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.

Tobacco smoking and risk of 36 cardiovascular disease subtypes: fatal and non-fatal outcomes in a large prospective Australian study.

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

Socioeconomic variation in incidence of primary and secondary major cardiovascular disease events: an Australian population-based prospective cohort study.

BACKGROUND: Cardiovascular disease (CVD) disproportionately affects disadvantaged people, but reliable quantitative evidence on socioeconomic variation in CVD incidence in Australia is lacking. This study aimed to quantify socioeconomic variation in rates of primary and secondary CVD events in mid-age and older Australians. METHODS: Baseline data (2006-2009) from the 45 and Up Study, an Australian cohort involving 267,153 men and women aged ≥ 45, were linked to hospital and death data (to December 2013). Outcomes comprised first event - death or hospital admission - for major CVD combined, as well as myocardial infarction and stroke, in those with and without prior CVD (secondary and primary events, respectively). Cox regression estimated hazard ratios (HRs) for each outcome in relation to education (and income and area-level disadvantage), separately by age group (45-64, 65-79, and ≥ 80 years), adjusting for age and sex, and additional sociodemographic factors. RESULTS: There were 18,207 primary major CVD events over 1,144,845 years of follow-up (15.9/1000 person-years), and 20,048 secondary events over 260,357 years (77.0/1000 person-years). For both primary and secondary events, incidence increased with decreasing education, with the absolute difference between education groups largest for secondary events. Age-sex adjusted hazard ratios were highest in the 45-64 years group: for major CVDs, HR (no qualifications vs university degree) = 1.62 (95% CI: 1.49-1.77) for primary events, and HR = 1.49 (1.34-1.65) for secondary events; myocardial infarction HR = 2.31 (1.87-2.85) and HR = 2.57 (1.90-3.47) respectively; stroke HR = 1.48 (1.16-1.87) and HR = 1.97 (1.42-2.74) respectively. Similar but attenuated results were seen in older age groups, and with income. For area-level disadvantage, CVD gradients were weak and non-significant in older people (> 64 years). CONCLUSIONS: Individual-level data are important for quantifying socioeconomic variation in CVD incidence, which is shown to be substantial among both those with and without prior CVD. Findings reinforce the opportunity for, and importance of, primary and secondary prevention and treatment in reducing socioeconomic variation in CVD and consequently the overall burden of CVD morbidity and mortality in Australia.

Education-related inequalities in cause-specific mortality: first estimates for Australia using individual-level linked census and mortality data.

BACKGROUND: Socioeconomic inequalities in mortality are evident in all high-income countries, and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide the first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons. METHODS: We used Australian Census (2016) linked to 13 months of Death Registrations (2016-17). We estimated relative rates (RR) and rate differences (RD, per 100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high (tertiary) education for individual causes of death (among those aged 25-84 years) and grouped according to preventability (25-74 years), separately by sex and age group, adjusting for age, using negative binomial regression. RESULTS: Among 13.9 M people contributing 14 452 732 person-years, 84 743 deaths occurred. All-cause mortality rates among men and women aged 25-84 years with low education were 2.76 [95% confidence interval (CI): 2.61-2.91] and 2.13 (2.01-2.26) times the rates of those with high education, respectively. We observed inequalities in most causes of death in each age-sex group. Among men aged 25-44 years, relative and absolute inequalities were largest for injuries, e.g. transport accidents [RR = 10.1 (5.4-18.7), RD = 21.2 (14.5-27.9)]). Among those aged 45-64 years, inequalities were greatest for chronic diseases, e.g. lung cancer [men RR = 6.6 (4.9-8.9), RD = 57.7 (49.7-65.8)] and ischaemic heart disease [women RR = 5.8 (3.7-9.1), RD = 20.2 (15.8-24.6)], with similar patterns for people aged 65-84 years. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men. CONCLUSIONS: Australian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.

Workforce participation in relation to cancer diagnosis, type and stage: Australian population-based study of 163,556 middle-aged people.

PURPOSE: To quantify the relationship of cancer diagnosis to workforce participation in Australia, according to cancer type, clinical features and personal characteristics. METHODS: Questionnaire data (2006-2009) from participants aged 45-64 years (n=163,556) from the population-based 45 and Up Study (n=267,153) in New South Wales, Australia, were linked to cancer registrations to ascertain cancer diagnoses up to enrolment. Modified Poisson regression estimated age- and sex-adjusted prevalence ratios (PRs) for non-participation in the paid workforce-in participants with cancer (n=8,333) versus without (n=155,223), for 13 cancer types. RESULTS: Overall, 42% of cancer survivors and 29% of people without cancer were out of the workforce (PR=1.18; 95%CI=1.15-1.21). Workforce non-participation varied substantively by cancer type, being greatest for multiple myeloma (1.83; 1.53-2.18), oesophageal (1.70; 1.13-2.58) and lung cancer (1.68; 1.45-1.93) and moderate for colorectal (1.23; 1.15-1.33), breast (1.11; 1.06-1.16) and prostate cancer (1.06; 0.99-1.13). Long-term survivors, 5 or more years post-diagnosis, had 12% (7-16%) greater non-participation than people without cancer, and non-participation was greater with recent diagnosis, treatment or advanced stage. Physical disability contributed substantively to reduced workforce participation, regardless of cancer diagnosis. CONCLUSIONS: Cancer survivors aged 45-64 continue to participate in the workforce. However, participation is lower than in people without cancer, varying by cancer type, and is reduced particularly around the time of diagnosis and treatment and with advanced disease. IMPLICATIONS FOR CANCER SURVIVORS: While many cancer survivors continue with paid work, participation is reduced. Workforce retention support should be tailored to survivor preferences, cancer type and cancer journey stage.

