Multiple myeloma incidence, mortality, and prevalence estimates and projections, Australia, 1982-2043: a statistical modelling study.

OBJECTIVES: To examine changes in multiple myeloma incidence and mortality rates during 1982-2018, and to estimate its incidence, mortality, and prevalence for 2019-2043. STUDY DESIGN: Population-based statistical modelling study; analysis of and projections based on Australian Institute of Health and Welfare multiple myeloma incidence, mortality, and survival data. SETTING: Australia, 1982-2018 (historical data) and projections to 2043. MAIN OUTCOME MEASURES: Changes in multiple myeloma incidence and mortality rates, 1982-2018, determined by joinpoint regression analysis (age-standardised to 2021 Australian population); projection of rates to 2043 based on age-period-cohort models; estimated 5- and 30-year prevalence of multiple myeloma (modified counting method). RESULTS: The incidence of multiple myeloma increased during 1982-2018 (eg, annual percentage change [APC], 2006-2018, 1.9%; 95% confidence interval [CI], 1.7-2.2%), but the mortality rate declined during 1990-2018 (APC, -0.4%; 95% CI, -0.5% to -0.2%). The age-standardised incidence rate was projected to increase by 14.9% during 2018-2043, from 8.7 in 2018 to 10.0 (95% CI, 9.4-10.7) new cases per 100 000 population in 2043; the mortality rate was projected to decline by 27.5%, from 4.0 to 2.9 (95% CI, 2.6-3.3) deaths per 100 000 population. The annual number of people newly diagnosed with multiple myeloma was estimated to increase by 89.2%, from 2120 in 2018 to 4012 in 2043; the number of deaths from multiple myeloma was projected to increase by 31.7%, from 979 to 1289. The number of people living with multiple myeloma up to 30 years after initial diagnosis was projected to increase by 163%, from 10 288 in 2018 to 27 093 in 2043, including 13 019 people (48.1%) diagnosed during the preceding five years. CONCLUSION: Although the decline in the mortality rate was projected to continue, the projected increases in the incidence and prevalence of multiple myeloma in Australia over the next 25 years indicate that investment in prevention and early detection research, and planning for prolonged treatment and care, are needed.

Multiple myeloma survival in New South Wales, Australia, by treatment era to 2020.

OBJECTIVE: Australia has relatively high multiple myeloma (MM) incidence and mortality rates. Advancements in MM treatment over recent decades have driven improvements in MM survival in high-income countries; however, reporting in Australia is limited. We investigated temporal trends in population-wide MM survival across 3 periods of treatment advancements in New South Wales (NSW), Australia. METHODS: Individuals with an MM diagnosis in the NSW Cancer Registry between 1985 and 2015 with vital follow-up to 2020, were categorized into 3 previously defined treatment eras according to their diagnosis date (1985-1995, chemotherapy only; 1996-2007, autologous stem cell transplantation; and 2008-2015, novel agents including proteasome inhibitors and immunomodulatory drugs). Both relative survival and cause-specific survival according to Fine and Gray's competing risks cumulative incidence function were calculated by treatment era and age at diagnosis. RESULTS: Overall, 11,591 individuals were included in the study, with a median age of 70 years at diagnosis. Five-year relative survival improved over the 36-year (1985-2020) study period (31.0% in 1985-1995; 41.9% in 1996-2007; and 56.1% in 2008-2015). For individuals diagnosed before 70 years of age, the 5-year relative survival nearly doubled, from 36.5% in 1985-1995 to 68.5% in 2008-2015. Improvements for those > 70 years of age were less pronounced between 1985-1995 and 1996-2007; however, significant improvements were observed for those diagnosed in 2008-2015. Similar overall and age-specific patterns were observed for cause-specific survival. After adjustment for gender and age at diagnosis, treatment era was strongly associated with both relative and cause-specific survival (P < 0.0001). CONCLUSIONS: Survival of individuals with MM is improving in Australia with treatment advances. However, older age groups continue to experience poor survival outcomes with only modest improvements over time. Given the increasing prevalence of MM in Australia, the effects of MM treatment on quality of life, particularly in older age, warrant further attention.

Out-of-pocket health care expenses for people with and without cancer, New South Wales, 2020: a cross-sectional study.

