Trend estimation of sub-national level daily smoking prevalence by age and sex in Australia.

INTRODUCTION: Despite that the smoking prevalence has considerably declined in Australia after successful public health strategies over many decades, smoking is still the leading cause of preventable diseases and death in Australia. These declines have not occurred consistently across all geographical-demographic domains. In order to provide an evidence base for monitoring the trend towards the goal of reducing smoking across all domains in Australia, this study aims to estimate trends of smoking prevalence for small domains cross-classified by seven age groups (18-24, 25-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), two genders, and eight states and territories over twenty years (2001-2021). METHODS: Direct estimates of smoking prevalence for the target small domains were calculated from the micro-data of the Australian National Health Surveys conducted in seven rounds during 2001-2021. The obtained direct estimates were then used as input for developing time-series models expressed in a hierarchical Bayesian structure as a form of small-area estimation. The developed models borrow cross-sectional, temporal, and spatial strength in such a way that they can interpolate smoking levels in the non-survey years for all detailed level small domains. Smoothed trends of smoking prevalence for higher aggregation levels are obtained by aggregation of the detailed level trend predictions. RESULTS: Model-based small area estimators provide consistent and reasonable smoothed trends at both detailed and higher aggregation levels. Results show that the national-level trend exhibits a steeper linear decline over the study period, from 24% in 2001 to 12% in 2021, with a considerable gender difference of around 5% over the period, with males reporting a higher prevalence. Improved model-based estimates at the state level and by age also show steady declines in trends except for the Northern Territory (still above 20%) and older age groups 60-69 and ≥70 years (declining trends remain stable after 2012). CONCLUSIONS: The findings of the study identify the geographical-demographic groups that had poor improvement over the period 2001-2021, and are still behind the target of achieving lower smoking prevalence. These, in turn, will help health researchers and policymakers deliver targeted programs to the most vulnerable, enabling the nation to meet its health goals in a timely way.

Gaining further insights into the COVID-19 pandemic in Australia: Evidence using capture-recapture methods.

Objective: We re-examined the reported number of COVID-19 cases in Australia and across its states during the first wave of the pandemic. We provided estimates of the total number of cases, adjusted for under-reporting. Methods: Publicly available data sourced from Australian governments at federal, state and territory levels included records on cumulative confirmed COVID-19 cases and cumulative deaths occurring in Australia and across its states on a daily basis. Lower bound and upper bound estimates of the total number of COVID-19 cases in Australia and across its states, that included the undetected cases that have not been recorded, were estimated. Results: On January 25, 2020, Australia recorded its first 4 cases of COVID-19 and the first death occurred on March 3, 2020. On April 1, 2020, 4864 cases had been reported with 21 deaths. Our estimation showed that on April 1, 2020, the minimum and maximum number of COVID-19 cases in Australia were in fact 10,160 (95 % CI: 9781-10,538) and 21,748 (95 % CI: 21,607-22,014) respectively. We estimated that the total number of cases were at least twice and at most four times the observed cases recorded. These differences were also found at the state level where in New South Wales there was a minimum and maximum of 207 and 447 cases in total for every 100 reported cases, while in Victoria these figures were much lower at 157 and 265 respectively for every 100 reported cases. Conclusion: Case ascertainment during the pandemic is known to have been underestimated due to difficulties in testing and contact tracing, amongst others. Capture-recapture methods provided a measure of the gap between the official number of cases recorded and the actual number during the first wave of the pandemic.

Trends in chronic childhood undernutrition in Bangladesh for small domains.

Chronic childhood undernutrition, known as stunting, is an important population health problem with short- and long-term adverse outcomes. Bangladesh has made strides to reduce chronic childhood undernutrition, yet progress is falling short of the 2030 Sustainable Development Goals targets. This study estimates trends in age-specific chronic childhood undernutrition in Bangladesh's 64 districts during 1997-2018, using underlying direct estimates extracted from seven Demographic and Health Surveys in the development of small area time-series models. These models combine cross-sectional, temporal, and spatial data to predict in all districts in both survey and non-survey years. Nationally, there has been a steep decline in stunting from about three in five to one in three children. However, our results highlight significant inequalities in chronic undernutrition, with several districts experiencing less pronounced declines. These differences are more nuanced at the district-by-age level, with only districts in more socio-economically advantaged areas of Bangladesh consistently reporting declines in stunting across all age groups.

