Exploring the impact of Medicare Benefits Schedule changes on breast cancer screening in Australia

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Olaleye, Enamul Kabir, Rasheda Khanam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5345881/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer is a major global health concern, with substantial mortality and incidence rates. This study aims to assess the impact of these MBS policy changes on breast cancer screening services in Australia. Methods An interrupted time series (ITS) analysis, incorporating segmented regression and Autoregressive Integrated Moving Average (ARIMA) models, was employed to evaluate the impact of MBS policy changes. Results The introduction of 3DBT led to a significant decrease in mammography scans but did not alter the total number of breast cancer screening scans. The second policy change in May 2020, restricting eligibility criteria, did not show a significant impact on the number of scans or associated benefits. Conclusion The study suggests that the introduction of 3DBT for breast cancer screening in Australia led to a shift in imaging modalities with associated cost increases. However, the policy change aimed at restricting eligibility criteria did not result in a significant reduction in costs. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Breast cancer is the most prevalent cancer worldwide and continues to have a significant impact on the number of cancer-related fatalities globally ( 1 , 2 ). Female breast cancer was the primary cause of cancer worldwide in 2020, with an estimated 2.3 million new cases. Breast cancer also accounted for more than 25 percent of all cases of cancer in women in 2020. Having surpassed lung cancer as the most frequently diagnosed cancer, breast continues to be a significant cause of morbidity and mortality in all settings. In 2020, breast cancer was estimated to be the cause of mortality for 685 000 women, representing one out of every six cancer-related deaths among women ( 1 , 2 ). Furthermore, the incidence of breast cancer was nearly double in high-income countries (55.9 per 100,000) like Australia compared to middle- and low-income countries (29.7 per 100,000) in the same year ( 1 ). In Australia, about 13% of females will be diagnosed with breast cancer by the time they turned 85 ( 3 ). Breast cancer is the second most commonly diagnosed malignancy in Australia. It is the most prevalent cancer in Australian women (excluding non-melanoma skin cancer) and the second most common cause of cancer-related mortality in women, after lung cancer. In 2022, breast cancer was estimated to have been diagnosed in over 20,600 individuals in Australia, with the median age at diagnosis being 62 ( 3 , 4 ). In the past few decades, the incidence of breast cancer has been on the rise ( 3 , 4 ) with 1,480 women aged 50 to 74 lost their lives due to breast cancer in 2019, equivalent to 43 deaths per 100,000 women ( 5 ). The goal of current medical efforts is to lower the death rate from breast cancer by prevention, raising awareness, early identification through screening, and treatment. Screening for cancer of the breast is an effective method for detecting early-stage disease and improving cancer patients' survival rates ( 6 , 7 ). In recent decades, a number of industrialized economies have implemented population-based breast cancer screening programmes, which have led to a decline in mortality and the incidence of advanced disease ( 8 ). Mammography, a medical imaging technique that utilizes low-dose X-rays to create detailed images of breast tissue, is frequently used for breast cancer screening. Mammograms (images from mammography) can aid in the early detection of breast cancer using early breast signs including tumors and abnormal growths. Mammography seeks to detect breast cancer at an early, curable stage( 6 , 9 ). Early and accurate diagnosis of breast cancer is essential for effective treatment and for improved prognosis. Mammography remains a popular breast imaging study. Its known sensitivity issues stem from the misinterpretation of architectural distortion and asymmetric density, as well as the fact that the cancer is covered by fibro- glandular tissue, which obscures the cancer margins. Therefore, high breast density significantly reduces the sensitivity of mammography ( 10 ). Three-Dimensional Breast Tomography (3-DBT) is a novel imaging technique for breast cancer screening that improved the detection of abnormalities, particularly in dense breast, and the diagnosis of benign lesions. 