OBJECTIVES: To investigate self-reported out-of-pocket health care expenses, both overall and by cost type, for a large population-based sample of Australians, by cancer status and socio-demographic and medical characteristics. STUDY DESIGN: Cross-sectional study. SETTING, PARTICIPANTS: New South Wales residents participating in the 45 and Up Study (recruited aged 45 years or older during 2005-2009) who completed the 2020 follow-up questionnaire; survey responses linked with New South Wales Cancer Registry data. MAIN OUTCOME MEASURES: Proportions of respondents who reported that out-of-pocket health care expenses during the preceding twelve months exceeded $1000 or $10 000; adjusted odds ratios (aORs) for associations with socio-demographic and medical characteristics. RESULTS: Of the 267 357 recruited 45 and Up Study participants, 45 061 completed the 2020 survey (response rate, 53%); 42.7% (95% confidence interval [CI], 42.2-43.1%) reported that overall out-of-pocket health care expenses during the previous year exceeded $1000, including 55.4% (52.1-58.7%) of participants diagnosed in the preceding two years and 44.9% (43.7-46.1%) of participants diagnosed with cancer more than two years ago. After adjustment for socio-demographic factors, out-of-pocket expenses greater than $1000 were more likely to be reported by participants with cancer than by those without cancer (diagnosis in past two years: aOR, 2.06 [95% CI, 1.77-2.40]; diagnosis more than two years ago: aOR, 1.22 [95% CI, 1.15-1.29]). The odds of out-of-pocket expenses exceeding $1000 increased with area-based socio-economic advantage and household income, and were higher for people with private health insurance (v people with Medicare coverage only: aOR, 1.64; 95% CI, 1.53-1.75). Out-of-pocket expenses exceeding $10 000 were also more likely for participants diagnosed with cancer during the past two years (v no cancer: aOR, 3.30; 95% CI, 2.56-4.26). CONCLUSIONS: People diagnosed with cancer during the past two years were much more likely than people without cancer to report twelve-month out-of-pocket health care expenses that exceeded $1000. Out-of-pocket expenses for people with cancer can exacerbate financial strain at a time of vulnerability, and affect health care equity because some people cannot pay for all available treatments.

Impact of weighting on the association between sociodemographic characteristics, health behaviours and cancer, cardiovascular and all-cause mortality in the Australian 45 and Up Study.

BACKGROUND: Weighting can improve study estimate representativeness. We examined the impact of weighting on associations between participants' characteristics and cancer, cardiovascular and all-cause mortality in the Australian 45 and Up Study cohort. METHODS: Raking weighted cohort data to the 2006 Australian population for seven sociodemographic characteristics. Deaths were ascertained via linkage to routinely collected data. Cox's proportional hazards regression quantified associations between 11 sociodemographic and health characteristics and cancer, cardiovascular and all-cause mortality. The ratios of hazard ratios (RHRs) compared unweighted and weighted estimates. RESULTS: Among 195,052 included participants (median follow-up 11.4 years), there were 7200 cancer, 5912 cardiovascular and 21,840 all-cause deaths. Overall, 102/111 (91.9%) weighted HRs did not differ significantly from unweighted HRs (100%, 86.5% and 89.2% of 37 HRs for cancer, cardiovascular and all-cause mortality, respectively). Significant differences included a somewhat stronger association between single/widowed/divorced (versus married/de-facto) and cardiovascular mortality (unweighted HR=1.25 (95%CI:1.18-1.32), weighted HR=1.33 (95%CI:1.24-1.42), RHR=1.06 (95%CI:1.02-1.11)); and between no school certificate/qualification (versus university degree) and all-cause mortality (unweighted HR=1.21 (95%CI:1.15-1.27), weighted HR=1.28 (95%CI:1.19-1.38), RHR=1.06 (95%CI:1.03-1.10)). CONCLUSION: Our results support the generalisability of most estimates of associations in the 45 and Up Study, particularly in relation to cancer mortality. Slight distortion of a few associations with cardiovascular or all-cause mortality were observed.

Pain and its interference with daily living in relation to cancer: a comparative population-based study of 16,053 cancer survivors and 106,345 people without cancer.