Tobacco smoking and mortality among Aboriginal and Torres Strait Islander adults in Australia.

BACKGROUND: Despite generally high smoking prevalences, stemming from colonization, the relationship of smoking to mortality has not been quantified reliably in an Indigenous population. We investigate smoking and mortality among Aboriginal and Torres Strait Islander adults in Australia, where current adult daily smoking prevalence is 40.2%. METHODS: A prospective study of 1388 cardiovascular disease- and cancer-free Aboriginal adults aged ≥45 years, of the 267 153 45 and Up Study participants randomly sampled from the New South Wales general population over 2006-09. Questionnaire and mortality data were linked (through the Centre for Health Record Linkage) to mid-2019. Adjusted hazard ratios (called relative risks, RRs) for all-cause mortality-among current- and past- versus never-smokers-were estimated overall, by smoking intensity and by age at cessation. Smoking-attributable fractions and associated deaths were estimated. RESULTS: Over 14 586 person-years' follow-up (median 10.6 years), 162 deaths accrued. Mortality RRs [95% confidence interval (CI)] were 3.90 (2.52-6.04) for current- and 1.95 (1.32-2.90) for past- versus never-smokers, with age heterogeneity. RRs increased with smoking intensity, to 4.29 (2.15-8.57) in current-smokers of ≥25 cigarettes/day. Compared with never-smokers, RRs were 1.48 (0.85-2.57) for those quitting at <45 years of age and 2.21 (1.29-3.80) at 45-54 years. Never-smokers lived an average >10 years longer than current-smokers. Around half of deaths among adults aged ≥45 years were attributable to smoking, exceeding 10 000 deaths in the past decade. CONCLUSIONS: In this population, >80% of never-smokers would survive to 75 years, versus ∼40% of current-smokers. Quitting at all ages examined had substantial benefits versus continuing smoking; those quitting before age 45 years had mortality risks similar to never-smokers. Smoking causes half of deaths in older Aboriginal and Torres Strait Islander adults; Indigenous tobacco control must receive increased priority.

Education inequalities in adult all-cause mortality: first national data for Australia using linked census and mortality data.

BACKGROUND: National linked mortality and census data have not previously been available for Australia. We estimated education-based mortality inequalities from linked census and mortality data that are suitable for international comparisons. METHODS: We used the Australian Bureau of Statistics Death Registrations to Census file, with data on deaths (2011-2012) linked probabilistically to census data (linkage rate 81%). To assess validity, we compared mortality rates by age group (25-44, 45-64, 65-84 years), sex and area-inequality measures to those based on complete death registration data. We used negative binomial regression to quantify inequalities in all-cause mortality in relation to five levels of education ['Bachelor degree or higher' (highest) to 'no Year 12 and no post-secondary qualification' (lowest)], separately by sex and age group, adjusting for single year of age and correcting for linkage bias and missing education data. RESULTS: Mortality rates and area-based inequality estimates were comparable to published national estimates. Men aged 25-84 years with the lowest education had age-adjusted mortality rates 2.20 [95% confidence interval (CI): 2.08‒2.33] times those of men with the highest education. Among women, the rate ratio was 1.64 (1.55‒1.74). Rate ratios were 3.87 (3.38‒4.44) in men and 2.57 (2.15‒3.07) in women aged 25-44 years, decreasing to 1.68 (1.60‒1.76) in men and 1.44 (1.36‒1.53) in women aged 65-84 years. Absolute education inequalities increased with age. One in three to four deaths (31%) was associated with less than Bachelor level education. CONCLUSIONS: These linked national data enabled valid estimates of education inequality in mortality suitable for international comparisons. The magnitude of relative inequality is substantial and similar to that reported for other high-income countries.

Factors related to receipt of non-cancer-related transurethral prostatectomy: findings from a large prospective study of 106†769 middle-aged and older Australian men.

