https://orcid.org/0000-0001-6921-6617
Figures
Abstract
Introduction
The gap in bowel cancer screening participation rates between the lowest socioeconomic position (SEP) groups and the highest in Australia is widening. This study estimates the long-term health impacts and healthcare costs at current colorectal cancer (CRC) screening participation rates by SEP in South Australia (SA).
Methods
A Markov microsimulation model for each socioeconomic quintile in SA estimated health outcomes over the lifetime of a population aged 50–74 years (total n = 513,000). The model simulated the development of CRC, considering participation rates in the National Bowel Cancer Screening Program and estimated numbers of cases of CRC, CRC deaths, adenomas detected, mean costs of screening and treatment, and quality adjusted life years. Screened status, stage of diagnosis and survival were obtained for patients diagnosed with CRC in 2006–2013 using data linked to the SA Cancer Registry.
Results
We predict 10915 cases of CRC (95%CI: 8017─13812) in the lowest quintile (Q1), 17% more than the highest quintile (Q5) and 3265 CRC deaths (95%CI: 2120─4410) in Q1, 24% more than Q5. Average costs per person, were 29% higher in Q1 at $11997 ($8754─$15240) compared to Q5 $9281 ($6555─$12007). When substituting Q1 screening and diagnostic testing rates with Q5’s, 17% more colonoscopies occur and adenomas and cancers detected increase by 102% in Q1.
Citation: Lal A, Gao L, Tan E, McCaffrey N, Roder D, Buckley E (2022) Long-term benefits for lower socioeconomic groups by improving bowel screening participation in South Australia: A modelling study. PLoS ONE 17(12): e0279177. https://doi.org/10.1371/journal.pone.0279177
Editor: Edward Jay Trapido, LSU Health Sciences Center New Orleans: Louisiana State University Health Sciences Center, UNITED STATES
Received: February 3, 2022; Accepted: November 27, 2022; Published: December 21, 2022
Copyright: © 2022 Lal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support the findings of this study are available from SA Department of Health and Wellbeing and the Australian Institute of Health and Welfare (AIHW) but restrictions apply to the availability of these data, which were used under license for the current study, imposed by the South Australian Human Research Ethics Committee (HREC/14/SAH/145) and are therefore not publicly available. Requests for access to the data can be made to the Australian Institute of Health and Welfare. Requests for data can be sent to SA NT DataLink SANTDataLink@unisa.edu.au and the AIHW Data Linkage Unit linkage@aihw.gov.au.
Funding: This study received financial support from the Australian National Data Service for data linkage and project activities. D.R. receives support from Cancer Council’s Beat Cancer Project on behalf of its donors. N.M is partly funded by the Cancer Council Victoria. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Colorectal cancer (CRC) has the second-highest disease burden of cancers in Australia [1] and the highest health system expenditure of cancers at $1.1billion [2]. Australians living in the most disadvantaged areas of Australia have the highest incidence rate of CRC (61 compared to 52 per 100,000 for the most advantaged) and the lowest 5-year observed survival (57% compared to 63% for the most advantaged) [3]. Early detection using faecal occult blood test (FOBT) screening significantly improves CRC survival outcomes [4]. The National Bowel Cancer Screening Program (NBSCP) was introduced in Australia in 2006. Age-specific participation rates of screening have been increasing however, Australians from the lowest socioeconomic group had a lower participation rate (40%) compared to those in the highest group (45%) in 2019 [7].
The NBSCP includes people aged 50–74 who are invited biennially to participate and is provided free of charge under Medicare, Australia’s universal health insurance scheme. Medicare is funded by the Commonwealth Government through a taxpayers’ levy and all Australian citizens and permanent residents are eligible. Bowel cancer screening kits are delivered to peoples’ homes, are self-administered, include a postage paid return envelope and participants do not have to see a health professional.