Geostatistical modelling of child undernutrition in developing countries using remote-sensed data: evidence from Bangladesh and Ghana demographic and health surveys.

Childhood chronic undernutrition, known as stunting, remains a critical public health problem globally. Unfortunately while the global stunting prevalence has been declining over time, as a result of concerted public health efforts, there are areas (notably in sub-Saharan Africa and South Asia) where progress has stagnated. These regions are also resource-poor, and monitoring progress in the fight against chronic undernutrition can be problematic. We propose geostatistical modelling using data from existing demographic surveys supplemented by remote-sensed information to provide improved estimates of childhood stunting, accounting for spatial and non-spatial differences across regions. We use two study areas-Bangladesh and Ghana-and our results, in the form of prevalence maps, identify communities for targeted intervention. For Bangladesh, the maps show that all districts in the south-eastern region are identified to have greater risk of stunting, while in Ghana the greater northern region had the highest prevalence of stunting. In countries like Bangladesh and Ghana with limited resources, these maps can be useful diagnostic tools for health planning, decision making and implementation.

Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh.

District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators - stunting, wasting and underweight - have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay-Herriot (MFH) model which accounts for the underlying correlation among the undernutrition indicators. Direct estimates (DIR) of the target indicators and their variance-covariance matrices calculated from the 2019 Bangladesh Multiple Indicator Cluster Survey microdata have been used as input for developing univariate Fay-Herriot (UFH), bivariate Fay-Herriot (BFH) and MFH models. The comparison of the various model-based estimates and their relative standard errors with the corresponding direct estimates reveals that the MFH estimator provides unbiased estimates with more accuracy than the DIR, UFH and BFH estimators. The MFH model-based district level estimates of stunting, wasting and underweight range between 16 and 43%, 15 and 36%, and 6 and 13% respectively. District level bivariate maps of undernutrition indicators show that districts in north-eastern and south-eastern parts are highly exposed to either form of undernutrition, than the districts in south-western and central parts of the country. In terms of the number of undernourished children, millions of children affected by either form of undernutrition are living in densely populated districts like the capital district Dhaka, though undernutrition indicators (as a proportion) are comparatively lower. These findings can be used to target districts with a concurrence of multiple forms of undernutrition, and in the design of urgent intervention programs to reduce the inequality in child undernutrition at the localised district level.

Population-level contribution of interpersonal discrimination to psychological distress among Australian Aboriginal and Torres Strait Islander adults, and to Indigenous-non-Indigenous inequities: cross-sectional analysis of a community-controlled First Nations cohort study.

