Inequalities in cancer mortality between people with and without disability: A nationwide data linkage study of 10 million adults in Australia

Yi Yang; Nina Afshar; Zoe Aitken; Rebecca J Bergin; Peter Summers; Roger L Milne; Sue M Evans; Anne Kavanagh; George Disney

doi:10.1371/journal.pmed.1004873

. 2026 Jan 5;23(1):e1004873. doi: 10.1371/journal.pmed.1004873

Inequalities in cancer mortality between people with and without disability: A nationwide data linkage study of 10 million adults in Australia

Yi Yang ^1,^2,^3,^*, Nina Afshar ^4,⁵, Zoe Aitken ³, Rebecca J Bergin ^4,^6,⁷, Peter Summers ^1,^2,³, Roger L Milne ^4,^5,⁸, Sue M Evans ^9,¹⁰, Anne Kavanagh ³, George Disney ^1,²

Editor: Wei Zheng¹¹

¹Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

²Melbourne Disability Institute, University of Melbourne, Melbourne, Australia

³Disability and Health Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

⁴Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia

⁵Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

⁶Centre for Quality and Patient Safety Research, Institute for Health Transformation, School of Nursing and Midwifery, Faculty of Health, Deakin University, Geelong, Australia

⁷Department of General Practice and Primary Care, and Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Australia

⁸Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia

⁹Victorian Cancer Registry, Cancer Council Victoria, Melbourne, Australia

¹⁰School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

¹¹Vanderbilt University School of Medicine, UNITED STATES OF AMERICA

^✉

* E-mail: yang.y2@unimelb.edu.au

The authors have declared that no competing interests exist.

Roles

Yi Yang: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

Nina Afshar: Investigation, Methodology, Validation, Writing – review & editing

Zoe Aitken: Investigation, Methodology, Writing – review & editing

Rebecca J Bergin: Investigation, Methodology, Writing – review & editing

Peter Summers: Data curation, Investigation, Methodology, Writing – review & editing

Roger L Milne: Investigation, Methodology, Writing – review & editing

Sue M Evans: Investigation, Writing – review & editing

Anne Kavanagh: Investigation, Methodology, Writing – review & editing

George Disney: Conceptualization, Investigation, Methodology, Writing – review & editing

Wei Zheng: Academic Editor

PMCID: PMC12768262 PMID: 41490345

Abstract

Background

Cancer is a major yet under-recognised contributor to the mortality gap between people with and without disability. Our study aims to quantify these inequalities to inform cancer control efforts to reduce the gap.

Methods and findings

We used nationally-linked data (2011–2022) to construct a cohort of over 10 million adults in Australia aged 25–74 years. Disability was measured in 2011 Census as requiring assistance in core daily activities and cancer related deaths identified in national death registrations. We estimated age-standardised and age-specific cancer mortality rates, and absolute and relative mortality inequalities (rate differences and ratios) between people with and without disability. The study included 10,414,951 people. Of the 5,403,503 females, 185,801 (3.4%) reported disability; 183,594 of the 5,011,448 males (3.7%) reported disability. Over 93,940,222 person-years (9.2 years on average), 219,257 cancer-related deaths occurred. After age-standardisation, per 100,000 person-years, there were 314 (95% confidence intervals [CI]: 301, 328) more cancer related deaths in females and 410 (95% CI: 394, 427) more in males with disability (1.96 [95% CI: 1.92, 2.00], and 1.83 [95% CI: 1.80, 1.87] times higher, respectively) than those without disability. The largest absolute inequalities were for lung cancer in both females and males (67 [95% CI: 60, 73] and 103 [95% CI: 95, 111] more deaths per 100,000 person-years, respectively), followed by breast cancer in females (54 [95% CI: 49, 60] more deaths), prostate cancer in males (31 [95% CI: 26, 36] more deaths), and colorectal cancer in both sexes (30 more [95% CI: 25, 34] deaths in females and 44 [95% CI: 38, 49] more in males). By 5-year age group, lung cancer was the leading contributor to absolute inequalities in females and males aged 35 years and older. In females, across most age groups, breast cancer was the second largest contributor to absolute inequalities, followed by colorectal cancer. In males, colorectal cancer was the second largest contributor across most age groups, with prostate cancer contributing substantially to absolute inequalities in those aged 55 years and older. A substantial proportion of differences in cancer-related deaths between people with and without disability, across most age groups in both females and males were driven by cancers linked to smoking, obesity, and alcohol consumption. We found similar-sized relative inequalities between individuals with and without disability in mortality due to individual cancers in both sexes. The main limitation of the study was that disability status was measured at a single time point.

Conclusions

People with disability had higher cancer mortality overall and in relation to specific cancers than people without disability. To close the gap, effort should prioritise interventions that work for people with disability across the cancer control pathway.

Author summary

Why was this study done?

Deaths due to cancer are a major yet under-recognised public health concern for people with disability.
This study aimed to find out whether people with disability in Australia die from cancer more often than people without disability.

What did the researchers do and find?

We used disability information collected in the Australian Census in 2011.
We obtained information on date of death and cause of death up to 2022 from death registrations.
We found higher death rate in people with disability compared with those without due to a range of cancers.
Much of this higher cancer-related death rate is linked to cancers that can be prevented or detected early through screening programs, and cancers associated with modifiable risk factors like smoking, obesity, and alcohol use.

What do these findings mean?

These findings show that people with disability face inequalities in cancer outcomes that could potentially be avoided.
Further research is needed to understand the causes of these inequalities.
Together with past evidence, the findings highlight the importance of making cancer prevention, screening and care more accessible and tailored to the needs of people with disability
Addressing these inequalities could help reduce avoidable cancer-related deaths among people with disability.
The main limitation was that disability status was recorded only on the 2011 Census day, and we could not track any changes over time.

Yi Yang and colleagues estimate overall and type-specific cancer mortality inequalities between adults with and without disability in Australia, reporting both relative and absolute inequalities to inform cancer control efforts and research priorities.

