Sociodemographic and health factors associated with genetic testing in australia: insights from a cohort-based study of 45,061 participants

ABSTRACT With increasing availability of genetic tests, it is important to consider differences in testing patterns between population subgroups. We examined self-reported genetic testing

among 45,061 participants of the Australian population-based 45 and Up Study, testing for associations with sociodemographic and health characteristics (multivariable logistic regression).

9.2% of participants reported ever having genetic testing; 3.9% reported disease-related testing, 5.2% non-disease-related testing, 0.7% both disease-related and non-disease-related testing.

Disease-related genetic testing was strongly associated with younger age, female sex, history of cancers and cardiovascular disease, and cancer family history. Disease-related testing was

also strongly associated with higher education (university _versus_ school certificate: adjusted OR [aOR] = 1.50 [95%CI:1.29–1.75]; certificate/diploma _versus_ school certificate: aOR = 

1.40 [95%CI:1.20–1.63]); there was suggestive evidence for association with higher household income ($AUD90,000+ _versus_ <$AUD30,000: aOR = 1.22 [95%CI:1.02–1.46]), which strengthened

when not adjusting for education (aOR = 1.34 [95%CI:1.13–1.60]). These results suggest further work on ensuring equitable access is needed to prevent potential health inequities. SIMILAR

CONTENT BEING VIEWED BY OTHERS DIRECT TO CONSUMER GENETIC TESTING IN DENMARK—PUBLIC KNOWLEDGE, USE, AND ATTITUDES Article 01 March 2021 A STATE-BASED APPROACH TO GENOMICS FOR RARE DISEASE

AND POPULATION SCREENING Article 27 November 2020 POLYGENIC RISK SCORES IN THE CLINIC: A SYSTEMATIC REVIEW OF STAKEHOLDERS’ PERSPECTIVES, ATTITUDES, AND EXPERIENCES Article 23 November 2024

INTRODUCTION Genetic and genomic testing (in the following, “genetic testing” for brevity) has considerable promise for precision health, with tests increasingly available for disease risk

prediction, diagnosis, and treatment [1, 2], especially in cancer [3, 4]. Australia has universal healthcare (‘Medicare’), supplemented by private health insurance; however, re-imbursement

for genetic testing is limited, with many tests covered by State/Territory Governments, private healthcare providers, and/or individuals [5]. Notably, disease-related direct-to-consumer

(DTC) tests are increasingly available without a specialist referral, alongside non-disease-related DTC tests that can increase familiarity with genetics and uptake of future testing. To

determine how genomics could support effective, efficient, and equitable healthcare, it is thus important to understand current patterns of genetic testing. Australian studies from 2016 to

2017 [6, 7] reported that health literacy and socioeconomic advantage were associated with increased access to genetic tests (Supplementary Information p19). Since then, availability of

genetic testing has increased substantially [8]. Here, we draw on more recent and larger-scale population-based data to investigate self-reported genetic testing (any, disease-related, and

non-disease-related) and examine associations with sociodemographic and health characteristics (cancer and non-cancer conditions) in Australia. MATERIALS AND METHODS 45 AND UP STUDY The Sax

Institute’s 45 and Up Study is a population-based cohort in New South Wales (NSW), Australia, with 267,357 participants aged 45+ years recruited in 2005–2009 [9, 10]. Briefly, potential

participants were randomly sampled from the Services Australia Medicare enrolment database (1,395,174 invitations sent, ~19% participation rate). People aged 80+ years and rural/remote

residents were oversampled. In 2020, questionnaires were sent to approximately one-third of the cohort (85,299 participants) as part of regular follow-up (52.8% response rate, details see

Supplementary Information p3, Supplemental Fig. 1). GENETIC TESTING The 2020 follow-up questionnaire (paper-based or online) asked whether participants ever had any genetic testing (Yes; No;