BACKGROUND: Pain is a common, debilitating, and feared symptom, including among cancer survivors. However, large-scale population-based evidence on pain and its impact in cancer survivors is limited. We quantified the prevalence of pain in community-dwelling people with and without cancer, and its relation to physical functioning, psychological distress, and quality of life (QoL). METHODS: Questionnaire data from participants in the 45 and Up Study (Wave 2, n = 122,398, 2012-2015, mean age = 60.8 years), an Australian population-based cohort study, were linked to cancer registration data to ascertain prior cancer diagnoses. Modified Poisson regression estimated age- and sex-adjusted prevalence ratios (PRs) for bodily pain and pain sufficient to interfere with daily activities (high-impact pain) in people with versus without cancer, for 13 cancer types, overall and according to clinical, personal, and health characteristics. The relation of high-impact pain to physical and mental health outcomes was quantified in people with and without cancer. RESULTS: Overall, 34.9% (5,436/15,570) of cancer survivors and 31.3% (32,471/103,604) of participants without cancer reported bodily pain (PR = 1.07 [95% CI = 1.05-1.10]), and 15.9% (2,468/15,550) versus 13.1% (13,573/103,623), respectively, reported high-impact pain (PR = 1.13 [1.09-1.18]). Pain was greater with more recent cancer diagnosis, more advanced disease, and recent cancer treatment. High-impact pain varied by cancer type; compared to cancer-free participants, PRs were: 2.23 (1.71-2.90) for multiple myeloma; 1.87 (1.53-2.29) for lung cancer; 1.06 (0.98-1.16) for breast cancer; 1.05 (0.94-1.17) for colorectal cancer; 1.04 (0.96-1.13) for prostate cancer; and 1.02 (0.92-1.12) for melanoma. Regardless of cancer diagnosis, high-impact pain was strongly related to impaired physical functioning, psychological distress, and reduced QoL. CONCLUSIONS: Pain is common, interfering with daily life in around one-in-eight older community-dwelling participants. Pain was elevated overall in cancer survivors, particularly for certain cancer types, around diagnosis and treatment, and with advanced disease. However, pain was comparable to population levels for many common cancers, including breast, prostate and colorectal cancer, and melanoma.

Development and Validation of txSim: A Model of Advanced Lung Cancer Treatment in Australia.

BACKGROUND AND OBJECTIVE: Since 2016, new therapies have transformed the standard of care for lung cancer, creating a need for up-to-date evidence for health economic modelling. We developed a discrete event simulation of advanced lung cancer treatment to provide estimates of survival outcomes and healthcare costs in the Australian setting that can be updated as new therapies are introduced. METHODS: Treatment for advanced lung cancer was modelled under a clinician-specified treatment algorithm for Australia in 2022. Prevalence of lung cancer subpopulations was extracted from cBioPortal and the Sax Institute's 45 and Up Study, a large prospective cohort linked to cancer registrations. All costs were from the health system perspective for the year 2020. Pharmaceutical and molecular diagnostic costs were obtained from public reimbursement fees, while other healthcare costs were obtained from health system costs in the 45 and Up Study. Treatment efficacy was obtained from clinical trials and observational study data. Costs and survival were modelled over a 10-year horizon. Uncertainty intervals were generated with probabilistic sensitivity analyses. Overall survival predictions were validated against real-world studies. RESULTS: Under the 2022 treatment algorithm, estimated mean survival and costs for advanced lung cancer 10 years post-diagnosis were 16.4 months (95% uncertainty interval [UI]: 14.7-18.1) and AU$116,069 (95% UI: $107,378-$124,933). Survival and costs were higher assuming optimal treatment utilisation rates (20.5 months, 95% UI: 19.1-22.5; $154,299, 95% UI: $146,499-$161,591). The model performed well in validation, with good agreement between predicted and observed survival in real-world studies. CONCLUSIONS: Survival improvements for advanced lung cancer have been accompanied by growing treatment costs. The estimates reported here can be used for budget planning and economic evaluations of interventions across the spectrum of cancer control.

Fifty-year forecasts of daily smoking prevalence: can Australia reach 5% by 2030?

OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030. METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed. RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066. CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.

Health services costs for ovarian cancer in Australia: Estimates from the 45 and Up Study.