Introduction

Advancing equity in cancer outcomes has become a key theme of national cancer control plans [1]. Multiple countries identify people with disability as a priority population [1], including in Australia’s new cancer control plan for 2024–2034 [2].

People with disability experience higher levels of social disadvantage such as poverty, lower educational attainment, social exclusion, and unemployment compared to people without disability [3]. Socioeconomic gradient exists in exposure to cancer risk factors such as smoking and obesity, lower participation in cancer screening, and inequitable treatment and care options, all of which could contribute to higher cancer mortality in people with disability [4,5]. This inequality is compounded by inaccessible healthcare for people with disability [6].

A recent systematic review of cancer inequalities faced by people with disability identified two critical gaps in the literature [7]. The first gap was the scarce evidence that quantified inequalities in cancer mortality between people with and without disability: 10 studies examined overall cancer mortality; only 4 examined type-specific cancer mortality [7]. Given the causes and control strategies differ by cancer types, it is important to examine the inequalities in mortality due to specific cancer types, and how they contribute to the overall cancer mortality gap. Most studies identified subpopulations of people with disability using medical diagnosis, such as diagnosis of intellectual disability or severe mental illnesses. The second gap pertained to the scale of inequalities examined. All studies reported inequalities only on a relative scale, with 1.2–2.5 times higher risks of mortality in people with disability relative to people without disability [7]. Without absolute inequality estimates, the similarly sized relative inequalities give limited information to guide priority setting and resource allocation.

In a recent study, we demonstrated that the magnitude of relative inequality in cancer mortality between people with and without disability was smaller than other causes of death, such as mortality due to neurological conditions [8]. However, when examined on the absolute scale, cancer was the leading contributor, accounting for approximately 20% of all additional deaths in people with disability compared to people without disability [8]. This signifies a public health priority for this population. Reporting inequalities only on the relative scale could potentially lead to cancer control efforts not being appropriately prioritised among people with disability.

This study aims to estimate overall and type-specific cancer mortality inequalities between adults with and without disability in Australia, following the recommended practice to report both relative and absolute inequalities to inform cancer control efforts and research priorities.[9]

Methods

Study design and data sources

We used the Australian Bureau of Statistics (ABS) Person Level Integrated Data Asset (PLIDA), which holds a range of administrative and research datasets [10]. Data collections are linked at the unit-record level through deterministic and probabilistic linkage to a ‘population spine’ that aims to cover all residents in Australia [10]. The spine is constructed using major administrative datasets on Australia’s national healthcare insurance scheme (Medicare), income and taxation, and government benefits and payments (see S1 Methods for details) [10].

Fig 1 shows how we constructed a Census-mortality cohort for this study. Data from the 2011 Australian Census of Population and Housing (the Census) and ABS data on death registrations (2011–2022) were linked via the population spine. The Census collected key socio-demographic information on the whole population of Australia on Census night—9th August 2011. The Census is compulsory, and had a response rate of 96.3% in 2011 [11].

Our cohort included Census respondents aged 25–74 years on Census night who responded to the disability question and were linked to the population spine with unique spine IDs. Deaths among the spine-linked cohort were ascertained through death records linked to the same spine IDs. People older than 74 years were excluded to ensure old age was not the primary reason for both disability and mortality. We excluded respondents who were visitors in Australia.

The study was approved by the University of Melbourne Human Ethics Committee (28947). The requirement for individual participant consent was waived by the University of Melbourne Human Ethics Committee.

Disability status

The Census disability questions asked about whether the respondent ever needed someone to help with, or be with them, for three core activity areas (self-care, body movement, or communication) and the reasons for needing assistance or supervision. If a person was unable to complete the questions themselves, a household member answered on their behalf. ABS releases a summary variable of disability for research purpose. People were classified as having disability if they reported requiring assistance or supervision with one or more core daily activities due to long-term health conditions or disability that lasted six months or more. People were categorised as non-disabled if they had reported no need for assistance or their need for assistance was because of short-term health conditions, difficulty with English, or young age as the only reason (see Supplementary Methods for details) [12]. These questions were designed to largely align with the ‘severe or profound limitation’ in core activity measure of disability used in the Survey of Disability, Ageing and Carers in Australia [13]. It aligns with the International Classification of Functioning, Disability and Health model of disability through emphasising the impact of functional limitations on an individual’s life [14].

Mortality

Overall cancer mortality and cancer type-specific mortality were determined using the underlying cause of death, defined according to the International Classification of Diseases version 10 (ICD-10) [15].

We examined overall cancer mortality (C00-C97) and mortality due to cancers targeted by Australia’s national screening programs for early detection (breast cancer [C50], colorectal cancer [C18–C20, C26.0], cervical cancer [C53]). In addition, we examined mortality due to other specific cancer types that are most common causes of cancer-related deaths in Australia [16], including lung cancer (C33–C34), prostate cancer (C61) and pancreatic cancer (C25). Based on evidence evaluation done by International Agency for Research on Cancer and World Cancer Research Fund [17–19], we also examined mortality due to groups of cancers where sufficient evidence is available for carcinogenicity from smoking, obesity, and alcohol consumption. See S1 Table for the list of cancers and ICD-10 codes.

Demographic information

Age and sex were reported in the Census. We created 5-year age groups using age on the day of Census.

Statistical analysis

We followed the cohort for cancer-related deaths to the earlier of (1) death or (2) 31 October 2022 when death registrations were considered complete in the data for this analysis, excluding the first 2 years of follow-up after the Census date to reduce the possibility that individuals who were close to the end of life may be more likely to report disability. We calculated age-standardised mortality rates for any cancer and specific cancer types per 100,000 person-years of follow-up for females and males with and without disability.