Don’t know/don’t want to say), and if so, what the genetic testing aimed to determine (multiple-choice question, see Supplementary Information p7). The questions were deliberately broad to

avoid disclosure of testing with life insurance implications, without separating clinical and non-clinical settings. For subsequent analyses, we considered three genetic testing categories:

“any testing” (ever had any genetic testing); “disease-related testing” (disease risk, diagnosis, or treatment); and “non-disease-related testing only” (genetic ancestry and/or

diet-/fitness-related tests, but not tests related to disease risk, diagnosis or treatment). PARTICIPANTS’ CHARACTERISTICS Participants’ sociodemographic and health characteristics were

obtained from the 2020 or baseline questionnaire, including age, sex, education, household income, health insurance status, area-based socioeconomic status [11], accessibility/remoteness of

place of residence [12], personal and family history of different diseases, and ever having children (details see Table 1, Supplementary Information p4). For the health characteristic of

personal cancer history, participants’ invasive cancer diagnoses were ascertained from probabilistic linkage [13] to NSW Cancer Registry data (1994–2019; Table 1, Supplemental Table 1;

registry data held by Cancer Institute NSW, linkage by the Centre for Health Record Linkage, http://www.cherel.org.au/). STATISTICAL ANALYSES We reported the number and proportion of

respondents for each genetic testing category, with exploratory analysis applying re-weighting for selected sociodemographic characteristics to Australian Census data (people aged 55+

years). Multivariable logistic regression was used to test for associations between participants’ characteristics and genetic testing, separately for each of the three genetic testing

categories (any, disease-related, or non-disease-related only). We calculated odds ratios (aOR) simultaneously adjusted for all characteristics shown in Table 1, and 95% confidence intervals

(95%CI). To account for multiple testing (≤50 non-reference categories per analysis), we defined significance at _p_ < 0.001 (Bonferroni-adjusted threshold). To indicate potential

avenues for further work, we also reported associations at _p_ < 0.05 as “suggestive evidence”. Due to strong associations between genetic testing and both personal and family history of

cancers, we further tested for associations specifically among participants with a previous invasive cancer diagnosis. We performed several sensitivity analyses for the association tests:

(1) for any genetic testing, excluding participants with “don’t know/don’t want to say” and missing responses (grouped with responses of no genetic testing in main analysis); (2) without

adjustment for education, to examine associations between genetic testing and different socioeconomic status (SES) characteristics (due to correlation between education and SES); (3)

excluding participants with personal or family history of cancer (to check for sex-specific cancers driving association between genetic testing and sex); (4) applying re-weighting to

Australian Census data (exploratory only); and (5) stratified by sex. Analyses used SAS v9.4 or R v4.3.1. RESULTS 45,061 participants who completed the 2020 follow-up questionnaire could be

included in the analysis (age at follow-up 56+ years, Table 1, Supplemental Fig. 1). Among all participants, 9.2% (95%CI:8.9–9.4%) reported ever having any genetic testing, 3.9% (3.7–4.1%)

disease-related testing, 5.2% (5.0–5.4%) non-disease-related testing, and 0.7% (0.6–0.8%) both disease-related and non-disease-related testing (Supplemental Tables 2–3). Estimates were

similar when re-weighting data to match the distribution of selected key characteristics to national or NSW data (absolute difference <0.6%, e.g. any genetic testing: 8.6–9.3%,

Supplemental Table 4). ASSOCIATIONS BETWEEN GENETIC TESTING AND PARTICIPANTS’ CHARACTERISTICS Ever having genetic testing was associated with age (80+ years: aOR = 0.81 versus 60–69 years)

and female sex (aOR = 1.15 versus male; Fig. 1). There was a significant association with university education (aOR = 1.25 versus school certificate) and suggestive evidence (_p_ < 0.05)

for $AUD90,000+ household income (aOR = 1.14 versus <$AUD30,000), but no evidence for association with area-based SES or remoteness of residence. Significant associations were also

observed with personal history of breast cancer, colorectal cancer and cardiovascular disease, family history of breast cancer, ovarian cancer and dementia/Alzheimer’s, and ever having

children. Disease-related testing showed similar association patterns, including stronger associations with age (70–79 years: aOR = 0.70; 80+ years: aOR = 0.40) and female sex (aOR = 1.62).