3-DBT also allowed for visualization of cancers not visible by conventional mammography ( 11 ). BreastScreen Australia is the national initiative for breast cancer screening. Through an organized approach to early identification of breast cancer using mammography as a screening tool to detect undiagnosed breast cancer in women, this initiative seeks to reduce breast cancer-related illness and mortality. Early detection affords the opportunity for early treatment, thereby reducing the likelihood of illness and mortality. Every two years, women aged 40 and older are eligible for mammograms( 5 ). In its bid to improve quality and coverage of health care services, optimize costs of health care services, and improve health of the population, the Government of Australia implemented a number of changes in its policy on Medicare Benefits Schedule with respect to mammography and breast screening between 2017 and 2022 ( 10 – 14 ). While many of the changes were motivated by the recommendations of a high-level scientific expert group recommendations ( 11 ), the impacts of the changes on healthcare services have not been adequately evaluated in literature. Therefore, this study aims to evaluate the impact of changes in Medicare Benefits Schedule policy on breast cancer screening in Australia. Quantitative Analysis of Policy Impact: The study provides a comprehensive quantitative analysis of the impact of two specific policy interventions related to breast cancer screening imaging on screening practices and healthcare expenditure in Australia. By examining trends in screening scans and benefits paid before and after each policy change, the study offers valuable insights into the effects of these interventions on healthcare delivery. Focus on Screening Technologies: The study places particular emphasis on the role of advanced screening technologies, such as 3D Breast Tomosynthesis, in shaping breast cancer screening practices. By highlighting the predominance of 3DBT scans and their impact on screening trends, the article underscores the importance of technological advancements in improving early detection and diagnosis of breast cancer. Methods The study aims to evaluate the impact of the MBS policy changes (1 November 2018 and 1 May 2020) on breast cancer screening services including: i) Was there a significant change in the overall number of breast cancer screening conducted (mammogram and 3DBT scans) in Australia due to the policy changes? and ii) Was there a significant change in the costs of MBS reimbursements of breast screening services as a result of the policy changes? The study separately evaluated the impacts of both MBS policy changes. Study design The study used an interrupted time series (ITS) analysis design. The ITS design relies on data acquired at multiple points over a period of time (often referred to as time series data) prior to and following an intervention to determine a link of causality between an intervention and an outcome of significance ( 15 , 16 ). ITS design entails comparing the observed outcome post intervention with the counterfactual ( 17 ). One of the most commonly used statistical methods for modelling ITS analysis is the segmented regression model ( 18 , 19 ). This model is often defined as follows ( 20 ): The segmented regression model used in interrupted time series analysis typically involves two segments: one before the intervention (the "pre-intervention" period) and one after the intervention (the "post-intervention" period). The equation for such a model can be represented as follows: Y = β_0 + β_1.t + β_2.intervention + β_3.t_2 + ℇ Where Y represents the value of the outcome variable at time t; β0 is the intercept term, representing the baseline level of the outcome variable at the beginning of the pre-intervention period; β1 represents the slope of the trend of time during the pre-intervention period; Intervention is a binary variable that indicates whether the intervention has occurred (usually coded as 0 for pre-intervention and 1 for post-intervention); β2 represents the immediate effect of the intervention on the outcome variable (the change in the intercept at the time of the intervention); β3 represents the change in the slope of the trend after the intervention; the time point at the moment in question is represented by the continuous variable time, t; t2 reflects the time after intervention has commenced, 0 before the intervention, t minus time elapsed (in measured units) since intervention will be the time after intervention commencement); ℇ signifies the error term at time t, assumed to be normally distributed with mean 0 ( 19 , 20 ). The use of segmented regression analysis has been criticized in literature as health data patterns may be obscure or difficult to identify, with substantial variation. Hence, outcomes of health interventions may not be linear. Furthermore, issues of autocorrelation and distributed residuals may be major limitations to the use of segmented regression in ITS analysis in the evaluation of health interventions ( 19 ). ARIMA modeling is a statistical technique within ITS analysis that assesses intervention effects while considering time trends and confounding factors ( 19 ). The standard notation for ARIMA models is ARIMA (p,d,q), where p represents the order of the autoregressive process, d, the level of differencing, and q, the ordering of the moving average process. These are positive integer parameters. The ARIMA model allows for autocorrelation, seasonality, as well as other structured variations in outcomes. The intervention-based analysis in the ARIMA model is not limited to modeling variations in level and slope alone; rather, it can be employed to evaluate complex trends that occur as a result of the intervention ( 1 ). ARIMA modeling consists of three stages for identifying the best model for estimation and explanation. First, we determine the differencing order, denoted by d. The second step diagnoses and determines the autoregressive and moving-average