The age-standardised mortality rate was calculated as a weighted average of age-specific mortality rates. The weights were the proportions of people in the corresponding 5-year age group of the standard population. Rather than using common standards such as the Australian Population Standard [20], we chose people with disability as the standard population, which allowed us to answer the question ‘How does cancer mortality in people with disability compare to people without disability of the same age profile?’ In this standardisation process, our mortality estimates for people with disability were largely unaffected, while the mortality estimates for people without disability were adjusted to what they would be if this group had the same age profile to that of the population with disability. This approach allowed us to produce mortality statistics centred around people with disability that more accurately reflect their real-life experience and inequalities [20]. Moreover, our standard population included both females and males with disability to allow comparison of mortality rates by sex.

Age-standardised mortality rate ratios (a measure of relative inequality) and age-standardised rate differences (a measure of absolute inequality) were calculated using people without disability as the reference, with 95% confidence intervals (CI) obtained from 1,000 bootstrap replicates. We presented rate differences, rate ratios and reference rates using a graphical tool for visualising inequalities [21]. We also calculated mortality rates for each five-year age group to ensure that important differences in age-specific mortality between people with and without disability were not masked in the age-standardised rates.

We conducted the following sensitivity analyses: (1) summarising age structure and disability prevalence in each 5-year age group for people linked and not linked to the population spine, to explore potential biases due to excluding people not linked to the population spine, and (2) estimating overall cancer mortality inequalities when categorising people with missing disability response as people with disability, and as people without disability, to evaluate the impact of excluding people who did not respond to the disability questions.

All analyses were performed in females and males separately. The statistical software used was Stata 17.0.

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). The study did not have a prospective protocol or analysis plan.

Results

We followed 10,414,951 adults aged 25–74 years over a mean of 9.2 years (93,940,222 person-years). Of the 5,403,503 females, 185,801 (3.4%) reported disability; 183,594 of the 5,011,448 males (3.7%) reported disability. During the follow-up period, 95,661 cancer-related deaths occurred in females, and 123,596 in males.

Fig 2 shows the age distribution by disability status in both sexes. Both females and males with disability had an older age profile compared to those without disability.

Age-standardised mortality rates and inequalities

Table 1 shows the crude and age-standardised overall cancer mortality rates in females and males by disability status, and the rate differences and rate ratios between groups with and without disability.

Table 1. Crude and age-standardised all-cancer mortality rates, rate differences, and rate ratios comparing people with and without disability, age 25–74 years, Australia.

Cause of death	People with disability			People without disability			Rate difference, per 100,000 years (95% CI)	Rate ratio (95% CI)^b
	Deaths (person-years)	Mortality rate, per 100,000 person years		Deaths (person-years)	Mortality rate, per 100,000 person years
	Deaths (person-years)	Crude	Age-standardised (95% CI)^a	Deaths (person-years)	Crude	Age-standardised (95% CI)^a
Females
All cancer	9,387 (1,534,217)	612	642 (629, 655)	86,274 (47,445,566)	182	327 (325, 330)	314 (301, 328)	1.96 (1.92, 2.00)
Males
All cancer	12,273 (1,470,272)	835	903 (887, 919)	111,323 (43,490,168)	256	493 (490, 496)	410 (394, 427)	1.83 (1.80, 1.87)

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^aThe Standard Population used is people (both females and males) with disability.

^bThe reference population is people (males and females) without disability.

Abbreviations: CI, confidence interval.

The overall cancer mortality rate was higher in people with disability than people without disability in both sexes. Compared to females without disability, females with disability had an additional 314 cancer-related deaths per 100,000 person-years (95% CI [301, 328]), corresponding to a 1.96 times higher cancer mortality rate (95% CI [1.92, 2.00]). Similarly, males with disability had an additional 410 cancer per 100,000 person-years (95% CI [394, 427]), representing a 1.83 times higher mortality rate (95% CI [1.80, 1.87]) than males without disability.

Fig 3 displays the reference age-standardised mortality rates in people without disability, age-standardised rate differences (absolute inequalities) and age-standardised rate ratios (relative inequalities) for individual cancer types. Point estimates and 95% CI were provided in S2 Table. In females, the greatest absolute mortality inequalities were observed for lung cancer (rate difference = 67, 95% CI [60, 73] per 100,000 person-years), breast cancer (rate difference = 54, 95% CI [49, 60]) and colorectal cancer (rate difference = 30, 95% CI [25, 34]), which were also common underlying causes of cancer related deaths in females without disability. Absolute inequalities in mortality due to pancreatic cancer (rate difference = 13, 95% CI [10, 17]) and cervical cancer (rate difference = 3, 95% CI [2, 4]) were moderate. In males, the greatest absolute inequalities were observed for lung cancer (rate difference = 103, 95% CI [95, 111]), which was also the most common underlying cause of cancer-related death in males without disability. This was followed by colorectal cancer (rate difference = 44, 95% CI [38, 49]) and prostate cancer (rate difference = 31, 95% CI [26, 36]). Absolute inequalities for pancreatic cancer were moderate (rate difference = 17, 95% CI [13, 20]). The mortality rate ratios ranged from 1.53 to 2.17 for females and 1.50 to 2.02 for males across individual cancers (S2 Table).

Age-specific mortality rates and inequalities

S1 and S2 Figs present overall and type-specific cancer mortality rates by five-year age group according to disability status for females and males, respectively (see S3 Table for age-specific rates, rate differences and rate ratios). In both sexes, overall cancer mortality rates increased with age for individuals with and without disability, with consistently higher rates in those with disability. In general, for most cancer types, absolute inequalities widened, and relative inequalities narrowed with increasing age. Absolute inequalities for some cancers were smaller in the oldest age group (70–74 years), including lung and cervical cancers in females, and pancreatic cancer in both sexes.

Fig 4 illustrates individual cancers contributing to the overall cancer mortality rate difference (absolute inequality) by 5-year age group, including screening-related cancers. For both sexes, lung cancer was the leading contributor to the absolute inequalities in mid-aged to older individuals (35 years and older). In females, breast cancer was the second major contributor in nearly all age groups, followed by colorectal cancer. In males, colorectal cancer was the second major contributor across most age groups, with prostate cancer contributing substantially in those aged 55 years and older.