INTRODUCTION: There have been significant advancements in risk identification and treatment for ovarian cancer over the last decade. However, their impact on health services costs is unclear. This study estimated the direct health system costs (government perspective) for women diagnosed with ovarian cancer in Australia during 2006-2013, as a benchmark prior to opportunities for precision-medicine approaches to treatment, and for health care planning. METHODS: Using cancer registry data, we identified 176 incident ovarian cancers (including fallopian tube and primary peritoneal cancer) in the Australian 45 and Up Study cohort. Each case was matched with four cancer-free controls on sex, age, geography, and smoking history. Costs were derived from linked health records on hospitalisations, subsidised prescription medicines and medical services to 2016. Excess costs for cancer cases were estimated for different phases of care relative to cancer diagnosis. Overall costs for prevalent ovarian cancers in Australia in 2013 were estimated based on 5-year prevalence statistics. RESULTS: At diagnosis, 10% of women had localised disease, 15% regional spread and 70% distant metastasis (5% unknown). The mean excess cost per ovarian cancer case was $40,556 in the initial treatment phase (≤12 months post-diagnosis), $9,514 per annum in the continuing care phase and $49,208 in the terminal phase (up to 12 months before death). Hospital admissions accounted for the greatest proportion of costs during all phases (66%, 52% and 68% respectively). Excess costs were higher for patients diagnosed with distant metastatic disease, particularly during the continuing care phase ($13,814 versus $4,884 for localised/regional disease). The estimated overall direct health services cost of ovarian cancer in 2013 was AUD$99million (4,700 women nationally). CONCLUSION: The excess health system costs of ovarian cancer are substantial. Continued investment in ovarian cancer research, particularly prevention, early detection and more effective personalised treatments is necessary to reduce the burden of disease.

Incidence profile of four major cancers among migrants in Australia, 2005-2014.

PURPOSE: To compare the incidence profile of four major cancers in Australia by place of birth. METHODS: In this retrospective population-based cohort study, the analysis included 548,851 residents diagnosed with primary colorectum, lung, female breast, or prostate cancer during 2005-2014. Incidence rate ratio (IRR) and 95% confidence intervals (CI) were calculated for migrant groups relative to Australian-born. RESULTS: Compared with Australian-born residents, most migrant groups had significantly lower incidence rates for cancers of the colorectum, breast and prostate. The lowest rates of colorectal cancer were among males born in Central America (IRR = 0.46, 95% CI 0.29-0.74) and females born in Central Asia (IRR = 0.38, 95% CI 0.23-0.64). Males born in North-East Asia had the lowest rates of prostate cancer (IRR = 0.40, 95% CI 0.38-0.43) and females born in Central Asia had the lowest rates of breast cancer (IRR = 0.55, 95% CI 0.43-0.70). For lung cancer, several migrant groups had higher rates than Australian-born residents, with the highest rates among those from Melanesia (males IRR = 1.39, 95% CI 1.10-1.76; females IRR = 1.40, 95% CI 1.10-1.78). CONCLUSIONS: This study describes cancer patterns among Australian migrants, which are potentially helpful in understanding the etiology of these cancers and guiding the implementation of culturally sensitive and safe prevention measures. The lower incidence rates observed for most migrant groups may be maintained with continued emphasis on supporting communities to minimize modifiable risk factors such as smoking and alcohol consumption and participation in organized cancer screening programmes. Additionally, culturally sensitive tobacco control measures should be targeted to migrant communities with high lung cancer incidence rates.

Female reproductive and hormonal factors and lung cancer mortality among never-smokers: A prospective cohort study of 287 408 Chinese women.

