BackgroundSevere air pollution generated by forest fires is becoming an increasingly frequent public health management problem. We measured the association between forest fire smoke events and hospital emergency department (ED) attendances in Sydney from 1996–2007.MethodsA smoke event occurred when forest fires caused the daily citywide average concentration of particulate matter (PM10 or PM2.5) to exceed the 99th percentile of the entire study period. We used a time-stratified case-crossover design and conditional logistic regression models adjusted for meteorology, influenza epidemics, and holidays to estimate odds ratios (OR) and 95% confidence intervals (CI) for ED attendances on event days compared with non-event days for all non-trauma ED attendances and selected cardiorespiratory conditions.ResultsThe 46 validated fire smoke event days during the study period were associated with same day increases in ED attendances for all non-trauma conditions (1.03, 95% CI 1.02, 1.04), respiratory conditions (OR 1.07, 95% CI 1.04, 1.10), asthma (OR 1.23, 95% CI 1.15, 1.30), and chronic obstructive pulmonary disease (OR 1.12, 95% CI 1.02, 1.24). Positive associations persisted for one to three days after the event. Ischaemic heart disease ED attendances were increased at a lag of two days (OR 1.07, 95% CI 1.01, 1.15) while arrhythmias had an inverse association at a lag of two days (OR 0.91, 95% CI 0.83, 0.99). In age-specific analyses, no associations present in children less than 15 years of age for any outcome, although a non-significant trend towards a positive association was seen with childhood asthma. A further association between smoke event and heart failure attendances was present for the 15–65 year age group, but not older adults at a lag of two days (OR 1.37 95% CI 1.05, 1.78).ConclusionSmoke events were associated with an immediate increase in presentations for respiratory conditions and a lagged increase in attendances for ischaemic heart disease and heart failure. Respiratory impacts were either absent or considerably attenuated in those <15 years. Similar to previous studies we found inconsistent associations between fire smoke and cardiovascular diseases. Better characterisation of the spectrum of population health risks is needed to guide public heath responses to severe smoke events as this exposure becomes increasingly common with global climate changeElectronic supplementary materialThe online version of this article (doi:10.1186/1476-069X-13-105) contains supplementary material, which is available to authorized users.

BackgroundIn contrast to aetiological associations, there is little empirical evidence for generalising health service use associations from cohort studies. We compared the health service use of cohort study participants diagnosed with bowel or lung cancer to the source population of people diagnosed with these cancers in New South Wales (NSW), Australia to assess the representativeness of health service use of the cohort study participants.MethodsPopulation-based cancer registry data for NSW residents aged ≥45 years at diagnosis of bowel or lung cancer were linked to the 45 and Up Study, a NSW population-based cohort study (N~ 267,000). We measured hospitalisation, emergency department (ED) attendance and all-cause survival, and risk factor associations with these outcomes using administrative data for cohort study participants and the source population. We assessed bias in prevalence and risk factor associations using ratios of relative frequency (RRF) and relative odds ratios (ROR), respectively.ResultsPeople from major cities, non-English speaking countries and with comorbidites were under-represented among cohort study participants diagnosed with bowel (n = 1837) or lung (n = 969) cancer by 20–50%. Cohort study participants had similar hospitalisation and ED attendance compared with the source population. One-year survival after major surgical resection was similar, but cohort study participants had up to 25% higher post-diagnosis survival (lung cancer 3-year survival: RRF = 1.24, 95% confidence interval 1.12,1.37). Except for area-based socioeconomic position, risk factors associations with health service use measures and survival appeared relatively unbiased.ConclusionsAbsolute measures of health service use and risk factor associations in a non-representative sample showed little evidence of bias. Non-comparability of risk factor measures of cohort study participants and non-participants, such as area-based socioeconomic position, may bias estimates of risk factor associations. Primary and outpatient care outcomes may be more vulnerable to bias.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0537-3) contains supplementary material, which is available to authorized users.