Fig 5 illustrates the proportions of overall cancer mortality rate differences (absolute inequality) driven by cancers related to lifestyle-related exposures, including obesity, alcohol, and smoking. Overall, substantial proportions of the absolute inequalities were driven by deaths due to lifestyle-related cancers, which accounted for 18%–83% (females) and 27%–73% (males) of cancer-related deaths across age groups.

Sensitivity analysis

The exclusion of people not linked to the spine had negligible impact on the disability prevalences within each the 5-year age group and the age structures of males and females with and without disability (S4 Table). Our sensitivity analysis, which categorised all non-respondents as people without disability, showed virtually no changes in the inequality estimates compared to the main analysis. If the non-respondents were all people with disability, the inequality estimates were slightly attenuated but remained substantial (S5 Table), noting that this is an extreme missing-not-at-random scenario.

Discussion

We followed over 10 million adults in Australia over 9.2 years on average, and found substantial cancer mortality inequalities between people with and without disability, overall and by cancer type. The largest absolute inequalities in age-standardised rates were observed for lung cancer mortality in both females and males. Substantial absolute inequalities were also found for breast cancer mortality in females, prostate cancer mortality in males, and colorectal cancer mortality in both sexes. For these cancers, which were also common among individuals without disability, we identified sizable relative inequalities, with almost doubled mortality rates for people with disability compared to people without disability of comparable age. Moderate absolute and relative inequalities were seen for mortality from pancreatic cancer, and cervical cancer.

Relative inequalities in overall cancer mortality narrowed from younger to older age groups as cancer mortality rates increased in the reference populations without disability in both females and males. However, absolute inequalities widened, affecting more people with disability in the older age groups. Breast cancer-related deaths in females over 30 years, prostate cancer-related deaths in males older than 55 years, and lung and colorectal cancers for mid-aged and older individuals in both sexes were substantial contributors to the absolute inequalities in age-specific mortalities. These inequalities were also largely driven by deaths from lifestyle-related cancers, especially those related to smoking.

This study estimated overall and type-specific cancer mortality inequalities, on both relative and absolute scales, using nationally linked electronic data. With close to 95 million person-years of follow-up, we were able to examine, by age group and sex, a range of cancer type-specific deaths, including cancers potentially preventable through screening programs and lifestyle interventions. We followed best practice of reporting inequalities on both relative and absolute scales [9], which has been lacking in much of the previous literature [7]. We used a measure of disability designed to capture its multidimensionality—functional limitation due to health conditions—which differs from medicalised disability definitions based on medical diagnoses used in previous research [14].

Some limitations should be considered when interpreting our results. To quantify inequalities, we inevitably had to use dichotomised disability status—a multifaceted construct. More detailed information on disability was not collected, therefore, we were unable to examine cancer mortality associated with specific disabling conditions, such as leukaemia-related mortality among people with Down syndrome [22]. Disability is also a dynamic status that may change over time. In our study, disability status was measured at a single point in time (the 2011 Census), which captured participants’ baseline status but not subsequent transitions. In the 5% sample of 2011 Census records linked to the 2016 Census in the Australian Census Longitudinal Dataset, about 33% of those aged 25–74 years who reported a disability in 2011 no longer did so in 2016, while around 3% of those without disability in 2011 reported disability in 2016 [23]. Disability status may also have changed between and beyond the two censuses. Therefore, our results should be interpreted as reflecting inequalities according to baseline disability status, rather than sustained or fluctuating status over time. Future data improvement efforts should aim to reconstruct disability dynamics at a population scale using multiple longitudinal administrative data sources, such as routine care and disability support data. This would allow better assessment of how time-varying disability influences cancer mortality. Without longitudinal data, we were also unable to examine how certain factors, such as socioeconomic disadvantage, act as causes or consequences of disability. We did not stratify analyses by these factors, as this might introduce bias. For instance, if disability contributed to socioeconomic disadvantage, which in turn led to higher mortality, adjusting for or stratifying by socioeconomic position would underestimate the inequality [24,25]. The Census questions had a strong conceptual basis for identifying individuals with severe functional limitations in performing core daily activities (3.5% of our study population) [13]. Using a broader disability definition that includes less severe disabilities, the estimated prevalence was 21% for ages 25–74 [26]. This may limit the generalisability of our findings to people with less severe forms of disability. However, the disability group in our study is more likely to experience socioeconomic disadvantage, thus higher avoidable inequalities in cancer mortality than people with less severe disability.

Another limitation relates to selecting individuals into our analysis. First, we excluded 2,102,320 (16.5% of all respondents aged 25–74 years) who could not be linked to the population spine, therefore not linked to death data. However, this exclusion is unlikely to have altered our conclusion given its minimal impact on the age structure and age-specific disability prevalence. Second, we excluded people who did not respond to the Census disability questions (n = 132,139, 1% of all respondents aged 25–74 years). However, sensitivity analysis showed that absolute and relative inequalities remained substantial under extreme missing-not-at-random scenarios.

Our findings of relative inequalities aligns with previous studies that found 1.2–2.5 times higher overall cancer mortality rates among subpopulations of people with disability compared to those without, such as people with intellectual disability, psychosocial disability (severe mental illness), and hearing loss [7]. Relative inequalities in mortality were of similar magnitude for specific cancers, such as breast (1.1–1.5 times higher in people with disability) [27–29], colorectal (1.1–1.7 times higher) [27–29], lung and respiratory (1.2 and 1.5 times higher) [27,28], and pancreatic cancer (1.2 and 1.4 times higher) [27,28]. In our study, relative inequalities were larger for these cancers (Fig 3 and S2 Table). This could be partly due to the varying statistical adjustments and focus on more severe forms of disability. Evidence was inconsistent for cervical and prostate cancer [27,29].