There is growing, but inconsistent evidence suggesting oestrogen may play a key role in lung cancer development, especially among never-smoking women for whom lung cancer risk factors remain largely elusive. Using the China Kadoorie Biobank, a large-scale prospective cohort with 302 510 women aged 30 to 79 years recruited from 10 regions in China during 2004 to 2008, we assessed the risk of lung cancer death among self-reported never-smoking women who were cancer-free at baseline, in relation to age at menarche, age at menopause, time since menopause, prior use of oral contraceptives (OCP), number of livebirths, breastfeeding and age at first livebirth. Women were followed up to December 31, 2016 with linkage to mortality data. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox regression, adjusting for key confounders including several socio-demographic, environmental and lifestyle factors. Among 287 408 never-smoking women, 814 died from lung cancer with a median follow-up of 10.3 years. Women who had used OCP within 15 years prior to baseline had a significantly higher hazard of lung cancer death compared with never-users: HR = 1.85 (95% CI: 1.14-3.00) and risk increased by 6% with each additional year of use: HR = 1.06 (1.01-1.10). Among parous women, the hazard of lung cancer death increased by 13% with each single livebirth: HR = 1.13 (1.05-1.23); and among post-menopausal women, the risk increased by 2% with each year since menopause: HR = 1.02 (1.01-1.04). These results suggest that reproductive factors which were proxies for lower endogenous oestrogen level, for example, longer duration of OCP use, could play a role in lung cancer development.

Economic impact of using risk models for eligibility selection to the International lung screening Trial.

OBJECTIVES: Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013). MATERIALS AND METHODS: The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in $ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR). RESULTS: Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95 %CI: $4205-$4383). Using the risk model was more cost-effective in females at both a 1.5 % and 1.7 % 6-year risk threshold (INB=$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38). CONCLUSION: The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.

Updated cost-effectiveness analysis of lung cancer screening for Australia, capturing differences in the health economic impact of NELSON and NLST outcomes.

BACKGROUND: A national, lung cancer screening programme is under consideration in Australia, and we assessed cost-effectiveness using updated data and assumptions. METHODS: We estimated the cost-effectiveness of lung screening by applying screening parameters and outcomes from either the National Lung Screening Trial (NLST) or the NEderlands-Leuvens Longkanker Screenings ONderzoek (NELSON) to Australian data on lung cancer risk, mortality, health-system costs, and smoking trends using a deterministic, multi-cohort model. Incremental cost-effectiveness ratios (ICERs) were calculated for a lifetime horizon. RESULTS: The ICER for lung screening compared to usual care in the NELSON-based scenario was AU$39,250 (95% CI $18,150-108,300) per quality-adjusted life year (QALY); lower than the NLST-based estimate (ICER = $76,300, 95% CI $41,750-236,500). In probabilistic sensitivity analyses, lung screening was cost-effective in 15%/60% of NELSON-like simulations, assuming a willingness-to-pay threshold of $30,000/$50,000 per QALY, respectively, compared to 0.5%/6.7% for the NLST. ICERs were most sensitive to assumptions regarding the screening-related lung cancer mortality benefit and duration of benefit over time. The cost of screening had a larger impact on ICERs than the cost of treatment, even after quadrupling the 2006-2016 healthcare costs of stage IV lung cancer. DISCUSSION: Lung screening could be cost-effective in Australia, contingent on translating trial-like lung cancer mortality benefits to the clinic.

Capacity of the 45 and Up Study to mobilise evidence-based improvements in cancer control: lung cancer case study.

OBJECTIVE: Over the 15 years since the 45 and Up Study (the Study) was established, researchers have harnessed its capacity for enabling rigorous, comprehensive investigation of cancer causes, care, and outcomes. For the first time in Australia, the entire cancer-control continuum could be investigated by linking questionnaire data with cancer registry notifications, hospital records, outpatient medical services and prescription medications at scale. Here, we use lung cancer as a case study to demonstrate the Study's potential to improve cancer control. METHOD: Narrative description. RESULTS: Between 2006-2013, approximately 1200 participants in the Study cohort who had no prior history of cancer were diagnosed with lung cancer, allowing the generation of novel, policy- and practice-relevant evidence for tobacco control, screening, and systems of care. The Study produced evidence on the continuing impact of smoking, including that 'light smoking' (1-5 cigarettes/day) is associated with nine times the risk of lung cancer compared to never-smoking; and that 54% of lung cancers could be avoided long-term if all Australians who smoked were to quit. The Study was used to validate a lung cancer screening risk prediction tool, correctly identifying 70% of the participants with a history of smoking who developed lung cancer within a 6-year period as 'high-risk'. Potential inequities in lung cancer care were identified using the Study cohort, including suboptimal levels of radiotherapy utilisation, below benchmark levels of systemic therapy for patients with metastatic disease, and high numbers of emergency department presentations prior to diagnosis. Participants with lung cancer reported poorer quality of life than those with almost any other cancer type, and about 50% reported severe physical functioning limitations. The Study also provided the infrastructure for the first comprehensive report on lung cancer health system costs. LESSONS LEARNT: As a statewide, population-based cohort, the Study provides reliable estimates of cancer risk, health services utilisation, and person-centred outcomes that can inform policy and practice decision making; and has provided the backbone for localising policy-relevant insights from international experience. We have found that the direct involvement of clinicians and policy makers in research design, and engagement with community networks, can yield tractable, policy-relevant, and ultimately impactful scientific insights.