In countries with universal healthcare such as Australia, the UK and Canada, financial barriers to screening are less likely to play a role, however socioeconomic inequalities in screening participation are evident [9, 10]. Todorov and colleagues [5] identified one of the main reasons reported for differences in participation by lower socioeconomic groups in South Australia was the lack of understanding that screening is effective when no symptoms are present. Limited health literacy (i.e. a low capacity to obtain, process, understand and use healthinformation) disproportionately affects people from lower socioeconomic positions [6]. Limited health literacy is a significant barrier to bowel screening that influences screening knowledge, beliefs and behaviour [7].
A main aim of government screening strategies in Australia is improving equity of access for disadvantaged groups [8]. Studies from Australia that have modelled the long-term health outcomes and cost-effectiveness of the NBCSP have focused on the whole population without an equity lens. At current participation rates the NBCSP is underutilised and substantial additional investment could be spent on improving participation while still remaining cost-effective [9]. The estimated maximum spending levels of effective interventions to improve screening rates from 40% to 60%, was estimated to be AUD$214–$502 per person and AUD$44–$91 per person to improve the diagnostic assessment rate (colonoscopy following positive test) [16].
Variation in CRC outcomes could be reduced if screening participation improved in priority population groups. This primary aim of this study is to determine the long-term health and healthcare costs disparities of CRC across socioeconomic groups in SA. The eligible screening population of the NBCSP will be examined from a healthcare perspective. The findings will provide evidence of long-term equity impacts for disadvantaged groups and whether enhancements to boost program outcomes for the less screened populations should be implemented, thereby improving health equity. The outcomes of this study will also inform the National Bowel Cancer Screening Program.
Methods
National Bowel Cancer Screening program
Intervention description.
In 2019, residents of Australia aged 50–74 enrolled in Medicare, were invited biennially to participate in the NBSCP via mail. Therefore, the NBCSP invited Australians aged 50, 52, 54, 55, 58, 60, 62, 64, 66, 68, 70, 72 and 74 to participate. The eligible population are sent an FOBT kit, an information booklet and a reply-paid envelope. The test involves collecting two separate samples of faeces. After completing the test, participants mail the samples to the NBCSP pathology laboratory for analysis. The test is free of charge for participants.
Study population
The simulated population of 513,000 is equal to the estimated 2019 South Australian resident population aged 50 to 74 years [10]. The model assumes there are 102,600 people in each Socioeconomic Index for Areas (SEIFA) quintile. The SEIFA quintiles represent groups of individuals who live in similarly ranked areas, based on a range of information such as the income, qualifications, and occupation skills of the area residents [11]. Proportions of the population in each age group were sourced from the Australian Bureau of Statistics (ABS) and were assumed to be the same for each quintile [12]. (S1 Table) The microsimulation model walks the same cohort of 102,600 people through each SEIFA quintile arm of the model over a 50 year time horizon.
Health benefit modelling
Overview.
We developed the Priority Population Microsimulation Colorectal Cancer (PRISM-CRC) model, which consists of five identically structured markov nodes to estimate the lifetime costs and benefits of the NBSCP by SEIFA quintile. The base case estimates the health outcomes–cases of CRC, mean quality adjusted life years (QALYs) gained, deaths and mean healthcare costs from current screening rates by SEIFA quintile. The second analysis estimates the health outcomes if screening rates of the more disadvantaged quintiles are replaced with those of the most advantaged quintile to mimic the addition of a targeted screening intervention aimed at lower SEIFA quintiles. The simulated cohort moves through the Markov model in 1-year cycles until death. At the end of each 1-year period the individual person either moves to a different health state or stays in the current health state. The model consists of three parts 1) natural cancer development 2) screening and 3) colonoscopy surveillance. Participants start in either screening or natural cancer development. A summary of the key input parameters used by Priority Population Microsimulation Colorectal Cancer (PRISM-CRC) model is provided in Table 1.
Colorectal cancer model.