parameters (p and q terms) of the ARIMA model. Third, the residuals (noise) are diagnosed ( 21 ). Interventions and outcomes This study assesses the impact of the two MBS policy changes regarding breast cancer screening in Australia from 2017 to date. These include: i) Introduction of MBS items for breast tomosynthesis (1 November 20218) ( 13 ); and ii) Revision of the eligibility criteria for MBS reimbursement for breast cancer screening by radiologic imaging (1 May 2020) ( 15 ). Breast cancer monitoring is intended to lower breast cancer deaths. As a screening technique, mammography enables the identification of breast cancer that has not yet manifested symptoms. In the MBS, reimbursements for breast mammography are recorded using items "59300" and "59303"( 22 ). The Medicare billing codes "59300" (bilateral mammogram) and "59303" (unilateral mammogram) reflect breast cancer screening services. In November 2018, the Australian government amended its policies regarding mammography-based breast cancer surveillance. These modifications involve the addition of two new time-limited MBS items ("59302" and "59305") for 3D-breast tomography (3DBT) and the removal of two items ("60100" and "60101") for conventional plain film tomography. The 3DBT is a relatively novel digital mammography technique that generates a 3D image of the breast using multiple X-rays taken from various angles. Despite being an established practice, this technological advancement has not yet been evaluated for safety, efficacy, or cost-effectiveness. Although some patients access 3DBT services, there were no corresponding MBS items until November of 2018( 13 ). Prior to the MBS policy change in 2020, the criteria for claiming MBS items for mammography and 3DBT were as follows: i) a previous diagnosis of the cancer of the breast in the patient or relatives; or ii) manifestations or signs of breast malignancy discovered during the healthcare visit of the patient ( 11 , 12 ). By 1 May 2020, the requirement for claiming MBS rebates for breast cancer imaging screening services included a restriction of the past occurrence of a malignancy to the patient, such that incidence of breast malignancy in a first-degree relative became more significant than incidence of the same in the grandmother ( 12 ). Hence, the interventions considered were the two MBS policy changes already described above. The primary outcomes considered in this study were the aggregate sums of Australian dollars spent on bilateral and unilateral mammograms, as well as 3DBT scans performed during the defined periods. The benefits (in Australian dollars) paid by Services Australia (the institution responsible for Medicare administration) were also considered in the index period as a primary outcome in the study. Data collection Publicly available MBS items labelled "59300" (unilateral mammogram), "59303" (bilateral mammogram), "59302" (unilateral 3DBT), and "59305" (bilateral 3DBT) ( 23 ) were the outcomes used in study. Both mammograms and 3DBT scans are being used for breast screening imaging since 1 November 2018 when 3DBT MBS items were introduced, aggregate sum of "59300"and "59302” as well as that of "59303" and "59305" were calculated. Furthermore, the respective costs (benefits – Medicare contribution to service) were also estimated for the defined period. Statistical analysis The objective of ITS research when used for evaluating policies is to estimate the "effect of intervention" or the impact of implementing the intervention on a specific outcome. While there are numerous categories of impacts that can be observed, this study will focus on the three of the most common impacts: step change, pulse, and ramp. Step change refers to a level shift characterized by an abrupt, persistent change in which the time series is either shifted up or down by a specific amount immediately after the intervention has occurred. Pulse refers to a sudden, transient change that becomes apparent for one or more-time intervals shortly after the intervention was performed and then returns to baseline level, whereas Ramp refers to a change in slope that occurs immediately after the intervention ( 19 ). In this study, we postulated that both MBS policy changes will result in step (level shift) and ramp changes in the number of unilateral and bilateral breast cancer screening scans conducted during the specified time periods, as well as in the Medicare benefits paid by Services Australia. In addition, we postulated that there will be an increase in the number of breast cancer screening scans as well as the associated benefits. Statistical models We created an ARIMA model using publicly-available datasets obtained from the MBS items reports, including unilateral and bilateral mammograms and 3DBT scans from May 2017 to April 2020 (18 months pre-post intervention I) on one hand and from November 2018 to April 2021 on the other hand. In developing ARIMA models for the study, the following steps: i) Time series graphs were plotted for pattern visualization; ii) The time series was tested for stationarity using the augmented Dickey-Fuller (ADF) test; iii) With the appropriate order of differencing (d) determined, the order of autoregression (p), and the order of moving average (q) using techniques such as autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were determined; iv) ARIMA models were then fitted; and v) Residual diagnostics were checked to ensure that the model assumptions were met. All analyses were conducted using Microsoft Excel and Python 3.10.12 ( 24 ). We also employed a counterfactual analysis to determine the impact of MBS policy changes on imaging-based breast cancer screening. The purpose of counterfactual analysis was to determine what would have occurred if the index policy changes had not been implemented. This was accomplished using ARIMA model estimation. The estimated models were then applied to the prediction of the dependent variable. The assumption for the counterfactual analysis in this study is that MBS policy change will result in increased breast cancer screening mammogram scans. Results Overall breast screening scans and benefits paid in Australia Breast cancer screening scans performed in Australia, captured by the MBS items reports from May 2017 to October 2021 are shown in Figures 1 & 2. Similarly, the Medicare benefits due to breast cancer screening scans within the same period are shown in Figure 3. Over the 54-month period, a total of about 1.61 million unilateral breast cancer screening scans (mammography and 3DBT) were conducted (about 30,000 scans per month, on average) while about 241,315 unilateral breast cancer screening scans (4,469 scans per month) were performed in the same period. The 3DBT scans constituted 87% of the total breast cancer screening imaging conducted. Furthermore, a total of about 247 million AUD (about 4.6 million AUD per month, on average) was paid as benefits due to breast cancer screening imaging in Australia in the index period and 3DBT scans represented 94% of the total benefits paid in the same period. Policy change 1: Introduction of 3D Breast Tomosynthesis in MBS items, 1 November 2018 To assess the impact of the MBS policy changes on breast cancer screening imaging in Australia (2017 to 2021) based on the study's hypotheses, we examined the level shifts, and post intervention slopes ("time after intervention") coefficients in Tables 1 and 2. The estimate or coefficient of the "time after intervention" variable represents the change in slope or trend of the mean number of breast cancer screening scans conducted post-intervention, relative to the slope or trend of these numbers pre-intervention. The "Intervention Indicator" describes the "level shift" brought about by the interventions. Following the MBS policy change on breast cancer screening by imaging (intervention 1), there was a significant decrease in the number of mammography scans (bilateral and unilateral scans) performed in Australia (Figure 1, Table 1). The final model (Table 1) estimated that the number of mammography scans decreased significantly (p=0.004) by about 23,000 after the intervention. Considering the total number of scans conducted (mammography and 3DBT), there was no statistically significant difference pre- and post-intervention. There was no statistically significant change in trend (total number of scans per month) before and after intervention when we evaluated mammography on one hand and mammography as well as 3DBT scans together on the other hand (Table 1, Figure 2). Furthermore, the MBS policy change (intervention 1) was associated with an upward level shift (immediate effect) in the benefits paid due to breast cancer screenings (mammography and 3DBT scans), increasing by about 3,250,000 AUD (95CI, 1,350,000-5,150,000) immediately post intervention (p=0.001). The counterfactual graphs provide a clear explanation of the impact of intervention on each of the variables evaluated (Figure 3). The intervention indicator coefficient estimates suggest significant changes in breast cancer screening outcomes following the introduction of 3DBT. There was a significant decrease in mammography scans and a significant increase in benefits paid immediately after the intervention. This was thought to be due to health providers’ preference for the newly-introduced 3DBT investigation. The optimal ARIMA model specifications appear to adequately capture the autocorrelation structure of the data, as indicated by the non-significant Ljung-Box statistics. The model demonstrates good accuracy in capturing the trends and effects of the introduction of 3DBT on breast cancer screening outcomes in Australia. The optimal ARIMA model specifications suggest a good fit to the data, as supported by the diagnostic tests. Policy change 2: Revision of criteria for Medicare reimbursement for breast cancer screening imaging, 1 May 2020 The second MBS policy revision (Intervention II) on breast cancer screening by imaging revealed that the number of mammograms administered in Australia following the intervention did not change significantly (Table 2, Figure 4). In addition, neither the level nor the trend of the number of scans (mammography and 3DBT) changed as a consequence of the intervention. In addition, the intervention had no statistically significant effect on the amount of benefits (AUD) paid due to mammography and 3DBT scans (Table 2, Figure 4). The counterfactual graphs associated with Intervention II provide a clear explanation of the intervention's effect on each of the evaluated variables (Figure 4). The significant