Notably, we found stronger associations for several SES characteristics: significant associations for both certificate/diploma (aOR = 1.40) and university education (aOR = 1.50), suggestive

evidence (_p_ < 0.05) and a higher estimate for $AUD90,000+ household income (aOR = 1.22), and suggestive evidence for private health insurance (aOR = 1.27). Reporting non-disease-related

testing only was significantly associated with university education (aOR = 1.35) and family history of dementia/Alzheimer’s (aOR = 1.18; Fig. 1). Results of analyses restricted to

participants with a personal cancer history were similar to the main analysis (Supplementary Information p12). Disease-related testing was also significantly associated with younger age at

diagnosis, more recent diagnosis periods, and metastatic/unknown spread of cancer at diagnosis (Supplemental Fig. 2). SENSITIVITY ANALYSES Excluding participants with “don’t know/don’t want

to say” and missing responses to any genetic testing (5% of all _n_ = 45,061) from the regression analysis had very little impact on the results. Without adjustment for education,

associations with higher household income increased (relative increase in aOR up to ~10%) and were statistically significant for $AUD90,000+ income (Supplementary Fig. 3). Associations with

other characteristics did not change substantially. There was a similar pattern in this analysis restricted to participants with cancer, with aORs for disease-related testing and $AUD90,000+

household income increasing, though not statistically significant (Supplementary Fig. 4). When the main association analyses were restricted to participants without any personal nor family

history of cancer, the association between disease-related testing and sex was slightly attenuated (aOR = 1.47) but remained significant (Supplementary Fig. 5), suggesting testing related to

sex-specific cancers is not the only contributing factor for this association between genetic testing and sex. When re-weighting study data to the Australian population, association results

were generally similar to the main analysis (Supplemental Table 5; Supplementary Information p17). Results from sex-stratified analyses were also largely similar, with most notable

differences of stronger association between genetic testing (disease-related and non-disease-related) and university education among males than females, and family history of breast cancer

associated with genetic testing (any and disease-related) among females only (Supplementary Table 6; Supplementary Information p18). DISCUSSION In this large-scale analysis of self-reported

genetic testing among >45,000 Australians (age 56+ years) from a population-based cohort, 9.2% of participants reported ever having any genetic testing, among whom 42.4% reported

disease-related testing and 56.3% non-disease-related testing, with 7.9% reporting both (see Supplementary information p20 for additional discussion). Re-weighted estimates to match the

general population age 55+ were similar to the main estimates. Self-reported genetic testing in our study was substantially lower than the 21.6% reported in a cross-sectional 2020 US survey

[14], with the USA currently representing the largest genetic testing market. Our estimate was also lower than the 22.4% reported in the Australian Genioz study [7], which might be related

to different participant demographics (56+ versus 18+ years; 56% versus 72% females) and/or recruitment (established cohort versus mix of strategies including social media; notably, 59% of

Genioz study participants were undertaking/had university education, and 15% were working in life science/genomics, which likely contributed to the high prevalence of genetic testing).

Consistent with previous studies [6, 7, 14], we found strong associations between genetic testing and younger age and female sex (not explained by sex-specific cancers alone, with potential

contributions of different health awareness and attitudes toward preventative care [15]). We also found very strong associations between genetic testing and education. While previous

Australian studies [6, 7] generally focused on university education only, we found a gradient across education levels. Compared to attaining at most a school certificate, odds ratio

estimates for disease-related genetic testing were highest for university education, followed by certificate/diploma (both _p_ < 0.001), then trade/apprenticeship qualifications (_p_ <