The first component of the modelling is the natural development of CRC, which influences the movement of patients to various health states. There are seven health states within the model
- Free of disease
- Low risk adenoma
- High risk adenoma
- Stage A CRC
- Stage B CRC
- Stage C CRC
- Stage D CRC
The stage of cancer is classified according to Australian Clinico-Pathological Staging (ACPS) system from A to D and indicates how far the cancer has spread anatomically [27]. Similar to the American Joint Committee on Cancer (AJCC) staging system, there are four main stages. Stage A of the ACPS translates to American Joint Committee on Cancer (AJCC) Stage grouping I, Stage B translates to IIA-IIC, Stage C translates to IIIA-IIIC and Stage D translates to IVA-IVC [28]. The model categorises the stage as either diagnosed or undiagnosed. In the non-screened group,diagnosed cancer will occur when a patient presents with symptoms (i.e., diagnosed at a more advanced stage compared to screened). The transition probabilities from one state to another attempt to capture the natural history of cancer. Life expectancy of the patient depends on the five-year stage-specific survival figures. Participants who are free of disease or have non-progressive adenoma for five years or more can die of other deaths determined by background mortality rates.
There are several assumptions in the model. The underlying risk of CRC remains constant using SA population CRC incidence rates in 2017. Patients who survive more than five years after diagnosis are assumed to be cancer survivors and have general population mortality rates with no recurrent CRC. The model does not account for the people with increased risk of CRC due to family history and assumes a maximum age of 100 years.
CRC screening.
The screening pathway is illustrated in Fig 1. Screening aims to disrupt the natural progression of CRC to prevent CRC or detect CRC early and increase life expectancy. The model estimates screening and diagnostic pathways based on participation rates, screening test results and adenoma and CRC incidence. In the screened group adenomas and non-symptomatic cancer may be diagnosed by FOBT test and colonoscopy. The eligible aged population are invited to be screened and if they participate, they receive either a positive or negative FOBT. The model assumes that all FOBT tests are completed correctly. If the result is negative, participants are invited to screen again in the next round. If the result is positive, participants either consult a general practitioner to obtain a referral for colonoscopy for further diagnosis or take no action. Those who take no action after a positive FOBT are moved to the natural cancer development arm and are assumed to be diagnosed without screening.
Markov models were used to track patients in the SA population aged 50 to 74 through the NBSCP from the point of participation in screening to death or aged 100 years. People without CRC may undergo multiple sceenings during the simulated time horizon until they reach age 75, unless they have died earlier due to other causes. S1 Appendix describes the PRISM-CRC model in detail.
Screening participation rates. To calculate participation rates by SEP, participation rates for South Australian residents by statistical area level 2 (SA2) as classified by the ABS, which align with one or more related suburbs [18] were matched with the ABS, Index of Relative Socioeconomic Disadvantage, Socioeconomic Index for Areas (SEIFA) deciles [29]. SEIFA deciles represent groups of individuals who live in similarly ranked areas, based on a range of information such as the income, qualifications, and occupation skills of the residents. SEIFA deciles were combined to create quintiles
Data sources.
A linked data platform was used for all patients in SA diagnosed with CRC (ICD10 18–20) as recorded by the South Australian Cancer Registry (SACR). Cancer patient records were linked to the NBCSP through the Australian Instutute of Health and Welfare (AIHW) and the National Death Index. This analysis uses patient records from 1 January 2006 to 31 December 2013.
Diagnostic variables.
Positivity rates of FOBT screening and subsequent colonoscopy rates by age and SEIFA observed in 2018 for Australia from the AIHW NBCSP monitoring report were used [19]. We then used the reported diagnostic results from the colonscopies due to the program, to calculate the probabilities of adenoma, cancer and no disease in the model [24].
Clinical variables.
Details of the main clinical variables used in the modelling are displayed in Table 1. The prevalence of adenoma and CRC for the unscreened population was estimated using SA population CRC incidence rates in 2017 for the lifetime of the model [13]. Incidence of adenoma and progressive adenoma was calculated using the age specific incidence of CRC and taken back to when an adenoma is likely to have developed. The time from progressive adenoma incidence to cancer diagnosis is assumed to be 21 years, based on an average from previous CRC modelled studies [14, 15]. The estimated proportion that develop into malignancies is 24% [16]. Progressive adenoma incidence was subtracted from all adenoma incidence to obtain the low risk adenoma incidence [16]. As there are no published quintile specific incidence rates by age, rate ratios based on SEIFA quintile and population incidence rates were calculated and used to adjust the population CRC incidence rates by age. For the screened population, adenoma and cancer prevalence data from colonoscopy assessment outcomes reported by the AIHW [17] were used. At diagnosis, the proportions in each stage of CRC by screening status were extracted from the SA linked dataset.