coefficients for time in the number of scans conducted indicate a decreasing trend over the evaluation period. The narrow confidence intervals and low p-values suggest high precision and statistical significance of these estimates. The diagnostic tests, such as the Ljung-Box statistic, indicate that the ARIMA models adequately capture the autocorrelation structure of the residuals, suggesting good model fit. While the models demonstrate good accuracy in capturing the trends in the number of scans conducted, there may be limitations in explaining the variation in benefits paid. Discussion This study investigated the effect of two significant MBS policy changes on breast cancer screening by imaging modalities in Australia from 2017 to 2021. Introduction of 3D Breast Tomosynthesis (3DBT) to the list of MBS items eligible for Medicare benefits (Intervention I) in Australia resulted in a significant decrease in mammograms performed during the index period. This change was not associated with a significant increase or decrease in the total number of breast cancer screening scans performed. This indicates a mere replacement of mammography by 3D Breast Tomosynthesis. As expected, this policy change was also associated with significant upward and sustained shift in the amount of Medicare benefits paid due to breast cancer screening by imaging modalities. This was thought to be due to increasing proportion of breast cancer screening scans that were conducted using 3DBT as the benefits due to 3DBT screening were more than twice those of related mammography ( 22 , 25 ). The MBS policy change restricting the use of imaging for breast cancer screening to a set of defined criteria (Intervention II) was motivated by the need to assure evaluation of breast lumps while discouraging individual, group, or opportunistic screening of asymptomatic patients ( 14 ). Hence, the implementation of the intervention was expected to be associated with significant decline in the number of breast cancer screening scans performed with a corresponding reduction in the associated benefit due to the imaging procedures. However, the study revealed that there was no significant effect of the policy change on these variables. The current study is one of the few studies evaluating MBS policy changes in Australia ( 26 – 29 ). However, this study appears to be the first one evaluating MBS policy changes on breast cancer screening in Australia. A similar study conducted in the United States showed that the elimination of cost-sharing arrangements under the Affordable Care Act did not increase mammography screening rates ( 30 ). Similarly, a retrospective review of breast cancer patients treated at a hospital in the US showed that there were no significant differences in the number of breast screening mammogram scans performed, the age at diagnosis of cancer, the quantity of cancers detected by mammography, or the clinical stage at diagnosis( 30 ). In Chicago, chart-based reminders increased the adherence of physicians to mammography guidelines ( 31 ). Limitations The research has several limitations. The coverage of the publicly accessible MBS datasets used in this study is limited, as some healthcare services and providers are not adequately represented in the MBS data collection platform. In addition, we analysed data in this study considering only the national sums of data collected. This implies that we were unable to explore differences between States and Territories. Similarly, the open access MBS datasets used in this study were not disaggregated by rural and remote areas. Hence, the differences between rural and remote areas and cities could not be described. ARIMA models are limited by stationarity, non-linearity, and the presence of outliers. In this study, the data are non-stationary and has limited number of outliers. Conclusion The study evaluated the impact of MBS policy changes on breast cancer screening using imaging modalities. This study demonstrated that while the introduction of 3DBT scans for breast cancer screening were in keeping with best practices, it was associated with significant cost increases to the government. Furthermore, intervention to restrict the use of breast cancer screening by imaging modalities did not appear to reduce costs. As breast cancer screening costs continue to increase, government needs to find alternative funding sources while seeking innovative ways to reduce healthcare costs. Government should increase public awareness campaigns to educate women about the importance of regular breast cancer screening and self-examination. These campaigns can be conducted through various media channels, including television, radio, social media, and community outreach programmes. In addition, government should promote routine clinical breast examinations (CBEs) by healthcare professionals during regular check-ups and encourage women to undergo CBEs at least once every 1 to 3 years starting in their 20s and annually for women aged 40 and older. Further studies are recommended to identify disparities in access to breast cancer screening, investigating rural, remote and regional variations in the uptake of breast cancer screening services following the MBS policy changes. Furthermore, evaluation of the impact of the MBS policy changes