The 45 and Up Study: reflecting on contributions to global evidence using case studies on cardiovascular disease and smoking.

BACKGROUND/OBJECTIVE: To describe the attributes that have underscored the success of the 45 and Up Study (the Study) and demonstrate its value by reflecting on two case studies: our research on socioeconomic inequalities in cardiovascular disease; and the harms of smoking. Type of program or service: The Study is the largest study of healthy ageing in Australia, and one of the biggest in the world; it recruited 267 357 participants aged 45 years and older from NSW, Australia from 2005 to 2009. For more than 15 years, it has provided high-quality evidence on a broad range of public health related issues. We reflect on its value using two research case studies. RESULTS: Four key attributes have enabled the success of the Study: its establishment as a collaborative resource, including early and ongoing engagement with researchers and policy and practice partners; its large scale, which makes it ideally suited to quantify associations between risk factors and health outcomes, including for high priority populations; high quality self-reported survey data; and linkage to routinely collected administrative data, including specialised data. Novel Australian findings on cardiovascular disease (CVD) and smoking illustrate how the Study has contributed to national and international evidence, informing policy and practice. Results on CVD demonstrated individual-level education-related inequalities in CVD incidence and mortality, and greater use of pharmacotherapy for secondary prevention of CVD, in people with low versus high socioeconomic status. In terms of smoking, Study data showed that current smokers have around three times the mortality of never-smokers; that even "light" smoking of <14 cigarettes per day doubles mortality; that quitting is beneficial at any age; that smoking increases the risk of multiple cancer types; and that smoking causes half of deaths in Aboriginal and Torres Strait Islander adults aged 45 years and over and more than one-third of all deaths in the population. This evidence has been used by more than 50 government and non-government organisations, including contributing to legislation, policy and national and international monitoring and reporting. LESSONS LEARNT: The Study has fulfilled a vital role in public health research and practice in Australia, providing locally relevant data to enable research on health issues of importance, including health inequity. Through ongoing partnerships, the Study's data has contributed to international scientific evidence and been used to inform public health policy and practice. It has also been used as a focus for collaboration and capacity building.

Projections of smoking-related cancer mortality in Australia to 2044.

BACKGROUND: While many high-income countries including Australia have successfully implemented a range of tobacco control policies, smoking remains the leading preventable cause of cancer death in Australia. We have projected Australian mortality rates for cancer types, which have been shown to have an established relationship with cigarette smoking and estimated numbers of cancer deaths attributable to smoking to 2044. METHODS: Cancer types were grouped according to the proportion of cases currently caused by smoking: 8%-30% and >30%. For each group, an age-period- cohort model or generalised linear model with cigarette smoking exposure as a covariate was selected based on the model fit statistics and validation using observed data. The smoking-attributable fraction (SAF) was calculated for each smoking-related cancer using Australian smoking prevalence data and published relative risks. RESULTS: Despite the decreasing mortality rates projected for the period 2015-2019 to 2040-2044 for both men and women, the overall number of smoking-related cancer deaths is estimated to increase by 28.7% for men and 35.8% for women: from 138 707 (77 839 men and 60 868 women) in 2015-2019 to 182 819 (100 153 men and 82 666 women) in 2040-2044. Over the period 2020-2044, there will be 254 583 cancer deaths (173 943 men and 80 640 women) directly attributable to smoking, with lung, larynx, oesophagus and oral (comprising lip, oral cavity and pharynx) cancers having the largest SAFs. INTERPRETATION: Cigarette smoking will cause over 250 000 cancer deaths in Australia from 2020 to 2044. Continued efforts in tobacco control remain a public health priority, even in countries where smoking prevalence has substantially declined.