Once the participant has entered the model, they move to another health state based on the probability of progression, calculated on a yearly basis. For the unscreened population, the natural history of adenomas was reproduced using three transition probabilities: adenoma incidence, transition from low to high-risk adenoma and transition from high-risk adenoma to CRC. Transition probabilities from low to high-risk and high-risk adenoma to CRC and CRC stage were based on Frazier et al [30] which have been estimated by calibration to U.S cancer incidence and stage distribution. Probability of diagnosis at each stage was estimated based on the percentage of people at each stage of cancer in the unscreened population using the methods reported in a previous CRC modelling study [31]
Survival.
When a participant is diagnosed with CRC, they either remain in that health state (cancer stage), progress to a more advanced cancer stage or if they survive more than 5 years they move to a ‘recovered’ health state. The life expectancy is calculated based on 5-year survival rates extracted by stage from the SA linked data set. If a participant survives more than 5 years, they are assumed to have the same life expectancy as the average population. Non-CRC mortality by age was obtained by subtracting the CRC mortality rate in SA 2016 [32] from the all-cause mortality rate in Australia 2016–2018 [33].
Outcomes.
Quality adjusted life years (QALYs) were used as a measure of any health gains associated with the NBCSP. QALYs are calculated by multiplying the utility weight associated with a health state (represents quality of life) by the length of life lived in that health state (represents quantity of life) [34, 35]. Utility-weights are measured on a scale from 0 to 1, in which ‘0’ represents being dead and ‘1’ refers to perfect health. In this study, utility scores based on a meta-analysis by CRC stage were used, with stages A to C valued at 0.74 and stage D valued at 0.68 [36]. Australian population norm utility scores, for the population without CRC were valued at 0.80 for ages 50–69, 0.76 for ages 70–79 and 0.70 for 80 years and over [37]. QALYs were discounted at a rate of 5%.
Healthcare costs
The reference year for costs is 2019. The FOBT screening costs include the FOBT kit, return postage and laboratory processing for Medicare Benefits schedule (MBS) item 66767. If a patient needs a colonoscopy they are referred to a general practitioner for a consultation costed as MBS item 23 [22]. The cost of colonoscopy procedure was assigned based on whether a polypectomy was performed and whether complications occurred. Treatment costs were based on CRC stage taken from an Australian study [24] that looked at cost of cancer therapies. Half the patients in Stage 4 receiving chemotherapy also received bevacizumab medication at a cost of approximately $10,000, therefore an average of the cost of treatment with and without bevacizumab was used. Costs were inflated to 2019 using the AIHW total health price index [25]. A discount rate of 5% was applied to costs.
Uncertainty and sensitivity analyses
Uncertainty analyses were undertaken to evaluate how robust the results are to the assumptions made in the analysis. A probabilistic multivariate uncertainty analysis was conducted using Monte Carlo simulation method. Means and 95% uncertainty intervals for incremental costs, are reported based on 2000 iterations of the model using TreeAge software. Uncertainty parameters are presented in Table 1.
We also ran the model with a start age of 50 over a 35 year period to validate the results with CRC incident cases and deaths from the South Australian Cancer Registry in 2017 [13].