Despite previous studies reporting similar relative inequalities to what we found, none reported which cancers caused the most additional deaths in people with disability. Our previous study found that, relative to other causes of death, people with disability had a seemingly moderate 2-fold higher overall cancer mortality compared to people without disability aged 0–74 [8]. This relative inequality translates to 368 (females) and 462 (males) additional cancer-related deaths per 100,000 person-years, making cancer the leading contributor to absolute all-cause mortality inequality among other causes of death [8]. Similarly, despite the comparable magnitude of mortality rate ratios across cancer types in the current study, the absolute inequalities were substantial for lung, colorectal, breast, and prostate cancers, reflecting the high rates in the reference populations without disability.

The mortality inequalities due to cancers for which population screening is available for early detection raises important questions of how we ensure existing screening programs meet the needs of people with disability. In the context of Australia’s commitment to eliminating cervical cancer, the 2-fold higher mortality rate among people with disabilities is concerning, even if the absolute inequality is small. Moreover, the adoption of lung cancer screening programs in recent years by multiple countries, including Australia, presents another critical opportunity to reduce the cancer mortality gap between people with and without disability though lung cancer control [30]. Missing this opportunity could potentially widen the cancer mortality gap further. Given the global low uptake of cancer screening programs for people with disability [7,31,32], common barriers need to be addressed to improve participation, including lack of autonomy, inaccessibility, financial cost, and stigma and fear [33,34].

Our findings showed that cancers associated with risk factors such as smoking, obesity, and alcohol consumption contribute substantially to the absolute inequalities in cancer mortality between people with and without disability. These factors are shaped by broader social, environmental, and structural determinants. Studying these modifiable contributors is important because they represent potential opportunities for interventions to reduce inequalities. Further research, including qualitative research, is also needed to assess whether public health programs are inclusive and effective for people with disability, and to guide equitable strategies for prevention and care.

Smoking-related cancers made a large contribution to the inequalities observed in our study. Research has shown that while smoking prevalence in Australia has declined in the general population, the decline was slower in people with disability, especially those with low incomes [35]. The slower decline suggests that smoking remains as a key driver in the cancer mortality gap between people with and without disability, especially for those experience socioeconomic disadvantage. Socioeconomic disadvantage may manifest in reduced access to timely and appropriate care due to financial barriers and challenges navigating complex health systems [6,36]. For example, a person with disability may delay seeking care due to out-of-pocket costs, inaccessible clinics, or lack of support with communication needs. These barriers could potentially lead to later-stage diagnosis, suboptimal treatment, or lower adherence to follow-up care, all of which contribute to poorer outcomes. Addressing these inequalities requires not only inclusive prevention strategies but also structural changes to make healthcare systems more equitable and accessible for people with disability.

Future studies should report both absolute and relative inequalities, as each provides complementary insights for informing policy, planning, and evaluation. Even modest relative inequalities can translate into a large burden of excess mortality, where targeted actions could deliver significant public health gains. Presenting both measures in future research is also important for monitoring and evaluation of cancer control efforts.

Our study examined inequalities in cancer mortality, which reflect the total disease burden but do not distinguish between differences in cancer incidence and survival. In Australia, cancer screening, diagnosis, and treatment data are not yet linked to disability data. Future data linkage with cancer registries is needed to better understand how disability influences cancer incidence, which may be reduced through prevention, and cancer survival, which can be improved through equitable care.

Our study confirms the need for greater attention to people with disability in cancer research and control. We call for more attention to investigate the mechanisms underpinning the higher cancer mortality rates and identify interventions to close this gap between people with and without disability.

Supporting information

S1 Methods. Supplementary Method.

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pmed.1004873.s001.docx^{(27KB, docx)}

S1 Fig. Age-specific mortality rates due to all cancer and specific cancer types according to disability status, females, age 25–74 years, Australia.

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pmed.1004873.s002.docx^{(258KB, docx)}

S2 Fig. Age-specific mortality rates due to all cancer and specific cancer types according to status of disability, males, age 25–74 years, Australia.

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S1 Table. Lifestyle-related cancers based on assessments of the International Agency for Research on Cancer and the World Cancer Research Fund.

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pmed.1004873.s004.docx^{(25.9KB, docx)}

S2 Table. Crude and age-standardised cancer-specific mortality rates, rate differences, and rate ratios comparing people with and without disability, age 25–74 years, Australia.

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pmed.1004873.s005.docx^{(26.7KB, docx)}

S3 Table. Age-specific overall cancer and type-specific mortality rates, rate differences and rate ratios comparing people with and without disability, age 25–74 years, Australia.

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pmed.1004873.s006.docx^{(50.7KB, docx)}

S4 Table. Age structure and prevalence of disability by 5-year age group in people who were linked and not linked to the population spine.

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pmed.1004873.s007.docx^{(29KB, docx)}

S5 Table. Age-standardised overall cancer mortality rate differences and rate ratios from sensitivity analysis for missing disability status.

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pmed.1004873.s008.docx^{(24.8KB, docx)}

S1 Checklist. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) checklist.

An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Websites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at http://www.strobe-statement.org.

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pmed.1004873.s009.doc^{(101.5KB, doc)}

Acknowledgments

We acknowledge the individuals and agencies involved in the collection of Census data used in this study. This study was supported by a University of Melbourne Early Career Researcher Grant and a postdoctoral fellowship by the Melbourne Disability Institute at University of Melbourne.

Abbreviations

ABS: Australian Bureau of Statistics
CI: confidence intervals
ICD-10: International Classification of Diseases version 10
PLIDA: Person Level Integrated Data Asset
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

Data Availability

The data used in this study are from the Person Level Integrated Data Asset (PLIDA). PLIDA combines information on health, education, government payments, income and taxation, employment and population demographics. PLIDA is governed by existing Australian Bureau of Statistics Privacy and Security protections. The access is limited to approved researchers. Therefore, the data used in our study are not available for sharing. Interested researchers can explore data access options with the Australian Bureau of Statistics (https://www.abs.gov.au/about/data-services/data-integration/access-and-services). Author-generated code used in this study is stored in a secure access environment managed by the Australian Bureau of Statistics. It is available from the authors upon request and subject to Australian Bureau of Statistics clearance.