on patient outcomes including the rates of early detection, stage at diagnosis, and the rates of survival. This will provide further understanding of how changes in screening modalities and eligibility criteria affect patient outcomes which is important for evaluating the overall effectiveness of the changes in policy. It is important to assess the impact of the MBS policy changes on health equity, particularly among underserved or marginalized populations in terms of widened or narrowed existing disparities in breast cancer screening access and outcomes. Declarations Competing interests There is no conflict of interest in the conduct and writing of this research. Ethics Declaration This study is part of a larger study which has received ethical clearance and approval (ETH2023-0357) from the UniSQ Human Research Ethics Committees (HREC), Toowoomba, Australia. Funding Declaration : University of Southern Queensland, Toowoomba, Queensland, Australia Author Contribution Abiola O. Olaleye: Conceptualization, Methodology, Investigation, Formal Analysis, Writing - Original Draft, Visualization.Enamul Kabir: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Supervision.Rasheda Khanam: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Supervision.All authors have read and agreed to the published version of the manuscript. Data Availability Data used for the preparation of the manuscript was obtained from Services Australia website: http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp Scope Guidelines Data Descriptors submitted to Scientific Data should provide detailed descriptions of valuable research datasets, including the methods used to collect the data and technical analyses supporting the quality of the measureents. Data Descriptors focus on helping others reuse data, rather than testing hypotheses, or presenting new interpretations, methods or in-depth analyses. Relevant datasets must be deposited in an appropriate public repository prior to Data Descriptor submission, and their completeness will be considered during editorial evaluation and peer review. The data must be made publicly available without restriction in the event that the Data Descriptor is accepted for publication (excepting reasonable controls related to human privacy issues or pubc safety). References Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast Off J Eur Soc Mastology. 2022 Sep 2;66:15–23. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Aust Health Rev Publ Aust Hosp Assoc. 2022 Oct;46(5):605–12. Looi JCL, Allison S, Kisely SR, Pring W, Reay RE, Bastiampillai T. Greatly increased Victorian outpatient private psychiatric care during the COVID-19 pandemic: new MBS-telehealth-item and face-to-face psychiatrist office-based services from April-September 2020. Australas Psychiatry Bull R Aust N Z Coll Psychiatr. 2021 Aug;29(4):423–9. Care AGD of H and A. Australian Government Department of Health and Aged Care. Australian Government Department of Health and Aged Care; 2020 [cited 2023 Jun 18]. COVID-19: Whole of population telehealth for patients, general practice, primary care and other medical services. Available from: https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/media/covid-19-whole-of-population-telehealth-for-patients-general-practice-primary-care-and-other-medical-services An MBS for the 21st Century Recommendations, Learnings and Ideas for the Future. Alharbi A, Khan MM, Horner R, Brandt H, Chapman C. 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Tables Table 1: Impact of Introduction of 3D Breast Tomosynthesis in MBS items report (1 November 2018) on breast cancer screening in Australia Mammography Total imaging scan (Combined Mammography and 3DBT scan) Benefits paid (AUD) Coefficient (95% CI) P value Coefficient (95% CI) P value Coefficient (95% CI) P value Intercept 36,600 (32,300 - 40,800) 0.000 34,740 (29,500 – 40,000) 0.000 258,300 (54,700- 462,000) 0.013 Time Change in number of scans conducted or amount of benefits (AUD) paid per month from the start of the evaluation period to the end. -381.11 (-721.81- 40.42) 0.028 -26.15 (-550.30 - 498.00) 0.922 412.94 (-284,000.- 284,000) 0.998 Intervention Indicator : Change in number of scans conducted or amount of benefits (AUD) paid when transiting from pre-intervention to post-intervention -22,860 (-38,300 - – 7,415.59) 0.004 1998.96 (-4129.79 - 8127.70) 0.523 3,010,000 (1,480,000 – 4,540,000 ) 0.001 Time after Intervention Difference in trend (number of scans or benefits (AUD) per month) before and after intervention 204.68 (931.06 - 1340.42) 0.724 -242.97 (-824.28- 338.34) 0.413 -158,000 (-444,000 – 128,000) 0.280 Optimal ARIMA model specification (p, d, q) 4, 0, 1 1, 0, 0 2, 0, 3 Ljung-Box (L1) (Q) 0.00 0.02 0.00 Table 2: Effects of MBS policy changes (Intervention II) on breast cancer screening in Australia (1 May 2020 – 31 October 2021) Mammography Total imaging scan (Combined Mammography and 3DBT scan) Benefits paid (AUD) Coefficient (95% CI) P value Coefficient (95% CI) P value Coefficient (95% CI) P value Intercept 5,558,000 (4,910,000 - 6,200,000) 0.000 258,300 (54,700- 462,000) 0.013 Time Change in number of scans conducted or amount of benefits (AUD) paid per month from the start of the evaluation period to the end. -341.93 (-594.476 - -89.387) 0.008 -26,920 (-94,300- 40,400) 0.434 -341.93(-284,000. - 284,000) 0.998 Intervention Indicator : Change in number of scans conducted or amount of benefits (AUD) paid when transiting from pre-intervention to post-intervention 495.60 (--1140.06 - 2131.27) 0.553 647,600 (-351,000 - 165,000) 0.204 1,477,000 (1,480,000 – 4,540,000 ) 0.434 Time after Intervention Difference in trend (number of scans or benefits (AUD) per month) before and after intervention 273.59 (-141.87- 689.06) 0.197 40,810 (-51,400- 133,000) 0.386 -158,000 (-444,000 – 128,000) 0.280 Optimal ARIMA model specification (p, d, q) 3, 1, 0 1, 0, 2 2, 1, 3 Ljung-Box (L1) (Q) 3.70 0.00 0.00 Additional Declarations No competing interests reported. 