Results
Within the context of the National Cancer Bowel screening program for the South Australian population aged 50–74, 10915 (95%CI: 8017─13812) cases of CRC are predicted in the lowest quintile Q1, 2580 (31.0%) more cases compared to the highest (Q5) (Table 2). In terms of mortality, when Q5 is compared to the lower quintiles, 692 (26.9%) more CRC deaths occur in Q1 and 609 (23.7%) more deaths in Q2. One of the largest differences is the estimated number of colonoscopies not taken up following a positive FOBT test in the lower quintiles, 1232 (80.2%) fewer colonoscopies in Q2 than Q5 and 1073 (69.8%) fewer colonoscopies in Q1 than Q5. Further, there were 29.7% and 12.7% fewer adenomas and CRCs detected by screening in Q1 and Q2 respectively compared to Q5.
Average costs per person, were over 25% higher in the two most disadvantaged quintiles, reflecting more cases and later stage treatment costs in these groups. Average quality adjusted life years (QALYs) per person, were slightly lower in the most disadvantaged quintiles with differences of 0.06 and 0.04, around 0.05% less compared to the most advantaged quintile. In a quintile population of 102,600 people, this equates to 6156 and 4104 QALYs respectively.
Figs 2 and 3 illustrate the predicted cases of CRC and deaths from CRC over the 50-year period for the cohort aged 50 to 74 starting in 2019. The SEIFA quintile gradient is apparent, with higher cases and deaths in the lower quintiles and a larger difference between Q5 and the lower quintiles.
When substituting the screening participation and follow up colonoscopy rates for Q1 and Q2 with Q5 rates, adenoma or cancers detected increased by 445 (101.5%) in Q1 and 268 (49.2%) in Q2. The percentage difference of cancer cases reduced by 466 (4.3%) and 387 (3.7%) in Q1 and Q2 respectively. Follow up colonoscopies after a positive result increased by 445 (17.0%) and 779 (28.1%) in Q1 and Q2 respectively.
Model validation
The model predicts 48,859 CRC cases over 50 years, i.e. 977 per year for the cohort of 513,000, slightly lower than the most recently reported new CRC cases in 2017 of 1078 in SA for persons aged 50 and over [22]. This difference is likely due to the lower number of people aged 50–74 at the beginning of the 50-year period in the fixed, modelled cohort and the additional new cases (approximately 150) reported in the ages outside the screening age groups in the ages of 75–79, 80–84 and 85 and over in the SA population [22].
Sensitivity analysis
The reported cases of CRC deaths in 2017 from the South Australian Cancer Registry was 387 with 118 deaths occurring at age 85 and over [22]. The model predicts a total of 14,801 CRC deaths over 50 years, or an average 296 deaths per year, lower than the predicted deaths, due to the cohort size. However, when using a start age of 50 years and a time horizon of 35 years, 13,961 deaths are predicted or 399 deaths per year which is close to the 387 reported deaths in 2017 by the South Australian Cancer Registry [22].
Discussion
We found that at current levels of bowel cancer screening by socioeconomic quintile in SA for the cohort aged 50 to 74 years, there are 2580 (31%) and 2258 (27%) more cases of bowel cancer and 692 and 609 (27% and 24%) more deaths in the two lowest socioeconomic quintiles (Q1 and Q2) compared to the highest (Q5). Even when screened, the two lowest quintiles have 1073 and 1232 (70% and 80%) fewer follow-up colonoscopies than those from the highest quintile, contributing to higher case numbers, more advanced CRC cases and deaths. Our modelling of the impact of increasing screening rates and colonoscopy rates of the lower quintiles to match those of Q5 indicated potential increases in adenoma or CRC detected of 445 (101.5%) and 268 (49.2%) in Q1 and Q2 respectively.
The higher deaths and cancer cases predicted in the lower quintiles are unsurprising given differences in screening rates, diagnosis, lifestyle risk factors, treatment and outcomes and access to healthcare services compared with the higher quintiles [38, 39]. The difference in screening rates between Q1 and Q4 and Q5 in SA of approximately 10% contributes to the widening socioeconomic differences in CRC outcomes, and the gap in screening rates appears to be widening. A previous study of NBCSP screening rates in SA using data from 2007–8 found that those from the most deprived quintile had 8% lower participation rates (40%) than the least deprived (48%) [40]. Our findings from 2018–19 data indicate that in the last decade screening rates have increased, but the least deprived have increased their rates of screening about 2% more (51.7%) than the most deprived (41.9%), a difference of 9.8%. The differences in screening rates undoubtedly result in the differences in stage at diagnosis; there were 12% less unscreened patients in Stage 1 and 12% more in Stage 4 than screened patients in the linked dataset. By improving screening rates, detection at earlier stages is more likely.