Funding Statement

This work was supported by the University of Melbourne Early Career Researcher Grant (YY, https://www.unimelb.edu.au/) and Melbourne Disability Institute Postdoctoral Fellowship (YY, https://disability.unimelb.edu.au/). The funder did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004873.r001

Decision Letter 0

Jennifer Thorley

21 Jul 2025

Dear Dr Yang,

Thank you for submitting your manuscript entitled "Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

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Please re-submit your manuscript within two working days, i.e. by Jul 23 2025 11:59PM.

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Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Jennifer Thorley

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004873.r002

Decision Letter 1

Alexandra Tosun

24 Sep 2025

Dear Dr Yang,

Many thanks for submitting your manuscript "Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults" (PMEDICINE-D-25-02540R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, although the reviewers point out that the study addresses an important research question, they also raise critical concerns. After discussing the paper with the editorial team, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

In addition to these revisions, you may need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests shortly. If you do not receive a separate email within a few days, please assume that checks have been completed, and no additional changes are required.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Oct 15 2025. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don't hesitate to contact me directly with any questions (atosun@plos.org).

Best regards,

Alexandra

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

atosun@plos.org

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: This is a strong paper addressing an important and under-explored question - the increased cancer mortality among people with disabilities. It uses a large and well-structured dataset in Australia, which is an almost unique resource. The authors present both absolute and relative differences in cancer mortality, which is an advancement of the topic. There is a relatively stringent definition of disability - requiring assistance or supervision with daily activities - which means that people labelled with disabilities will be severely affected and so the inequities identified may be larger than for all people with disabilities. This limitation is acknowledged.

My main question is the focus on cancers due to lifestyle in the results and discussion. It seems to me that a lot of the inequalities related to screening-related cancers (breast, cervical, prostate, colorectal), which the authors could also consider further.

Moreover, the discussion was relatively long and could be cut by about a quarter. In particular, the section on future research was a little repetitive.

It is a small point - but in the introduction the authors could flag that another reason for higher cancer mortality among people with disabilities is that for specific conditions that are frequently disabling can also lead to cancer - such as Down Syndrome and leukaemia.

Is it possible for the authors to explore further the role of socio-economic differences between people with and without disabilities in cancer mortality inequities? If not, could the authors reference this limitation?

Reviewer #2: "Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults" analyzes the potential contribution of cancer, to the mortality gap between people with and without disabilities. It was found that people with disability had higher cancer mortality as compared to people without disability, and therefore proposed that interventions targeted towards people with disability should be prioritized across the cancer control pathway.

A strength of the study is its consideration of type-specific effects for cancer, with the reporting of absolute inequality estimates also raised as a novel contribution, that is relevant in resource allocation when compared to other potential contributors (e.g. neurological conditions). Concerns include the simplified definition and self-reported status of disability, and moreover the assignment of participants' disability status based on a single timepoint.

Some further issues might be considered:

1. In the Study Design and data sources subsection, it is stated that data was obtained from the 2011 Australian Census of Population for respondents aged 25 to 74 years (cutoff to minimize age-related disability/mortality) that responded to the disability question, together with ABS data on death registrations from 2011-2022 for mortality.

As supported by the following Disability status subsection, this appears to indicate that disability status is determined at a single timepoint (2011) for all participants. However, it appears likely that disability status may change during the course of the study, especially for older participants. It might therefore be carefully justified as to how this effect is considered, in analyzing and presenting the results. If possible, estimates might be provided for the prevalence of such changes in disability status.

2. Related to the above, it appears likely that disability status is dynamic and correlates with (cancer) mortality. For example, patients determined to be without disability when surveyed in 2011, would appear likely to be eventually considered disabled were they to develop (terminal) cancer afterwards. It might be discussed as to how such effects are addressed in the analysis.

3. In the Study Design and data sources subsection, it is further stated that only persons who responded to the disability question were included in this study. It might be clarified as to the census participation rate (in particular, whether it is mandatory), the disability question response rate (and any implications of the response, e.g. eligibility for disability programs/benefits), and whether this sampling may have been biased (e.g. against persons living in more-remote regions, or disabled persons themselves, etc.)

4. In the Mortality subsection, it is then stated that cancer and cancer type-specific mortality were determined using the underlying cause of death, according to ICD-10. It might be specifically clarified as to whether Australian reporting standards under ABS would include multiple (possible) causes of death, since this is especially material to the analysis.

5. In Figure 2, the presented disability statistics appear to indicate that significantly more than 50% of participants aged above 65 are disabled (as opposed to non-disabled), in 2011; however, SDAC data (https://www.abs.gov.au/statistics/health/disability/disability-ageing-and-carers-australia-summary-findings/latest-release) states that only about 52% of people aged 65 years and over have disability in 2022, despite a general trend towards increasing disability since 2018 (and likely 2011). This seeming large discrepancy might be clarified.

6. In the Statistical analysis subsection, the analysis for age-standardized mortality rates is described. However, it appears that potential covariates (e.g. weight, BMI, socioeconomic status, smoking/drinking/drug use, family history of diseases etc.) relevant to mortality are not accounted for. While the possible impact of socioeconomic disadvantage is discussed as a limitation, the lack of correction for other common covariates/confounders might be addressed. In particular, have previous studies considered these factors?

7. It might be instructive to also present all-cause mortality rates stratified by disability status (as in Supplementary Table 3).

