, and they used autoregressive Poisson models for daily mortality to examine associations with air pollution variables. The authors used research methods developed in the Air Pollution on Health, European Approach (APHEA), project to control confounding effects of weather and temporal trends. The air pollutants examined included particulate pollution (measured by nephelometry [bsp data]), sulfur dioxide, ozone, and nitrogen dioxide. The results indicated that the associations between total daily mortality and particulate levels found in studies in the United States and other countries may be applicable in Brisbane, Australia. Ozone levels were also associated significantly with total daily mortality. There was little evidence of interaction between the ozone effects (mainly in summer) and particulates or with sulfur dioxide and nitrogen dioxide. The associations between pollutants (ozone, bsp) and daily mortality were significant only for individuals who were older than 65 y of age; positive associations were also found with cardiovascular disease categories, and the regression coefficients--when significant--were higher than those for total mortality. The results indicated a possible threshold for ozone levels, but a similar result for particulate levels was not apparent.

The short-term effects of air pollution on daily mortality in four Australian cities

Objective: To examine the short‐term health effects of air pollution on daily mortality in four Australian cities (Brisbane, Melbourne, Perth and Sydney), where more than 50% of Australians reside.

Influence of socioeconomic and cultural factors on rural health.

OBJECTIVE To provide a framework for investigating the influence of socioeconomic and cultural factors on rural health. DESIGN Discussion paper. RESULTS Socioeconomic and cultural factors have long been thought to influence an individuals health. We suggest a framework for characterising these factors that comprises individual-level (e.g. individual socioeconomic status, sex, race) and neighbourhood-level dimensions (population composition, social environment, physical environment) operating both independently and through interaction. Recent spatial research suggests that in rural communities, socioeconomic disadvantage and indigenous status are two of the greatest underlying influences on health status. However, rural communities also face additional challenges associated with access to, and utilisation of, health care. The example is given of procedural angiography for individuals with an acute coronary event. CONCLUSIONS Socioeconomic and cultural factors specific to rural Australia are key influences on the health of residents. These range from individual-level factors, such as rural stoicism, poverty and substance use norms, to neighbourhood-level social characteristics, such as lack of services, migration out of rural areas of younger community members weakening traditionally high levels of social cohesion, and to environmental factors, such as climate change and access to services.

Small area estimation of sparse disease counts using shared component models-application to birth defect registry data in New South Wales, Australia

In the field of disease mapping, little has been done to address the issue of analysing sparse health datasets. We hypothesised that by modelling two outcomes simultaneously, one would be able to better estimate the outcome with a sparse count. We tested this hypothesis utilising Bayesian models, studying both birth defects and caesarean sections using data from two large, linked birth registries in New South Wales from 1990 to 2004. We compared four spatial models across seven birth defects: spina bifida, ventricular septal defect, OS atrial septal defect, patent ductus arteriosus, cleft lip and or palate, trisomy 21 and hypospadias. For three of the birth defects, the shared component model with a zero-inflated Poisson (ZIP) extension performed better than other simpler models, having a lower deviance information criteria (DIC). With spina bifida, the ratio of relative risk associated with the shared component was 2.82 (95% CI: 1.46-5.67). We found that shared component models are potentially beneficial, but only if there is a reasonably strong spatial correlation in effect for the study and referent outcomes.

The study design and methodology for the ARCHER study - adolescent rural cohort study of hormones, health, education, environments and relationships

BackgroundAdolescence is characterized by marked psychosocial, behavioural and biological changes and represents a critical life transition through which adult health and well-being are established. Substantial research confirms the role of psycho-social and environmental influences on this transition, but objective research examining the role of puberty hormones, testosterone in males and oestradiol in females (as biomarkers of puberty) on adolescent events is lacking. Neither has the tempo of puberty, the time from onset to completion of puberty within an individual been studied, nor the interaction between age of onset and tempo. This study has been designed to provide evidence on the relationship between reproductive hormones and the tempo of their rise to adult levels, and adolescent behaviour, health and wellbeing.Methods/DesignThe ARCHER study is a multidisciplinary, prospective, longitudinal cohort study in 400 adolescents to be conducted in two centres in regional Australia in the State of New South Wales. The overall aim is to determine how changes over time in puberty hormones independently affect the study endpoints which describe universal and risk behaviours, mental health and physical status in adolescents. Recruitment will commence in school grades 5, 6 and 7 (10–12 years of age). Data collection includes participant and parent questionnaires, anthropometry, blood and urine collection and geocoding. Data analysis will include testing the reliability and validity of the chosen measures of puberty for subsequent statistical modeling to assess the impact over time of tempo and onset of puberty (and their interaction) and mean-level repeated measures analyses to explore for significant upward and downward shifts on target outcomes as a function of main effects.DiscussionThe strengths of this study include enrollment starting in the earliest stages of puberty, the use of frequent urine samples in addition to annual blood samples to measure puberty hormones, and the simultaneous use of parental questionnaires.

