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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.

Additional file 1. : Brief Summary of Consumer Engagement Plan.

Additional file 3. : Brief review of person-centred outcomes and cancer.

Additional file 2: Table S1. Number of cases grouped under “other” cancers. Table S2. Prevalence of and age-, sex- and education- adjusted prevalence ratios (PRs) for adverse person-centred outcomes according to joint categories of physical functioning limitations and cancer. Figure S1. Prevalence of moderate/high distress by cancer type and time since diagnosis. Figure S2. Prevalence of moderate/high distress by cancer type and stage. Figure S3. Prevalence of moderate/high distress by cancer type and recent treatment. Figure S4. Prevalence of poor/fair self-rated health by cancer type and time since diagnosis; Figure S5. Prevalence of poor/fair self-rated health by cancer type and stage. Figure S6. Prevalence of poor/fair self-rated health by cancer type and recent treatment; Figure S7. Prevalence of poor/fair self-rated quality of life by cancer type and time since diagnosis. Figure S8. Prevalence of poor/fair self-rated quality of life by cancer type and stage. Figure S9. Prevalence of poor/fair self-rated quality of life by cancer type and recent treatment. Figure S10. Prevalence of high psychological distress by cancer type. Figure S11. Prevalence of moderate/severe physical functioning limitations by cancer type. Figure S12. Prevalence of and age- and sex- adjusted prevalence ratios (PR) for adverse person-centred outcomes by cancer type: analyses restricted to those without severe physical functioning limitations.

Capture–recapture methods (also referred to as ‘multiple-record systems’) have been widely used in enumerating human populations in the fields of epidemiology and public health. In this article, we introduce latent class models into multiple-record systems to account for unobserved heterogeneity in the population. Two approaches, the full and the conditional likelihood, are proposed to estimate the unknown population abundance. We also suggest rules to diagnose identifiability of the proposed latent class models. The methodologies are illustrated by two real examples: the first is to count the undercount of homelessness in the Adelaide central business district, and the second concerns the incidence of diabetes in a small Italian town. Refereed/Peer-reviewed

Thesis (PhD)--University of South Australia, 2009 The estimation of human population size is important for decision making and planning. It is not uncommon that statistical techniques are employed towards this purpose. A recent advance in the counting of missing human populations is the capture-recapture approach which utilizes duplicate information from several incomplete lists to estimate the number of people otherwise unobserved. Capture-recapture methods originated in the estimation of animal populations in wildlife biology, and have been widely used to count hidden or hard-to-reach populations in the areas of epidemiology and public health. The use of multiple lists gives rise to two main issues in the capture-recapture framework, namely list dependence and heterogeneity; the latter problem can be further classified as observed and unobserved heterogeneity. The literature is well developed when it concerns separately modelling these problems. However, we believe that the literature can still be improved when it comes to a joint modelling of these problems. This thesis provides some methods which can simultaneously control the effects due to list dependence and heterogeneity. First, we introduce a binomial latent model which focuses on the heterogeneity aspect and use covariates to control the observed heterogeneity within capture-recapture data; the unobserved heterogeneity is accounted for by means of latent classes. The covariates effects can be modelled either in the latent class probabilities or the capture probabilities. In this particular approach, the modelling is designed so that the covariates affect the latent class probabilities. We then extend this approach to include list dependence by employing a multinomial latent class model in which the lists effects are incorporated within the capture probabilities. Second, we start with a model which can account for list dependence and unobserved heterogeneity by means of multinomial latent class modelling. Similarly, the lists effects are modelled in the capture probabilities. We then extend the model to include the covariates effects. Here, the modelling is set up in such a way that the covariates affect the capture probabilities. This is thus an alternative methodology to the one above where we use the covariates within the latent classes. It is to be noted that our modelling can involve both discrete and continuous covariates and this is a significant benefit of our methods. We employ the maximum likelihood estimation technique and the Expectation-Maximization (EM) algorithm for maximization purposes. We work with the conditional likelihood function which has the advantage that it can perform estimation based only on the captured individuals and does not require information about the whole population. This is particulary suitable when covariate modelling is involved as the covariate information for the uncaptured individuals is not available. At the same time, this advantage allows the conditional likelihood approach to be computationally less tedious than the full likelihood approach when it comes to running the EM algorithm. The difficulty of standard errors computations in latent class modelling is overcome by using an approximation to the observed Information matrix rather than directly evaluating the Information matrix. The methods are illustrated by means of both simulations and real examples concerning the undercount of homeless people in a city in Australia and diabetes patients in a town in Italy.