8. The methods used for CI estimation might be briefly described in supplementary material.

9. Minor grammatical errors (e.g. "...compare[d] to those without", Line 291) might be corrected.

Reviewer #3: Thank you for the opportunity to review this interesting paper. This paper provides a clear and concise analysis of cancer mortality among people with disability using Australian mortality data. The analysis is comprehensive and well-documented, covering a wide range of aspects, including interpretations, limitations, and a discussion of inequalities indicators. I recommend that this paper would be published.

Reviewer #4: Thank you for the opportunity to review this national study out of Australia providing a descriptive epidemiology study of cancer mortality among people reporting disability compared to those who did not report disability. There remains an importance of continued documentation of differences in cancer outcomes for adults with disabilities globally.

Major concerns

1. Why use standardization to account for age distribution differences rather than using regression models to directly compare cancer-specific mortality rates over time between the group who reported a disability and the group that did not? As has been pointed out in other studies, using standardization to account for differences in age structure means that you cannot tell whether differences are due to a difference in age specific rates or a difference in population structure. As a result, drawing strong, actionable inferences from the data are not possible.

2. Cancer mortality is a function of both the incidence and fatality of a disease. Cancer mortality estimates or comparisons without accompanying accounts of cancer incidence are less valuable. Adding estimates of cancer incidence alongside the estimates of cancer mortality would significantly strengthen the value and impact of the included work. Otherwise, it is unclear whether differences in mortality rates between those reporting disability and those that did not is caused by a greater burden of the disease or a greater progression from or disparities in management following diagnosis. As a result, drawing strong , actionable inferences from the data are not possible.

3. Disability as defined in this study suggests that disability is not a static construct or medical diagnosis and therefore changes over time, as it uses a timeframe for determining its occurrence (>6 months) and removes folks experiencing more transient forms of disability. However, after that initial differentiation, the definition of disability used in the study assumes no change in disability status over a 15-year period. It's unclear that the measures used in the study have validity or reliability in such an extended time-period and would still be reflected at the time of a cancer-related death. It is also unclear how heterogenous this population is and therefore how to direct future implications or resources, when and to whom to prevent cancer mortality (or risk of developing cancer). As a result, drawing strong, actionable inferences is more challenging.

4. The authors previously published using the same cohort, including a comparison of cancer-related mortality (overall). It is unclear why the authors differently defined the cohort between the two studies, given the rationale provided in the study under review (to ensure disability was not the cause of mortality) and similarity of goals and purposes of the published study and the one under review. Age is a determinant of both cancer incidence and disability, and the authors aim to account for differences in the age distribution between those who report and did not report a disability by standardizing the population to the age distribution of those with a disability. Why would folks additionally need to be excluded on age?

5. The rationale for labeling and focusing on "lifestyle related cancers" inappropriately emphasizes the role of individual factors with the reported disability in cancer-related mortality and places blame or responsibility on the person for their circumstances, rather than the systems or structures in place that may result in that individual's "lifestyle" or cancer outcomes.

Any attachments provided with reviews can be seen via the following link: [LINK]

--------------------------------------------------------- ---

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PLoS Med. 2026 Jan 5;23(1):e1004873. doi: 10.1371/journal.pmed.1004873.r003

Author response to Decision Letter 2

13 Oct 2025

Attachment

Submitted filename: R1_Response.docx

pmed.1004873.s012.docx^{(54.4KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004873.r004

Decision Letter 2

Alexandra Tosun

9 Dec 2025

Dear Dr. Yang,

Thank you very much for re-submitting your manuscript "Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults" (PMEDICINE-D-25-02540R2) for review by PLOS Medicine.

Thank you for your detailed response to the reviewers' and editors’ comments. I have discussed the paper with my colleagues, and it has also been seen again by three of the original reviewers. The changes made to the paper were satisfactory to the reviewers. As such, we intend to accept the paper for publication, pending your attention to the editors' comments below in a further revision. When submitting your revised paper, please once again include a detailed point-by-point response to the editorial comments. The remaining issues that need to be addressed are listed at the end of this email.

In revising the manuscript for further consideration here, please ensure you address the specific points made by the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Dec 16 2025. However, if this deadline is not feasible, please contact us by email, and we can discuss a suitable alternative.

Sincerely,

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

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Comments from Reviewers:

Reviewer #2: We thank the authors for addressing our previous comments, particularly the sensitivity analyses on possible census response bias. The descriptive objective of the study is also noted, and might be emphasized in the manuscript.

Reviewer #4: The authors have thoughtfully responded to all questions and concerns. I have no further comments.

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Requests from Editors:

GENERAL

* Please confirm that your title complies with to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

* Statistical reporting: Please revise throughout the manuscript, including tables and figures.

- Please report statistical information as follows to improve clarity for the reader ""22% (95% CI [13,28]; p</=)"".

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ABSTRACT

* Please confirm that your abstract complies with our requirements, including providing all the information relevant to this study type https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-abstract

* Please quantify the main results (with 95% CIs and p values).

* Please confirm that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

* In the abstract, please include the important dependent variables that are adjusted for in the analyses.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

* We suggest changing the language from ‘cancer deaths’ to ‘cancer related deaths’. Please revise throughout.

* We suggest reporting numerators and denominators alongside percentages (as done in the Result section of the main text).

* We suggest reporting the average follow-up time (9.2 years on average) alongside the person-years.

* “We found similar-sized relative inequalities in mortality due to individual cancers in both sexes.” – between individuals with and without disability?

AUTHOR SUMMARY

* In the author summary, in the final bullet point of 'What Do These Findings Mean?', please include the main limitations of the study in non-technical language.

INTRODUCTION

* Please change the heading to ‘INTRODUCTION’.

METHODS AND RESULTS

* We suggested that you complete the GATHER checklist and report your study accordingly, but we cannot find a response to our request in the rebuttal.

* If you do not think that GATHER is the appropriate checklist for your study, please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: ""This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).""

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

* Figure 2: Please add 'years' as a unit on the y-axis.