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Olaleye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACCSA2ADH4GJgPP/gAZjAwE9RSAGKwMbClGc4ACrARo+UDRAuPgTQPMVok288Ybvi4x06ejb3BwNim4nAdG3vzYQOGGptoXFqkeXKMDWc8SzZs4zmQ8DjnzGEJNp5jyQkMx9JyG3BokWPIMTPmOcCcwCaRcMA4tw2oRSLH+ABjw2HcWvjfmP/mOVCfwCb/sEHakhgt0hI5BkBbDgNtYWaQZoRqScCnRXLGswLDGQeOA/2SxmbYcyZdsg3oF4MEPH6ROJ+8weDDgWp5fvbznx/8qLDm5weGmMSHGhucWhgYOAywCCbgVA4C7A/wSo+CUTAKRsEoYAAAIfRR113sbTQAAAAASUVORK5CYII=","orcid":"","institution":"University of Southern Queensland","correspondingAuthor":true,"prefix":"","firstName":"Abiola","middleName":"O.","lastName":"Olaleye","suffix":""},{"id":375079860,"identity":"bfcdefb6-575e-485b-aa75-158d1cb3077d","order_by":1,"name":"Enamul Kabir","email":"","orcid":"","institution":"University of Southern Queensland","correspondingAuthor":false,"prefix":"","firstName":"Enamul","middleName":"","lastName":"Kabir","suffix":""},{"id":375079862,"identity":"93ff9ad8-34b3-436b-b2a6-6b90b24a10fd","order_by":2,"name":"Rasheda Khanam","email":"","orcid":"","institution":"University of Southern Queensland","correspondingAuthor":false,"prefix":"","firstName":"Rasheda","middleName":"","lastName":"Khanam","suffix":""}],"badges":[],"createdAt":"2024-10-28 09:38:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5345881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5345881/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69438916,"identity":"5a851ab4-af40-4b58-aad6-8e964bab2927","added_by":"auto","created_at":"2024-11-20 10:56:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime series graphs of breast cancer screening scans performed in Australia (1 May 2017 to 31 October 2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5345881/v1/074f4f16ef3a316a1cd31e02.png"},{"id":69438914,"identity":"d6484252-3ef9-468f-97a6-b4cabd931118","added_by":"auto","created_at":"2024-11-20 10:56:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime series graphs of Medicare benefit due to breast screening scans performed in Australia (1 May 2017 to 31 October 2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5345881/v1/ebe014357c00aab2e27deb9a.png"},{"id":69438915,"identity":"1b5ac1b7-94f4-4088-a36a-420c8d96382e","added_by":"auto","created_at":"2024-11-20 10:56:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCounterfactuals – Number of mammograms only, mammograms and 3DBT scans, and benefits due to mammograms and 3DBT scans in the index period (Intervention I)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5345881/v1/ca2b71f8d2ef44a1604e6f48.png"},{"id":69438917,"identity":"f2b8e24e-a8b6-4f38-b45e-97c0ed8ff894","added_by":"auto","created_at":"2024-11-20 10:56:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":227421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCounterfactuals associated with Intervention II – Number of mammograms only, mammograms and 3DBT scans, and benefits due to mammograms and 3DBT scans in the index period\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5345881/v1/ebba7173744781834348bb89.png"},{"id":69440562,"identity":"024c6955-a549-4ed0-87e4-dc3a8b2640c4","added_by":"auto","created_at":"2024-11-20 11:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1745149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5345881/v1/314ecbf0-aad1-46f8-bc3a-cb980c5256b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the impact of Medicare Benefits Schedule changes on breast cancer screening in Australia","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is the most prevalent cancer worldwide and continues to have a significant impact on the number of cancer-related fatalities globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Female breast cancer was the primary cause of cancer worldwide in 2020, with an estimated 2.3\u0026nbsp;million new cases. Breast cancer also accounted for more than 25 percent of all cases of cancer in women in 2020. Having surpassed lung cancer as the most frequently diagnosed cancer, breast continues to be a significant cause of morbidity and mortality in all settings. In 2020, breast cancer was estimated to be the cause of mortality for 685 000 women, representing one out of every six cancer-related deaths among women (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Furthermore, the incidence of breast cancer was nearly double in high-income countries (55.9 per 100,000) like Australia compared to middle- and low-income countries (29.7 per 100,000) in the same year (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Australia, about 13% of females will be diagnosed