BACKGROUND: International and population-specific evidence identifies elevated psychological distress prevalence among those experiencing interpersonal discrimination. We aim to quantify the potential whole-of-population contribution of interpersonal discrimination to psychological distress prevalence and Indigenous-non-Indigenous gaps in Australia. METHODS: We did a cross-sectional analysis of data from Mayi Kuwayu: the National Study of Aboriginal and Torres Strait Islander Wellbeing. Baseline surveys were completed between June 8, 2018, and Sept 28, 2022. We analysed responses from participants who were aged 18 years or older at survey completion, whose surveys were processed between Oct 1, 2018, and May 1, 2021. Sample weights were developed on the basis of national population benchmarks. We measured everyday discrimination using an eight-item measure modified from the Everyday Discrimination Scale and classified experiences as racial discrimination if participants attributed these experiences to their Indigeneity. Psychological distress was measured using a validated, modified Kessler-5 scale. Applying logistic regression, we calculated unadjusted odds ratios (ORs), to approximate incident rate ratios (IRRs), for high or very high psychological distress in relation to everyday discrimination and everyday racial discrimination across age-gender strata. Population attributable fractions (PAFs), under the hypothetical assumption that ORs represent causal relationships, were calculated using these ORs and population-level exposure prevalence. These PAFs were used to quantify the contribution of everyday racial discrimination to psychological distress gaps between Indigenous and non-Indigenous adults. FINDINGS: 9963 survey responses were eligible for inclusion in our study, of which we analysed 9951 (99·9%); 12 were excluded due to responders identifying as a gender other than man or woman (there were too few responses from this demographic to be included as a category in stratified tables or adjusted analyses). The overall prevalence of psychological distress was 48·3% (95% CI 47·0-49·6) in those experiencing everyday discrimination compared with 25·2% (23·8-26·6) in those experiencing no everyday discrimination (OR 2·77 [95% CI 2·52-3·04]) and psychological distress prevalence was 49·0% (95% CI 47·3-50·6) in those experiencing everyday racial discrimination and 31·8% (30·6-33·1) in those experiencing no everyday racial discrimination (OR 2·06 [95% CI 1·88-2·25]. Overall, 49·3% of the total psychological distress burden among Aboriginal and Torres Strait Islander adults could be attributable to everyday discrimination (39·4-58·8% across strata) and 27·1% to everyday racial discrimination. Everyday racial discrimination could explain 47·4% of the overall gap in psychological distress between Indigenous and non-Indigenous people (40·0-60·3% across strata). INTERPRETATION: Our findings show that interpersonal discrimination might contribute substantially to psychological distress among Aboriginal and Torres Strait Islander adults, and to inequities compared with non-Indigenous adults. Estimated PAFs include contributions from social and health disadvantage, reflecting contributions from structural racism. Although not providing strictly conclusive evidence of causality, this evidence is sufficient to indicate the psychological harm of interpersonal discrimination. Findings add weight to imperatives to combat discrimination and structural racism at its core. Urgent individual and policy action is required of non-Indigenous people and colonial structures, directed by Aboriginal and Torres Strait Islander peoples. FUNDING: National Health and Medical Research Council of Australia, Ian Potter Foundation, Australian Research Council, US National Institutes of Health, and Sierra Foundation.

Residential proximity to a fertility clinic is independently associated with likelihood of women having ART and IUI treatment.