Health utilities for participants in a population-based sample who meet eligibility criteria for lung cancer screening.

INTRODUCTION: Trial-based, risk-targeted lung cancer screening with low-dose computed tomography has been shown to reduce lung cancer mortality but implementation may depend on favourable cost-effectiveness evaluations where quality-adjusted life-years are a key metric. Baseline health utility values for a screening population at high risk of lung cancer are not likely to match age-specific population norms, and utilities derived from screening trials may not be representative of real-world screening populations. We estimated utility values for screening-eligible individuals in a population-based cohort study in Australia. METHODS: Cancer-free participants aged 50-80 years in the New South Wales 45 and Up Study completed the 12-Item Short Form Survey (2010-2011). Mean SF-6D utility values were calculated for 19,991 participants and compared across screening criteria defined by the US Preventive Services Task Force (USPSTF-2021/2013), NELSON trial eligibility, and the PLCOm2012 risk tool. RESULTS: Mean SF-6D utility values were comparable across screening criteria: USPSTF-2021, 0.772 (95%CI, 0.768-0.776); USPSTF-2013, 0.764 (95%CI, 0.759-0.770); NELSON, 0.768 (95%CI, 0.763-0.774), and were each lower than among ineligible participants (0.810-0.814). While there was a decline in utilities with increasing risk of lung cancer as measured with the PLCOm2012 risk tool, mean utility values for those with ≥ 1.51% 6-year risk did not differ to other criteria (0.772, 95%CI, 0.767-0.776). CONCLUSION: Risk criteria are necessary for the efficiency of lung cancer screening programs, but they select populations with lower mean health utilities than population norms. We provide baseline values that can be used in cost-effectiveness evaluations of risk-targeted lung cancer screening.

Large-Scale Population-Based Surveys Linked to Administrative Health Databases as a Source of Data on Health Utilities in Australia.

OBJECTIVES: Large-scale health surveys that contain quality-of-life instruments are a rich source of health utility data for health economic evaluations, especially when linked to routinely collected, administrative health databases. We derived health utility values for a wide range of health conditions using a large Australian cohort study linked to population-wide health databases. METHODS: Short-Form 6-Dimension utility values were calculated for 56 094 adults, aged 47+ years, in the New South Wales 45 and Up Study who completed the Social, Economic, and Environmental Factors survey (2010-2011). Mean utilities were summarized for major health conditions identified through self-report, hospital records, primary cancer notifications, and claims for government-subsidized prescription medicines and medical services. To identify unique associations between health conditions and utilities, beta regression was performed. Utility values were analyzed by time to death using linked death records. RESULTS: Mean Short-Form 6-Dimension utility was 0.810 (95% confidence interval [CI] 0.809-0.811), was age dependent, and was higher in men than women. Utilities for serious health conditions ranged from 0.685 (95% CI 0.652-0.718) for lung cancer to 0.800 (95% CI 0.787-0.812) for melanoma whereas disease-free respondents had a mean of 0.859 (95% CI 0.858-0.861). Most health conditions were independently associated with poorer quality of life. Utility values also declined by proximity to death where participants sampled 6 months before death had a mean score of 0.637 (95% CI 0.613-0.662). CONCLUSIONS: Our data offer a snapshot of the health status of an older Australian population and show that record linkage can enable comprehensive ascertainment of utility values for use in health economic modeling.

Raking of data from a large Australian cohort study improves generalisability of estimates of prevalence of health and behaviour characteristics and cancer incidence.