 0.05). Notably, we found evidence for stronger association between disease-related genetic testing and university education among males than females, which could be of interest for future

investigation. Generally, associations with education could be related to increased health literacy and/or higher income facilitating out-of-pocket expenses for non-reimbursed tests (latter

also supported by the increased and significant association with the highest household income when not adjusting for education). Out-of-pocket expenses for genetic testing are also highly

relevant for DTC tests, with potential for health inequities discussed further in Supplementary Information p21. The strong associations between genetic testing and personal and family

history of several cancers were consistent with expectations based on current Australian genetic testing guidelines [16, 17] and increased use of genetic testing for targeted treatment [18]

(germline and somatic tests were not separated in the self-report). We found a significant association between CVD and genetic testing, consistent with increasing availability of genetic

tests for e.g., inherited cardiomyopathy and inherited hypercholesterolemia [19]. As a study limitation, the cohort was not representative of the general population (e.g., due to older age,

higher education and socioeconomic advantage); nonetheless, previous work suggests within-cohort associations are expected to mirror population relationships [20]. Self-reported genetic

testing is subject to recall bias, which could differ by age and/or education. We could not distinguish whether genetic testing occurred through health professionals. Notable strengths of

this study include the very large sample, inclusion of a very broad range of participants’ characteristics, data linkage to cancer registry, and rigorous statistical analysis. In conclusion,

our results provide insights on genetic testing patterns in Australia as an example of a high-income country, and re-enforce the need for further work to ensure equitable access to current

and future genomic technologies, covering both educational and financial considerations in depth. DATA AVAILABILITY This study uses third-party data not owned or collected by the authors,

with on-provision by authors not permitted by the relevant data custodians (Sax Institute, Cancer Institute NSW), as it would compromise the participants’ confidentiality and privacy.

However, the data are available from the data custodians for approved research projects - data access enquiries can be made to the Sax Institute (see

https://www.saxinstitute.org.au/our-work/45-upstudy/governance/ for details). Other researchers would be able to access these data using the same process followed by the authors. REFERENCES

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population with contrasting response rates and designs. BMC Med Res Methodol. 2010;10:26. Article  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS This research

was completed using data collected through the 45 and Up Study (www.saxinstitute.org.au). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer

Council NSW and partners the Heart Foundation and the NSW Ministry of Health. We thank the many thousands of people participating in the 45 and Up Study and the Centre for Health Record

Linkage (CHeReL) for the record linkage. The NSW Cancer Registry data is provided by the Cancer Institute NSW. Secure data access was provided through the Sax Institute’s Secure Unified

Research Environment (SURE). The questions on genetic testing in the Sax Institute’s 45 and Up Study survey in Australia were sponsored by Cancer Council NSW. Population-based

sociodemographic characteristics were obtained from the Australian Bureau of Statistics 2021 Census data. FUNDING JS is the recipient of a Cancer Institute NSW Career Development Fellowship

(2022/CDF1154). KC receives salary support from the National Health and Medical Research Council (#APP1194679). AEC is funded by a National Health and Medical Research Council Investigator

Fellowship (#2008454). AKS is supported by a National Health and Medical Research Council Synergy grant (#2009923) and Medical Research Future Fund grant (#MRFF 2024995). KLAD is supported

by a National Health and Medical Research Council Investigator grant to AEC (#2008454) and a Medical Research Future Fund grant (#MRFF2024995). HMT is supported by a Medical Research Future

Fund grant (#MRF2007708). YJK and PN are supported by a Medical Research Future Fund grant (#MRFF 1200535). MAM is supported by a Department of Defense Ovarian Cancer Research Program,

Ovarian Cancer Academy Early Career Investigator Award (W81XWH-21-1-0914). Open Access funding enabled and organized by CAUL and its Member Institutions. AUTHOR INFORMATION Author notes *

These authors contributed equally: David E. Goldsbury, Yoon-Jung Kang. AUTHORS AND AFFILIATIONS * The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW,