BACKGROUND: Transurethral prostatectomy (TURP) is a common surgical intervention for chronic lower urinary tract symptoms (LUTS). Little large-scale evidence exists on factors related to receipt of non-cancer-related TURP. METHODS: A prospective study of men aged ≥45 years participating in the 45 and Up Study, a large Australian cohort study, without prior prostatectomy and/or bowel/genital/urinary-tract cancer; questionnaire data were linked to hospitalisations and deaths. HRs for TURP were estimated in relation to multiple factors, adjusting for confounders. RESULTS: There were 3416 incident TURPs among 106 769 men (median follow-up 5.8 years), with rates of 1.8, 5.3, 9.1 and 11.4/1000 person-years for ages 45-54, 55-64, 65-74 and ≥75 years, respectively. Age-adjusted rates of TURP varied markedly according to baseline LUTS from 2.2/1000 person-years with no/mild symptoms to 30.7/1000 person-years with severe symptoms. Annual household income ≥$70 000 versus <$20 000, having private health insurance and living in major cities were associated with higher TURP rates; there were no significant differences according to baseline diabetes, stroke, high blood pressure or cardiovascular disease. Men reporting severe versus no physical functioning limitation, high versus low psychological distress or poor versus excellent self-rated health were 36-51% more likely to undergo procedures overall, but were 24-37% less likely to undergo procedures following additional adjustment for need (baseline LUTS). CONCLUSIONS: TURP rates were most strongly related to baseline LUTS and age, consistent with appropriate health services targeting. Lower TURP rates in men experiencing socioeconomic disadvantage and with poor health/disability, after accounting for baseline LUTS, suggest inequity and factors such as frailty and risks related to surgery.

Categorising major cardiovascular disease hospitalisations from routinely collected data.

UNLABELLED: Objectives and importance of the study: Routine hospital administrative data provide an important source of information about cardiovascular disease (CVD) for health statistics reporting, health services and research. How such conditions are classified and grouped has implications for the use and interpretation of these data. International Classification of Diseases (ICD) diagnosis codes from hospital data collections are often used to classify CVD, but there is little published evidence on the most appropriate ways to use these codes to categorise CVD in a way that maximises the usefulness of hospital data for reporting and research. In particular, ICD codes under 'Diseases of the circulatory system' (I00-I99) are often grouped together into a general CVD category. However, this category is heterogeneous and combines common severe atherosclerotic and thrombotic CVDs (such as myocardial infarction and pulmonary embolism) with common, less severe and pathologically dissimilar conditions (such as varicose veins and haemorrhoids). In addition, hospital data collections contain a range of data fields, including those relating to primary and additional diagnoses and those relating to procedures. All of these have the potential to contribute valuable information on CVD. This paper proposes a pragmatic approach to using ICD diagnosis codes and procedure codes to capture major atherosclerotic and arteriovenous thromboembolic and related CVD. METHODS: We reviewed the ICD diagnosis codes and procedure codes and developed an algorithm for classifying and categorising major CVD diagnoses. This approach was then applied to linked hospitalisation data from individuals participating in the 45 and Up Study, a cohort study of 267 153 New South Wales residents aged 45 and over, to investigate the implications of the proposed approach for quantifying CVD. RESULTS: Large differences were observed in the numbers of events in grouped CVD outcomes, depending on the methods used. CONCLUSIONS: In cases where the reporting and research interest relates to incident disease, it may be appropriate to prioritise specific disease categories and pathological homogeneity.

Relationship between lifestyle and health factors and severe lower urinary tract symptoms (LUTS) in 106,435 middle-aged and older Australian men: population-based study.

BACKGROUND: Despite growing interest in prevention of lower urinary tract symptoms (LUTS) through better understanding of modifiable risk factors, large-scale population-based evidence is limited. OBJECTIVE: To describe risk factors associated with severe LUTS in the 45 and Up Study, a large cohort study. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional analysis of questionnaire data from 106,435 men aged ≥ 45 years, living in New South Wales, Australia. OUTCOME MEASURES AND STATISTICAL ANALYSIS: LUTS were measured by a modified version of the International Prostate Symptom Score (m-IPSS). The strength of association between severe LUTS and socio-demographic, lifestyle and health-related factors was estimated, using logistic regression to calculate odds ratios, adjusted for a range of confounding factors. RESULTS: Overall, 18.3% reported moderate, and 3.6% severe, LUTS. Severe LUTS were more common among men reporting previous prostate cancer (7.6%), total prostatectomy (4.9%) or having part of the prostate removed (8.2%). After excluding men with prostate cancer or prostate surgery, the prevalence of moderate-severe LUTS in the cohort (n = 95,089) ranged from 10.6% to 35.4% for ages 45-49 to ≥ 80; the age-related increase was steeper for storage than voiding symptoms. The adjusted odds of severe LUTS decreased with increasing education (tertiary qualification versus no school certificate, odds ratio (OR = 0.78 (0.68-0.89))) and increasing physical activity (high versus low, OR = 0.83 (0.76-0.91)). Odds were elevated among current smokers versus never-smokers (OR = 1.64 (1.43-1.88)), obese versus healthy-weight men (OR = 1.27 (1.14-1.41)) and for comorbid conditions (e.g., heart disease versus no heart disease, OR = 1.36 (1.24-1.49)), and particularly for severe versus no physical functional limitation (OR = 5.17 (4.51-5.93)). CONCLUSIONS: LUTS was associated with a number of factors, including modifiable risk factors, suggesting potential targets for prevention.