BACKGROUND: Health surveys are commonly somewhat non-representative of their target population, potentially limiting the generalisability of prevalence estimates for health/behaviour characteristics and disease to the population. To reduce bias, weighting methods have been developed, though few studies have validated weighted survey estimates against generally accepted high-quality independent population benchmark estimates. METHODS: We applied post-stratification and raking methods to the Australian 45 and Up Study using Census data and compared the resulting prevalence of characteristics to accepted population benchmark estimates and separately, the incidence rates of lung, colorectal, breast and prostate cancer to whole-of-population estimates using Standardised Incidence Ratios (SIRs). RESULTS: The differences between 45 and Up Study and population benchmark estimates narrowed following sufficiently-informed raking, e.g. 13.6% unweighted prevalence of self-reported fair/poor overall health, compared to 17.0% after raking and 17.9% from a population benchmark estimate. Raking also improved generalisability of cancer incidence estimates. For example, unweighted 45 and Up Study versus whole-of-population SIRs were 0.700 (95%CI:0.574-0.848) for male lung cancer and 1.098 (95%CI:1.002-1.204) for prostate cancer, while estimated SIRs after sufficiently-informed raking were 0.828 (95%CI:0.684-0.998) and 1.019 (95%CI:0.926-1.121), respectively. CONCLUSION: Raking may be a useful tool for improving the generalisability of exposure prevalence and disease incidence from surveys to the population.

Solid Fuel, Secondhand Smoke, and Lung Cancer Mortality: A Prospective Cohort of 323,794 Chinese Never-Smokers.

Rationale: Household air pollution and secondhand tobacco smoke are known carcinogens for lung cancer, but large-scale estimates of the relationship with lung cancer mortality are lacking. Objectives: Using the large-scale cohort China Kadoorie Biobank, we prospectively investigated associations between these two risk factors and lung cancer death among never-smokers. Methods: The Biobank recruited 512,715 adults aged 30-79 years from 10 regions in China during 2004-2008. Self-reported never-smoking participants were followed up to December 31, 2016, with linkage to mortality data. Total duration of exposure to household air pollution was calculated from self-reported domestic solid fuel use. Exposure to secondhand tobacco smoke was ascertained using exposure at home and/or other places. Hazard ratios and 95% confidence intervals for associations between these two exposures and lung cancer death were estimated using Cox regression, adjusting for key confounders. Measurements and Main Results: There were 979 lung cancer deaths among 323,794 never-smoking participants without a previous cancer diagnosis during 10.2 years of follow-up. There was a log-linear positive association between exposure to household air pollution and lung cancer death, with a 4% increased risk per 5-year increment of exposure (hazard ratio = 1.04; 95% confidence interval = 1.01-1.06; P trend = 0.0034), and participants with 40.1-50.0 years of exposure had the highest risk compared with the never-exposed (hazard ratio = 1.53; 95% confidence interval = 1.13-2.07). The association was largely consistent across various subgroups. No significant association was found between secondhand smoke and lung cancer death. Conclusions: This cohort study provides new prospective evidence suggesting that domestic solid fuel use is associated with lung cancer death among never-smokers.

Evaluating risk factors for lung cancer among never-smoking individuals using two Australian studies.

PURPOSE: Lung cancer (LC) in never-smoking individuals would rank as Australia's eighth most deadly cancer, yet risk factors remain uncertain. We investigated demographic, lifestyle and health-related exposures for LC among never-smoking Australians. METHODS: Using the prospective 45 and Up Study with 267,153 New South Wales (NSW) residents aged ≥ 45 years at recruitment (2006-2009), we quantified the relationship of 20 potential exposures with LC among cancer-free participants at baseline who self-reported never smoking. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) for incident LC were estimated using Cox regression. The NSW Cancer, Lifestyle and Evaluation of Risk (CLEAR) Study, a case-control study including 10,781 NSW residents aged ≥ 18 years (2006-2014), was used to examine 16 potential LC exposures among cancer-free never-smoking participants. Adjusted odds ratios (OR) and 95% CI of LC were estimated using logistic regression. RESULTS: There were 226 LC cases among 132,354 cancer-free 45 and Up Study participants who reported never smoking, with a median follow-up of 5.41 years. The CLEAR Study had 58 LC cases and 1316 cancer-free controls who had never smoked. Analyses of both datasets showed that Asian-born participants had a higher risk of LC than those born elsewhere: cohort, adjusted HR = 2.83 (95% CI 1.64-4.89) and case-control, adjusted OR = 3.78 (1.19-12.05). No significant association with LC was found for other exposures. CONCLUSION: Our findings support the growing evidence that never-smoking, Asian-born individuals are at higher risk of developing LC than those born elsewhere. Ethnicity could be considered when assessing potential LC risk among never-smoking individuals.