* Table 1: For the ‘Rate Ratio’, please add ‘(95% CI)’ to define the statistical meaning of the numbers in brackets.

* “The mortality rate ratios ranged from 1.53 to 2.17 for females and 1.50 to 2.02 for males across individual cancers.” – We suggest referencing S2 Table here (again).

* Figure 4/5: Please convert any stacked bar charts to another data representation for example a table, or other type of graph. At minimum, please provide the values behind the stacked bars in supplementary tables.

* Figure 4/5: Please define ‘Other cancers’.

* Figure 5: In the description, please define ‘Obesity-related cancers’, ‘Alcohol-related cancers’, ‘Smoking-related cancers’ and ‘Lifestyle-related cancers’.

* Please confirm that you provided the unadjusted comparisons as well as the adjusted comparisons in all relevant Tables.

* Please specify the variables controlled for in all relevant Tables.

DISCUSSION

* Please remove the 'conclusions' subheading from the discussion. Please also remove any other subheadings from the discussion.

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General Editorial Requests

1) We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

2) Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

3) Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

PLoS Med. 2026 Jan 5;23(1):e1004873. doi: 10.1371/journal.pmed.1004873.r005

Author response to Decision Letter 3

10 Dec 2025

Attachment

Submitted filename: R2_Response.docx

pmed.1004873.s013.docx^{(40.6KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004873.r006

Decision Letter 3

Alexandra Tosun

11 Dec 2025

Dear Dr Yang,

On behalf of my colleagues and the Academic Editor, Wei Zheng, I am pleased to inform you that we have agreed to publish your manuscript "Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults" (PMEDICINE-D-25-02540R3) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript, and editorially there are only three remaining points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at atosun@plos.org.

Please see below the minor points that we request you respond to:

* Ethics statement: Please clarify in the main text whether the requirement for individual participant consent was waived by the University of Melbourne Human Ethics Committee.

* Title: We suggest including the study setting in the title. Editorial suggestion: Inequalities in cancer mortality between people with and without disability: a nationwide data linkage study of 10 million adults in Australia

* Thank you for adding the statement: “Author-generated code used in this study is available from the authors upon request”. We have added the statement to the data availability statement in the online submission form. We strongly encourage you to share your code via a repository that issues persistent identifiers, such as DOIs (e.g. on Zenodo). If you do so, we will be happy to update the Data Availability Statement accordingly.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email (including the editorial requests above). Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Methods. Supplementary Method.

(DOCX)

pmed.1004873.s001.docx^{(27KB, docx)}

S1 Fig. Age-specific mortality rates due to all cancer and specific cancer types according to disability status, females, age 25–74 years, Australia.

(DOCX)

pmed.1004873.s002.docx^{(258KB, docx)}

S2 Fig. Age-specific mortality rates due to all cancer and specific cancer types according to status of disability, males, age 25–74 years, Australia.

(DOCX)

pmed.1004873.s003.docx^{(232.1KB, docx)}

S1 Table. Lifestyle-related cancers based on assessments of the International Agency for Research on Cancer and the World Cancer Research Fund.

(DOCX)

pmed.1004873.s004.docx^{(25.9KB, docx)}

S2 Table. Crude and age-standardised cancer-specific mortality rates, rate differences, and rate ratios comparing people with and without disability, age 25–74 years, Australia.

(DOCX)

pmed.1004873.s005.docx^{(26.7KB, docx)}

S3 Table. Age-specific overall cancer and type-specific mortality rates, rate differences and rate ratios comparing people with and without disability, age 25–74 years, Australia.

(DOCX)

pmed.1004873.s006.docx^{(50.7KB, docx)}

S4 Table. Age structure and prevalence of disability by 5-year age group in people who were linked and not linked to the population spine.

(DOCX)

pmed.1004873.s007.docx^{(29KB, docx)}

S5 Table. Age-standardised overall cancer mortality rate differences and rate ratios from sensitivity analysis for missing disability status.

(DOCX)

pmed.1004873.s008.docx^{(24.8KB, docx)}

S1 Checklist. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) checklist.

(DOC)

pmed.1004873.s009.doc^{(101.5KB, doc)}

Attachment

Submitted filename: R1_Response.docx

pmed.1004873.s012.docx^{(54.4KB, docx)}

Attachment

Submitted filename: R2_Response.docx

pmed.1004873.s013.docx^{(40.6KB, docx)}

Data Availability Statement

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PERMALINK

Inequalities in cancer mortality between people with and without disability: A nationwide data linkage study of 10 million adults in Australia

Yi Yang

Nina Afshar

Zoe Aitken

Rebecca J Bergin

Peter Summers

Roger L Milne

Sue M Evans

Anne Kavanagh

George Disney

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Study design and data sources

Fig 1. Flowchart of data linkage and cohort construction of Census 2011 respondents and death registrations from 2011 to 2022.

Disability status

Mortality

Demographic information

Statistical analysis

Results

Fig 2. Age structure of females and males according to disability status, age 25–74 years, 2011, Australia.

Age-standardised mortality rates and inequalities

Table 1. Crude and age-standardised all-cancer mortality rates, rate differences, and rate ratios comparing people with and without disability, age 25–74 years, Australia.

Fig 3. Age-standardised cancer-specific mortality rate ratio and rate difference comparing females and males with and without disability, age 25–74 years, Australia.

Age-specific mortality rates and inequalities

Fig 4. Specific cancers contributing to the differences in all-cancer mortality rates for people with and without disability in 5-year age groups, age 25–74 years, Australia.

Fig 5. Lifestyle-related cancers contributing to the differences in all-cancer mortality rates for people with and without disability in 5-year age groups, age 25–74 years, Australia.

Sensitivity analysis

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Jennifer Thorley

Roles

Decision Letter 1

Alexandra Tosun

Roles

Author response to Decision Letter 2

Decision Letter 2

Alexandra Tosun

Roles

Author response to Decision Letter 3

Decision Letter 3

Alexandra Tosun

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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