Overcoming the barriers to screening in disadvantaged groups remains a challenge. Negative beliefs particularly prevalent in this group, such as cancer screening being threatening, difficult to accomplish and not beneficial, need to be countered [41]. Lack of social norms are also predictors of non-participation in cancer screening [41]. Improving health literacy will help patients increase their knowledge and understanding of bowel cancer screening, leading to an increase in screening uptake, a decrease in the social inequalities in screening and potential inequalities in survival [7, 42].
A previous study examining a similar dataset from SA for CRC patients in 2003─2008 found the hazard ratio for death to be 0.75 (95% 0.62─0.91) for the highest socioeconomic quintile, or a 25% reduction in risk of death compared with the lowest [43]. These findings are similar to the mortality differences we found between the higher and lowest quintiles. Previous studies that have modelled the long-term health outcomes of CRC in Australia have not considered socioeconomic differences, making comparison of our results challenging [14, 31, 44–46]. Comparing the outcomes of our study with others is possible when similar costs, outcomes and participation rates and assumptions have been used. Of these five previous studies, four are not directly comparable due to either not accounting for treatment or colonoscopy surveillance costs or using outdated costs of treatment [31, 44–46]. The study by Lew et al [14] is the most comparable using recent cost estimates and a 40% participation rate scenario, comparable to our participation rates of 42% in quintile 1. Lew and colleagues estimated mean life years per person of 15.597, increasing to 15.610 for 60% participation or an increase of 0.08%. Our QALY estimates were 12.86 in Q1, 0.5% lower than Q5 at 12.91, are expected to be lower than the Lew study as ours are adjusted for quality of life, but percentage differences between the quintiles are close to the Lew et al study [14].
A strength of our study is the use of data extracted from a population-based linked dataset containing CRC patients from SA using multiple health and administrative datasets. This included screening status of patients, stage of diagnosis by SEIFA and survival by cancer stage. Unfortunately, not all data was available by SEIFA due to small cell sizes, however national data by SEIFA was used where available. We were unable to obtain more recent data for the South Australian population as this was not publicly available, therefore the number of deaths may be overstated. However, the socioeconomic disparities would likely be relatively very similar [47]. As with all modelling studies there are limitations. The proportions in each stage of cancer extracted from the linked dataset, were assumed to remain constant. This may over or under estimate future outcomes. We did not use sensitivity and specificity rates of FOBTs and colonoscopies, as the reported diagnostic results were used to calculate the probabilities of adenoma, cancer and no disease in the model [17]. However, based on data from the 2006–2010 NBCSP participants, less than 0.1% of those with a negative screen were diagnosed with bowel cancer within 2 years [4]. We did not account for costs or health outcomes of adverse events of colonoscopy therefore these will be underestimated. We also assumed that patients who did not have a follow-up colonoscopy after a positive FOBT moved to and remained in the not screened group, with a higher risk of late-stage cancer. The model assumes the risk of CRC remains stable over the 50 year period.
Conclusions
At current levels of screening, we see the long-term socioeconomic differences in higher rates of CRC and deaths in lower SEP groups in SA over 50 years. Also apparent are the higher rates of assessing individuals with positive FOBT tests and subsequent early detection of adenomas and cancers in higher SEP quintiles. Applying these results nationally, implementing targeted programs to increase screening and follow up tests in disadvantaged groups is critical in improving CRC outcomes population-wide. Evaluating these programs for their effectiveness and cost-effectiveness needs prioritising. Ultimately this would lead to better cancer-related outcomes amongst disadvantaged groups, leading to improved long-term survival and reductions in the emotional, social and economic burden borne by themselves, their families and the healthcare system.
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