STUDY QUESTION: Is geographic proximity to a fertility clinic associated with the likelihood of women of reproductive age undertaking different forms of medically assisted fertility treatment? SUMMARY ANSWER: After adjusting for socioeconomic status (SES) and other confounders including a proxy for the need for infertility treatment, women who lived within 15 km of a fertility clinic were 21% more likely to undergo ART treatment and 68% more likely to undergo IUI treatment than those who lived further than 60 km away. WHAT IS KNOWN ALREADY: In most countries, patients living outside of metropolitan areas are more likely to be more socio-economically disadvantaged and to have less equitable access to healthcare. However, how a woman's residential proximity to fertility clinics predicts utilization of high-cost/high-technology treatment (ART) and low-cost/low-technology treatment (IUI) is limited, and whether socio-economic disadvantage explains much of the hypothesized lower utilization is unknown. Australia's universal insurance scheme provides supportive reimbursement for almost all ART and IUI treatment regardless of age or number of cycles, providing a unique setting to investigate disparities in access to infertility treatment. STUDY DESIGN, SIZE, DURATION: National population-based observation study of ART and IUI treatment utilization by women across socio-economic gradients and Australian residential locations between August 2015 and December 2017. PARTICIPANTS/MATERIALS, SETTING, METHODS: Universal insurance claims information on female patients who underwent ART or IUI were provided by Services Australia, comprising 67 670 female patients who accessed 162 795 ART treatments, and 10 211 female patients who accessed 19 615 IUI treatments over a 29-month period. Incidence rates by SES and proximity to fertility clinics were calculated to describe the number of women undergoing at least one ART or IUI treatment cycle per 1000 women of reproductive age (25-44). Treatment frequencies were calculated to describe the average number of ART or IUI treatment cycles per woman of reproductive age who had undergone at least one ART or IUI treatment during the study period. Poisson regression analyses were used to estimate the independent effect on accessibility to infertility treatment by geographic proximity (based on small area locations) to the closest fertility clinic after adjusting for SES, childbearing delay, remoteness area, and marital status. MAIN RESULTS AND THE ROLE OF CHANCE: On average, 19.1 women per 1000 women of reproductive age underwent at least one fresh or frozen ART cycle, with an average 2.3 ART cycles each, while 3.0 women per 1000 women of reproductive age received at least one IUI cycle, with an average of 1.6 IUI cycles each. After adjusting for SES and other confounders including a proxy for the need for infertility treatment, women who lived within 15 km of a fertility clinic were 21% more likely to undergo ART treatment and 68% more likely to undergo IUI than those who lived over 60 km away. Regardless of geographic location, there was a steady and independent gradient in access to ART treatment based on increasing SES, with women residing in the most advantaged residential quartile having a 37% higher rate of receiving ART treatment compared to those in the most disadvantaged quartile. The negative effect of social disadvantage on ART use became more pronounced as distance from a fertility clinic grew, indicating that the barriers to access to ART care caused by distance were further compounded by the level of socioeconomic advantage of the women's residential location. In contrast, socioeconomic status did not modify the likelihood of using IUI over and above the distance from a fertility clinic. In relation to IUI treatment, differences in utilization by SES disappeared after adjusting for geographic proximity to a fertility clinic, childbearing delay, remoteness area, and marital status. LIMITATIONS, REASONS FOR CAUTION: Information is aggregated by small geographic areas and it therefore may not reflect individual characteristics. Australia provides partial but comparably supportive reimbursement for both ART and IUI through its universal healthcare system and thus the results may not be fully generalizable to other settings. WIDER IMPLICATIONS OF THE FINDINGS: Residential proximity to a fertility clinic is a persistent barrier to accessing ART and IUI treatment, regardless of SES, even in countries characterized by supportive public funding, such as Australia. SES is less of a barrier to accessing IUI than ART, presumably driven by the lower cost and fewer clinic visits required with IUI treatment. Safe and effective fertility treatment should be available to all women regardless of where they live. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Australian National University Research scholarship and by the Higher Degree Research Fee Merit Scholarship. The authors have no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Prevalence of child undernutrition measures and their spatio-demographic inequalities in Bangladesh: an application of multilevel Bayesian modelling.

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.

Risk of severe illness from COVID-19 among Aboriginal and Torres Strait Islander adults: the construct of 'vulnerable populations' obscures the root causes of health inequities.

OBJECTIVE: To quantify the prevalence of known health-related risk factors for severe COVID-19 illness among Aboriginal and Torres Strait Islander adults, and their relationship with social determinants. METHODS: Weighted cross-sectional analysis of the 2018-19 National Aboriginal and Torres Strait Islander Health Survey; Odds Ratios for cumulative risk count category (0, 1, or ≥2 health-related risk factors) by social factors calculated using ordered logistic regression. RESULTS: Of the adult population, 42.9%(95%CI:40.6,45.2) had none of the examined health-related risk factors; 38.9%(36.6,41.1) had 1, and 18.2%(16.7,19.7) had ≥2. Adults experiencing relative advantage across social indicators had significantly lower cumulative risk counts, with 30-70% lower odds of being in a higher risk category. CONCLUSIONS: Aboriginal and Torres Strait Islander peoples must continue to be recognised as a priority population in all stages of pandemic preparedness and response as they have disproportionate exposure to social factors associated with risk of severe COVID-19 illness. Indigeneity itself is not a 'risk' factor and must be viewed in the wider context of inequities that impact health Implications for public health: Multi-sectoral responses are required to improve health during and after the COVID-19 pandemic that: enable self-determination; improve incomes, safety, food security and culturally-safe healthcare; and address discrimination and trauma.

Bayesian spatial modelling of early childhood development in Australian regions.