BackgroundAboriginal people are known to be under-recorded in routinely collected datasets in Australia. This study examined methods for enhancing the reporting of cancer incidence among Aboriginal people using linked data methodologies.MethodsInvasive cancers diagnosed in New South Wales (NSW), Australia, in 2010–2014 were identified from the NSW Cancer Registry (NSWCR). The NSWCR data were linked to the NSW Admitted Patient Data Collection, the NSW Emergency Department Data Collection and the Australian Coordinating Register Cause of Death Unit Record File. The following methods for enhancing the identification of Aboriginal people were used: ‘ever-reported’, ‘reported on most recent record’, ‘weight of evidence’ and ‘multi-stage median’. The impact of these methods on the number of cancer cases and age-standardised cancer incidence rates (ASR) among Aboriginal people was explored.ResultsOf the 204,948 cases of invasive cancer, 2703 (1.3%) were recorded as Aboriginal on the NSWCR. This increased with enhancement methods to 4184 (2.0%, ‘ever’), 3257 (1.6%, ‘most recent’), 3580 (1.7%, ‘weight of evidence’) and 3583 (1.7%, ‘multi-stage median’). Enhancement was generally greater in relative terms for males, people aged 25–34 years, people with cancers of localised or unknown degree of spread, people living in urban areas and areas with less socio-economic disadvantage. All enhancement methods increased ASRs for Aboriginal people. The weight of evidence method increased the overall ASR by 42% for males (894.1 per 100,000, 95% CI 844.5–945.4) and 27% for females (642.7 per 100,000, 95% CI 607.9–678.7). Greatest relative increases were observed for melanoma and prostate cancer incidence (126 and 63%, respectively). ASRs for prostate and breast cancer increased from below to above the ASRs of non-Aboriginal people with enhancement of Aboriginal status.ConclusionsAll data linkage methods increased the number of cancer cases and ASRs for Aboriginal people. Enhancement varied by demographic and cancer characteristics. We considered the weight of evidence method to be most suitable for population-level reporting of cancer incidence among Aboriginal people. The impact of enhancement on disparities in cancer outcomes between Aboriginal and non-Aboriginal people should be further examined.

Little has been published on the diagnostic and referral pathway for lung cancer in Australia. This study set out to quantify general practitioner (GP) and lung specialist attendance and diagnostic imaging in the lead-up to a diagnosis of non-small cell lung cancer (NSCLC) and identify common pathways to diagnosis in New South Wales (NSW), Australia. We used linked health data for participants of the 45 and Up Study (a NSW population-based cohort study) diagnosed with NSCLC between 2006 and 2012. Our main outcome measures were GP and specialist attendances, X-rays and computed tomography (CT) scans of the chest and lung cancer-related hospital admissions. Among our study cohort (N = 894), 60% (n = 536) had ≥4 GP attendances in the 3 months prior to diagnosis of NSCLC, 56% (n = 505) had GP-ordered imaging (chest X-ray or CT scan), 39% (N = 349) attended a respiratory physician and 11% (N = 102) attended a cardiothoracic surgeon. The two most common pathways to diagnosis, accounting for one in three people, included GP and lung specialist (respiratory physician or cardiothoracic surgeon) involvement. Overall, 25% of people (n = 223) had an emergency hospital admission. For 14% of people (N = 129), an emergency hospital admission was the only event identified on the pathway to diagnosis. We found little effect of remoteness of residence on access to services. This study identified a substantial proportion of people with NSCLC being diagnosed in an emergency setting. Further research is needed to establish whether there were barriers to the timely diagnosis of these cases.

ObjectiveTo determine a practical approach for deriving life expectancy estimates in Australian New South Wales local government areas which display a large diversity in population sizes.DesignPopulation-based study utilising mortality and estimated residential population data.Setting153 local government areas in New South Wales, Australia.Outcome measuresKey performance measures of Chiang II, Silcocks, adjusted Chiang II and Bayesian random effects model methodologies of life expectancy estimation including agreement analysis of life expectancy estimates and comparison of estimate SEs.ResultsChiang II and Silcocks methods produced almost identical life expectancy estimates across a large range of population sizes but calculation failures and excessively large SEs limited their use in small populations. A population of 25 000 or greater was required to estimate life expectancy with SE of 1 year or less using adjusted Chiang II (a composite of Chiang II and Silcocks methods). Data aggregation offered some remedy for extending the use of adjusted Chiang II in small populations but reduced estimate currency. A recently developed Bayesian random effects model utilising the correlation in mortality rates between genders, age groups and geographical areas markedly improved the precision of life expectancy estimates in small populations.ConclusionsWe propose a hybrid approach for the calculation of life expectancy using the Bayesian random effects model in populations of 25 000 or lower permitting the precise derivation of life expectancy in small populations. In populations above 25 000, we propose the use of adjusted Chiang II to guard against violations of spatial correlation, to benefit from a widely accepted method that is simpler to communicate to local health authorities and where its slight inferior performance compared with the Bayesian approach is of minor practical significance.