Background Improved survival means that cancer is increasingly becoming a chronic disease. Understanding and improving functional outcomes are critical to optimising survivorship. We quantified physical and mental health-related outcomes in people with versus without cancer, according to cancer type. Methods Questionnaire data from an Australian population-based cohort study (45 and Up Study (n = 267,153)) were linked to cancer registration data to ascertain cancer diagnoses up to enrolment. Modified Poisson regression estimated age- and sex-adjusted prevalence ratios (PRs) for adverse person-centred outcomes—severe physical functional limitations (disability), moderate/high psychological distress and fair/poor quality of life (QoL)—in participants with versus without cancer, for 13 cancer types. Results Compared to participants without cancer (n = 244,000), cancer survivors (n = 22,505) had greater disability (20.6% versus 12.6%, respectively, PR = 1.28, 95%CI = (1.25–1.32)), psychological (22.2% versus 23.5%, 1.05 (1.02–1.08)) and poor/fair QoL (15.2% versus 10.2%; 1.28 (1.24–1.32)). The outcomes varied by cancer type, being worse for multiple myeloma (PRs versus participants without cancer for disability 3.10, 2.56–3.77; distress 1.53, 1.20–1.96; poor/fair QoL 2.40, 1.87–3.07), lung cancer (disability 2.81, 2.50–3.15; distress 1.67, 1.46–1.92; poor/fair QoL 2.53, 2.21–2.91) and non-Hodgkin’s lymphoma (disability 1.56, 1.37–1.78; distress 1.20, 1.05–1.36; poor/fair QoL 1.66, 1.44–1.92) and closer to those in people without cancer for breast cancer (disability 1.23, 1.16–1.32; distress 0.95, 0.90–1.01; poor/fair QoL 1.15, 1.05–1.25), prostate cancer (disability 1.11, 1.04–1.19; distress 1.09, 1.02–1.15; poor/fair QoL 1.15, 1.08–1.23) and melanoma (disability 1.02, 0.94–1.10; distress 0.96, 0.89–1.03; poor/fair QoL 0.92, 0.83–1.01). Outcomes were worse with recent diagnosis and treatment and advanced stage. Physical disability in cancer survivors was greater in all population subgroups examined and was a major contributor to adverse distress and QoL outcomes. Conclusions Physical disability, distress and reduced QoL are common after cancer and vary according to cancer type suggesting priority areas for research, and care and support.

In Australia and internationally, there are increasing calls for the use of strengths-based methodologies, to counter the dominant deficit discourse that pervades research, policy, and media relating to Indigenous health and wellbeing. However, there is an absence of literature on the practical application of strengths-based approaches to quantitative research. This paper describes and empirically evaluates a set of strategies to support strengths-based quantitative analysis. A case study about Aboriginal and Torres Strait Islander child wellbeing was used to demonstrate approaches to support strengths-based quantitative analysis, in comparison to the dominant deficit approach of identifying risk factors associated with a negative outcome. Data from Wave 8 (2015) of the Australian Longitudinal Study of Indigenous Children were analysed. The Protective Factors Approach is intended to enable identification of factors protective against a negative outcome, and the Positive Outcome Approach is intended to enable identification of factors associated with a positive health outcome. We compared exposure-outcome associations (prevalence ratios and 95% confidence intervals (CIs), calculated using Poisson regression with robust variance) between the strengths-based and deficit approaches. In this case study, application of the strengths-based approaches retains the identification of statistically significant exposure-outcome associations seen with the standard deficit approach. Strengths-based approaches can enable a more positive story to be told, without altering statistical rigour. For Indigenous research, a strengths-based approach better reflects community values and principles, and it is more likely to support positive change than standard pathogenic models. Further research is required to explore the generalisability of these findings.

Discrimination is a fundamental determinant of health and health inequities. However, despite the high prevalence of discrimination exposure, there is limited evidence specific to Indigenous populations on the link between discrimination and health. This study employs a validated measure to quantify experiences of everyday discrimination in a national sample of Aboriginal and Torres Strait Islander (Australia’s Indigenous peoples) adults surveyed from 2018 to 2020 (≥16 years, n = 8108). It quantifies Prevalence Ratios (PRs) and 95% Confidence Intervals (CIs) for wellbeing outcomes by level of discrimination exposure, and tests if associations vary by attribution of discrimination to Indigeneity. Of the participants, 41.5% reported no discrimination, 47.5% low, and 11.0% moderate-high. Discrimination was more commonly reported by younger versus older participants, females versus males, and those living in remote versus urban or regional areas. Discrimination was significantly associated in a dose-response manner, with measures of social and emotional wellbeing, culture and identity, health behaviour, and health outcomes. The strength of the association varied across outcomes, from a 10–20% increased prevalence for some outcomes (e.g., disconnection from culture (PR = 1.08; 95% CI: 1.03, 1.14), and high blood pressure (1.20; 1.09, 1.32)), to a five-fold prevalence of alcohol dependence (4.96; 3.64, 6.76), for those with moderate-high versus no discrimination exposure. The association was of consistent strength and direction whether attributed to Indigeneity or not—with three exceptions. Discrimination is associated with a broad range of poor wellbeing outcomes in this large-scale, national, diverse cohort of Aboriginal and Torres Strait Islander adults. These findings support the vast potential to improve Aboriginal and Torres Strait Islander peoples’ wellbeing, and to reduce Indigenous-non-Indigenous inequities, by reducing exposure to discrimination.

This study evaluated a community-driven animal health and management program in the remote community of Wadeye, Northern Territory. This evaluation used a pre-post design to assess changes in animal and human health outcomes over a 12-month period of program implementation, from June 2018 to June 2019. The evaluation assessed the program by comparing animal health outcomes before versus one year after program implementation and comparing human health outcomes before versus during the first 12 months of the program. Outcome measures included the desexing status of dogs and cats, body condition and hair score of dogs, and rates of people presenting to the health clinic for a dog bite. Animal health outcomes significantly improved after program implementation. From pre to post program, there was a 77% increase in the prevalence of good body condition score among dogs and a 9% increase in the prevalence of good hair score among dogs, and the prevalence of desexed dogs and cats more than doubled. There was no significant change in the number of people presenting to the health clinic for a dog bite. Consideration on how to further incorporate human and environmental health aspects into the program could be useful for future One Health programs.

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.