Reconceptualising risk: Perceptions of risk in rural and remote maternity service planning

OBJECTIVE to explore perceptions and examples of risk related to pregnancy and childbirth in rural and remote Australia and how these influence the planning of maternity services. DESIGN data collection in this qualitative component of a mixed methods study included 88 semi-structured individual and group interviews (n=102), three focus groups (n=22) and one group information session (n=17). Researchers identified two categories of risk for exploration: health services risk (including clinical and corporate risks) and social risk (including cultural, emotional and financial risks). Data were aggregated and thematically analysed to identify perceptions and examples of risk related to each category. SETTING fieldwork was conducted in four jurisdictions at nine sites in rural (n=3) and remote (n=6) Australia. PARTICIPANTS 117 health service employees and 24 consumers. MEASUREMENTS AND FINDINGS examples and perceptions relating to each category of risk were identified from the data. Most medical practitioners and health service managers perceived clinical risks related to rural birthing services without access to caesarean section. Consumer participants were more likely to emphasise social risks arising from a lack of local birthing services. KEY CONCLUSIONS our analysis demonstrated that the closure of services adds social risk, which exacerbates clinical risk. Analysis also highlighted that perceptions of clinical risk are privileged over social risk in decisions about rural and remote maternity service planning. IMPLICATIONS FOR PRACTICE a comprehensive analysis of risk that identifies how social and other forms of risk contribute to adverse clinical outcomes would benefit rural and remote people and their health services. Formal risk analyses should consider the risks associated with failure to provide birthing services in rural and remote communities as well as the risks of maintaining services.

Diagnosing Potentially Preventable Hospitalisations (DaPPHne): protocol for a mixed-methods data-linkage study

Introduction Rates of potentially preventable hospitalisations (PPH) are used as a proxy measure of effectiveness of, or access to community-based health services. The validity of PPH as an indicator in Australia has not been confirmed. Available evidence suggests that patient-related, clinician-related and systems-related factors are associated with PPH, with differences between rural and metropolitan settings. Furthermore, the proportion of PPHs which are actually preventable is unknown. The Diagnosing Potentially Preventable Hospitalisations study will determine the proportion of PPHs for chronic conditions that are deemed preventable and identify potentially modifiable factors driving these, in order to develop effective interventions to reduce admissions and improve measures of health system performance. Methods and analysis This mixed methods data linkage study of approximately 1000 eligible patients with chronic PPH admissions to one metropolitan and two regional hospitals over 12 months will combine data from multiple sources to assess the: extent of preventability of chronic PPH admissions; validity of the Preventability Assessment Tool (PAT) in identifying preventable admissions; factors contributing to chronic PPH admissions. Data collected from patients (quantitative and qualitative methods), their general practitioners, hospital clinicians and hospital records, will be linked with routinely collected New South Wales (NSW) Admitted Patient Data Collection, the NSW Registry of Births, Death and Marriages death registration and Australian Bureau of Statistics mortality data. The validity of the PAT will be assessed by determining concordance between clinician assessment and that of a ‘gold standard’ panel. Multivariable logistic regression will identify the main predictor variables of admissions deemed preventable, using study-specific and linked data. Ethics and dissemination The NSW Population and Health Services Research Ethics Committee granted ethical approval. Dissemination mechanisms include engagement of policy stakeholders through a project Steering Committee, and the production of summary reports for policy and clinical audiences in addition to peer-review papers.

THE EFFECTS OF LOW LEVEL AIR POLLUTION ON DAILY MORTALITY AND HOSPITAL ADMISSIONS IN SYDNEY, AUSTRALIA, 1994 TO 2000: ISEE-571