Sydney, NSW, Australia David E. Goldsbury, Yoon-Jung Kang, Hamzeh M. Tanha, Amelia K. Smit, Kate L. A. Dunlop, Lara Petelin, Preston Ngo, Harriet Hui, Nicola S. Meagher, Melissa A. Merritt, 

Marianne Weber, Anna DeFazio, Anne E. Cust & Julia Steinberg * The University of Sydney, Sydney, NSW, Australia Catherine Tang * Melbourne School of Population & Global Health,

University of Melbourne, Melbourne, VIC, Australia Lara Petelin * Department of Gynaecological Oncology, Westmead Hospital, Sydney, NSW, Australia Anna DeFazio * The Westmead Institute for

Medical Research, Sydney, NSW, Australia Anna DeFazio * School of Public Health, The University of Sydney, Sydney, NSW, Australia Karen Canfell Authors * David E. Goldsbury View author

publications You can also search for this author inPubMed Google Scholar * Yoon-Jung Kang View author publications You can also search for this author inPubMed Google Scholar * Catherine

Tang View author publications You can also search for this author inPubMed Google Scholar * Hamzeh M. Tanha View author publications You can also search for this author inPubMed Google

Scholar * Amelia K. Smit View author publications You can also search for this author inPubMed Google Scholar * Kate L. A. Dunlop View author publications You can also search for this author

inPubMed Google Scholar * Lara Petelin View author publications You can also search for this author inPubMed Google Scholar * Preston Ngo View author publications You can also search for

this author inPubMed Google Scholar * Harriet Hui View author publications You can also search for this author inPubMed Google Scholar * Nicola S. Meagher View author publications You can

also search for this author inPubMed Google Scholar * Melissa A. Merritt View author publications You can also search for this author inPubMed Google Scholar * Marianne Weber View author

publications You can also search for this author inPubMed Google Scholar * Anna DeFazio View author publications You can also search for this author inPubMed Google Scholar * Anne E. Cust

View author publications You can also search for this author inPubMed Google Scholar * Karen Canfell View author publications You can also search for this author inPubMed Google Scholar *

Julia Steinberg View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS JS led study conception, with contributions from MW and KC. JS and DG

designed the study, with other authors reviewing and commenting on the analysis plan. CT led a scan of other relevant literature, in collaboration with JS, YJK, and HMT. DG performed

statistical analysis. DG, YJK, CT, and JS drafted the manuscript. All authors contributed to interpretation of the results and critical review of the manuscript, approving the final version

for publication. CORRESPONDING AUTHOR Correspondence to Julia Steinberg. ETHICS DECLARATIONS COMPETING INTERESTS Professor Karen Canfell is co-principal investigator of an

investigator-initiated trial of cervical screening, Compass, run by the Australian Centre for Prevention of Cervical Cancer (ACPCC), which is a government-funded not-for-profit charity; the

ACPCC has received equipment and a funding contribution from Roche Molecular Diagnostics, and operational support from the Australian Government. KC is also co- principal investigator on a

major investigator-initiated implementation programme Elimination Partnership in Cervical Cancer (EPICC) which receives support from the Australian government and Minderoo Foundation and

equipment donations from Cepheid. Anna DeFazio has received research support from AstraZeneca and Illumina. David E Goldsbury, Yoon-Jung Kang, Catherine Tang, Hamzeh M Tanha, Amelia K Smit,

Kate L A Dunlop, Lara Petelin, Preston Ngo, Harriet Hui, Nicola S Meagher, Melissa A Merritt, Marianne Weber, Anne E Cust and Julia Steinberg declare that they have no conflict of interest.

ETHICAL APPROVAL The conduct of the Sax Institute’s 45 and Up Study was approved by the University of New South Wales Human Research Ethics Committee. The NSW Population Health and Health

Services Research Ethics Committee approved the work described here (HREC/14/CIPHS/54). ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional

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health factors associated with genetic testing in Australia: insights from a cohort-based study of 45,061 participants. _Eur J Hum Genet_ (2025). https://doi.org/10.1038/s41431-025-01816-x

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