BACKGROUND: Children's early development plays a vital role for maintaining healthy lives and influences future outcomes. It is also heavily affected by community factors which vary geographically. Direct methods do not provide a comprehensive picture of this variation, especially for areas with sparse populations and low data coverage. In the context of Australia, the Australian Early Development Census (AEDC) provides a measure of early child development upon school entry. There are two primary aims of this study: (i) provide improved prevalence estimates of children who are considered as developmentally vulnerable in regions across Australia; (ii) ascertain how social-economic disadvantage partly explains the spatial variation. METHODS: We used Bayesian spatial hierarchical models with the Socio-economic Indexes for Areas (SEIFA) as a covariate to provide improved estimates of all 335 SA3 regions in Australia. The study included 308,953 children involved in the 2018 AEDC where 21.7% of them were considered to be developmentally vulnerable in at least one domain. There are five domains of developmental vulnerability-physical health and wellbeing; social competence; emotional maturity; language and cognitive skills; and communication and general knowledge. RESULTS: There are significant improvements in estimation of the prevalence of developmental vulnerability through incorporating the socio-economic disadvantage in an area. These improvements persist in all five domains-the largest improvements occurred in the Language and Cognitive Skills domain. In addition, our results reveal that there is an important geographical dimension to developmental vulnerability in Australia. CONCLUSION: Sparsely populated areas in sample surveys lead to unreliable direct estimates of the relatively small prevalence of child vulnerability. Bayesian spatial modelling can account for the spatial patterns in childhood vulnerability while including the impact of socio-economic disadvantage on geographic variation. Further investigation, using a broader range of covariates, could shed more light on explaining this spatial variation.

Aboriginal and Torres Strait Islander preferences for healthcare settings: effective use of design images in survey research.

Objective The aim of this study was to develop an effective digital survey instrument incorporating images to investigate Aboriginal and Torres Strait Islander preferences for health clinics and hospitals, design of healthcare settings and the differences between inner regional and remote locations. Methods Design-related constructs developed from qualitative interviews informed the construction of healthcare setting images. These images were embedded in an online survey instrument to elicit data on design preferences and an area-based recruitment strategy ensured participation by Indigenous Australians from three Queensland locations. Logistic regression analyses were used to estimate the odds of preference for a healthcare setting design by location. Results Statistical analysis of data from 602 participants showed a preference for health care at an Aboriginal and Torres Strait Islander clinic. The odds of preference for an in-patient room with a two- versus single-bed design was greater for participants with less education, and lower for participants with a long-term health problem. Conclusions The multidisciplinary approach to developing an online survey instrument with images and the willingness of Indigenous people of all ages to engage with the images demonstrated the effectiveness of this method in providing robust evidence for the design of culturally appropriate healthcare spaces for Indigenous users. What is known about the topic? Evidence-based research influences the design of healthcare buildings, yet the field currently provides negligible evidence on cross-cultural perceptions or experiences of conventional modern hospitals and clinics. Although recent healthcare buildings show signs of acknowledging Indigenous users, general principles in the Australian Health Facility Guidelines give limited specific information about how cultural requirements may translate into architectural design. What does this paper add? The multidisciplinary approach to research design has enabled the development of healthcare design-related constructs from consultation with Indigenous people, the presentation of these constructs as images and the inclusion of these images for comparison and selection in a digital survey instrument. Combined with a culturally appropriate recruitment strategy, this survey provides evidence from a large sample of the Indigenous population. Selected results from analyses of survey responses show the capacity of the methodological approach to address broader questions about Indigenous preferences for healthcare settings by location, age and sex. What are the implications for practitioners? A focus on individual preferences related to healthcare clinics and the combined significance of design and setting can give practitioners a better understanding of Aboriginal and Torres Strait Islander people's preferences and suggest ways to reduce the incidence of poor engagement with healthcare services.

Subsequent Migration of Immigrants Within Australia, 1981-2016.

Australia is a major immigration country and immigrants currently represent around 28% of the total population. The aim of this research is to understand the long-term consequences of this immigration and, particularly, how migrants respond to opportunities within the country after arriving through the process of subsequent (internal) migration. The focus is on major immigrant groups in Australia, including persons born in the United Kingdom, New Zealand, China and India, and how their patterns differ from persons born in Australia. To conduct this analysis, we have gathered data for a 35-year period based on quinquennial census data. We also obtained birthplace-specific mortality data for constructing multiregional life tables for the immigrant populations. Subsequent migration is important for understanding population redistribution, and the relative attractiveness of destinations within host countries. Our results highlight the importance of subsequent migration and the diversity of migration behaviours amongst different immigrant groups in the context of overall declines in internal migration since 1981.