Introduction: PM levels in Sydney are low compared with most cities where time series studies of the acute effects of air pollution have been conducted. The 50th and 90th percentiles of daily average PM10 (particulate matter [pounds] 10mm) in Sydney are 16 and 26 mg/m3 respectively, and 8 and 14 mg/m3 for PM2.5 (particulate matter [pounds] 2.5mm). Time series studies in Sydney in the early 1990s demonstrated associations between particles (measured by light scatter using integrating nephelometers), nitrogen dioxide and ozone with daily mortality and hospital admissions. Light scatter (BSP) is considered to be a proxy measure for PM2.5. From 1994 air pollution data in Sydney is available from a larger number of monitoring sites and includes daily data on PM10 (measured by TEOM). Daily PM2.5 data (measured by TEOM) is available from 1997. Methods: We investigate associations between the three ambient particulate measures (BSP, PM2.5 and PM10) and the gaseous pollutants (nitrogen dioxide and ozone) with daily mortality and hospital admissions for all ages and the elderly (65+years) in the Sydney metropolitan area from 1994 to 2000 using time series analysis controlling for a range of confounders. We conducted sensitivity analyses to investigate the effect of different modeling approaches including the use of various methods for smoothing long term and seasonal trends, and weather parameters (penalized splines compared with loess and natural splines). Results: All three particulate measures (PM2.5, PM10 and BSP) were associated with all cause mortality, all cardiovascular mortality and all respiratory mortality. The magnitude of the PM2.5 effects were generally larger than those of BSP and PM10. Nitrogen dioxide was also associated with all cause mortality, all cardiovascular mortality and all respiratory mortality. Ozone was weakly associated with respiratory mortality. All three particulate measures were associated with all cardiac, IHD (ischaemic heart disease) and respiratory admissions, and weakly associated with COPD (chronic obstructive pulmonary disease). The magnitude of the effect for PM2.5 and BSP were generally larger than those for PM10. Nitrogen dioxide was also associated with all cardiac, IHD, all respiratory and COPD hospital admissions. Conclusions: The relatively low levels of particulate air pollution in Sydney were consistently associated with both daily mortality and hospital admissions. These particulate associations were generally strongest for fine particles (ie: PM2.5 and BSP) compared with PM10, and persist even at the relatively low particulate levels seen in Sydney, indicating no threshold concentrations are present. These results are consistent with the international literature. Nitrogen dioxide is also consistently associated with both daily mortality and hospital admissions. Further investigation is required to determine the effects of these specific pollutants, compared to the air pollution mix in Sydney.

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

&NA; Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land‐use regression (LUR). Satellite‐based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid‐2004). We investigated whether contemporary satellite‐based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO2) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back‐extrapolate year‐2006 NO2 predictions: (1) ‘do nothing’ (i.e., use the year‐2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year‐2006, following national average patterns in 2006–2011), and; (3) adjust year‐2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R2 and mean‐square error R2 (MSE‐R2), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE‐R2 = 31%) and 80% (2003; MSE‐R2 = 78%) of spatial variability in NO2 in a given year, and 76% (MSE‐R2 = 72%) averaged over 1990–2005. We conclude that simple methods for back‐extrapolating prior to year‐2006 yield valid historical NO2 estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite‐based LUR has a potential role to play in long‐term exposure assessment, even in the absence of historical predictor data. HighlightsWe assessed how well a year‐2006 satellite‐based LUR model captures historical NO2.We used three methods to estimate annual mean NO2 during 1990–2005.We measured their performance using standard LUR validation techniques.Back‐extrapolated 2006 levels captured up to 76% of spatial variability (90–05).

The short-term effects of air pollution on respiratory admissions: A meta-analysis for 4 Australian cities

This poster complements the paper in this area (A description of the SPIRT study for Brisbane, Melbourne, Perth and Sydney: Simpson et al) and details all the results referred in summary in the paper on the short-term health effects of air pollution on respiratory admissions in four Australian cities-Brisbane, Melbourne, Perth and Sydney. The study used a protocol similar to that used in Europe (Air Pollution and Health: A European Approach-APHEA) to examine the associations between health outcomes, such as daily mortality and daily hospital admissions counts, and air pollutants. This poster details all the results for the period 1996-1999 of a meta-analysis for the four cities for the acute health impacts of the pollutants-fine particles, nitrogen dioxide, and ozone. The meta-analyses often show statistically significant differences between the cities indicating the results can be quite different in different cities. Fine particles (as measured by nephelometery) and nitrogen dioxide both have a significant impact on cardiac admissions (14-65 years, greater than 65 years) and IHD admissions (all ages, > 65 years), while ozone has a significant impact on cardiac admissions (14-65 years, greater than 65 years) (although the results are sensitive to how temperature is used in the models). The sensitivity of the results to different statistical methods used in the meta-analyses have been carried out using a combination of three approaches: generalised additive models (GAM) using the S Plus statistical package and loess smoothing, generalised additive models using the S Plus package and natural spiines, and penalised splines using the R statistical package. The results for all methods are presented and for periods of lags of days 0-3 for all pollutants.