The Sources and Diversity of Immigrant Population Change in Australia, 1981-2011.

Australia has one of the largest percentages of immigrant populations in the developed world with a highly regulated system of immigration control and regular censuses to track their changes over time. However, the ability to explain the population change through the demographic components of immigration, emigration, and death by age and sex is complicated because of differences in measurement and sources of information. In this article, we explore three methods for reconciling the demographic accounts from 1981 to 2011 for the Australia-born and 18 foreign-born population groups. We then describe how the immigrant populations have changed and what has contributed most to that change. We find that the sources of immigrant population change have varied considerably by age, sex, country of birth, and period of immigration. Immigrants from Europe are currently the oldest and slowest-growing populations, whereas those from elsewhere are growing rapidly and exhibit relatively young population age structures. Studying these patterns over time helps us to understand the nature of international migration and its long-term contributions to population change and composition.

Including mobile-only telephone users in a statewide preventive health survey-Differences in the prevalence of health risk factors and impact on trends.

The Queensland preventive health survey is conducted annually to monitor the prevalence of behavioural risk factors in the north-east Australian state. Prompted by domestic and international trends in mobile telephone usage, the 2015 survey incorporated both mobile and landline telephone numbers from a list-based sampling frame. Estimates for landline-accessible and mobile-only respondents are compared to assess potential bias in landline-only surveys in the context of public health surveillance. Significant differences were found in subcategories of all health prevalence estimates considered (alcohol consumption, body mass index, smoking, and physical activity) from 2015 survey results. Results from Australian and international studies that have considered mobile telephone non-coverage bias are also summarised and discussed. We find that adjusting for sampling biases of telephone surveys by weighting does not fully compensate for the differences in prevalence estimates. However, predicted trends from previous years' surveys only differ significantly for the 2015 prevalence estimates of alcohol consumption. We conclude that the inclusion of mobile telephones into standard telephones surveys is important for obtaining valid, reliable and representative data to reduce bias in health prevalence estimates. Importantly, unlike some international experiences, the addition of mobiles telephones into the Queensland preventive health survey occurred before population trends were significantly affected.

Profiling the mobile-only population in Australia: insights from the Australian National Health Survey.

BACKGROUND: The Australian population that relies on mobile phones exclusively has increased from 5% in 2005 to 29% in 2014. Failing to include this mobile-only population leads to a potential bias in estimates from landline-based telephone surveys. This paper considers the impacts on selected health prevalence estimates with and without the mobile-only population. METHODS: Using data from the Australian Health Survey - which, for the first time, included a question on telephone status - we examined demographic, geographic and health differences between the landline-accessible and mobile-only population. These groups were also compared to the full population, controlling for the sampling design and differential non-response patterns in the observed sample through weighting and benchmarking. RESULTS: The landline-accessible population differs from the mobile-only population for selected health measures resulting in biased prevalence estimates for smoking, alcohol risk and private health insurance coverage in the full population. The differences remain even after adjusting for age and gender. CONCLUSIONS: Using landline telephones only for conducting population health surveys will have an impact on prevalence rate estimates of health risk factors due to the differing profiles of the mobile-only population from the landline-accessible population.

Ethnic Differences in the Quality of the Interview Process and Implications for Survey Analysis: The Case of Indigenous Australians.

Comparable survey data on Indigenous and non-Indigenous Australians are highly sought after by policymakers to inform policies aimed at closing ethnic socio-economic gaps. However, collection of such data is compromised by group differences in socio-economic status and cultural norms. We use data from the Household, Income and Labour Dynamics in Australia Survey and multiple-membership multilevel regression models that allow for individual and interviewer effects to examine differences between Indigenous and non-Indigenous Australians in approximate measures of the quality of the interview process. We find that there are both direct and indirect ethnic effects on different dimensions of interview process quality, with Indigenous Australians faring worse than non-Indigenous Australians in all outcomes ceteris paribus. This indicates that nationwide surveys must feature interview protocols that are sensitive to the needs and culture of Indigenous respondents to improve the quality of the survey information gathered from this subpopulation.