with breast cancer by the time they turned 85 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Breast cancer is the second most commonly diagnosed malignancy in Australia. It is the most prevalent cancer in Australian women (excluding non-melanoma skin cancer) and the second most common cause of cancer-related mortality in women, after lung cancer. In 2022, breast cancer was estimated to have been diagnosed in over 20,600 individuals in Australia, with the median age at diagnosis being 62 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the past few decades, the incidence of breast cancer has been on the rise (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) with 1,480 women aged 50 to 74 lost their lives due to breast cancer in 2019, equivalent to 43 deaths per 100,000 women (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goal of current medical efforts is to lower the death rate from breast cancer by prevention, raising awareness, early identification through screening, and treatment. Screening for cancer of the breast is an effective method for detecting early-stage disease and improving cancer patients' survival rates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In recent decades, a number of industrialized economies have implemented population-based breast cancer screening programmes, which have led to a decline in mortality and the incidence of advanced disease (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Mammography, a medical imaging technique that utilizes low-dose X-rays to create detailed images of breast tissue, is frequently used for breast cancer screening. Mammograms (images from mammography) can aid in the early detection of breast cancer using early breast signs including tumors and abnormal growths. Mammography seeks to detect breast cancer at an early, curable stage(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly and accurate diagnosis of breast cancer is essential for effective treatment and for improved prognosis. Mammography remains a popular breast imaging study. Its known sensitivity issues stem from the misinterpretation of architectural distortion and asymmetric density, as well as the fact that the cancer is covered by fibro- glandular tissue, which obscures the cancer margins. Therefore, high breast density significantly reduces the sensitivity of mammography (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Three-Dimensional Breast Tomography (3-DBT) is a novel imaging technique for breast cancer screening that improved the detection of abnormalities, particularly in dense breast, and the diagnosis of benign lesions. 3-DBT also allowed for visualization of cancers not visible by conventional mammography (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBreastScreen Australia is the national initiative for breast cancer screening. Through an organized approach to early identification of breast cancer using mammography as a screening tool to detect undiagnosed breast cancer in women, this initiative seeks to reduce breast cancer-related illness and mortality. Early detection affords the opportunity for early treatment, thereby reducing the likelihood of illness and mortality. Every two years, women aged 40 and older are eligible for mammograms(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn its bid to improve quality and coverage of health care services, optimize costs of health care services, and improve health of the population, the Government of Australia implemented a number of changes in its policy on Medicare Benefits Schedule with respect to mammography and breast screening between 2017 and 2022 (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While many of the changes were motivated by the recommendations of a high-level scientific expert group recommendations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the impacts of the changes on healthcare services have not been adequately evaluated in literature. Therefore, this study aims to evaluate the impact of changes in Medicare Benefits Schedule policy on breast cancer screening in Australia.\u003c/p\u003e \u003cp\u003eQuantitative Analysis of Policy Impact: The study provides a comprehensive quantitative analysis of the impact of two specific policy interventions related to breast cancer screening imaging on screening practices and healthcare expenditure in Australia. By examining trends in screening scans and benefits paid before and after each policy change, the study offers valuable insights into the effects of these interventions on healthcare delivery.\u003c/p\u003e \u003cp\u003eFocus on Screening Technologies: The study places particular emphasis on the role of advanced screening technologies, such as 3D Breast Tomosynthesis, in shaping breast cancer screening practices. By highlighting the predominance of 3DBT scans and their impact on screening trends, the article underscores the importance of technological advancements in improving early detection and diagnosis of breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study aims to evaluate the impact of the MBS policy changes (1 November 2018 and 1 May 2020) on breast cancer screening services including: i) Was there a significant change in the overall number of breast cancer screening conducted (mammogram and 3DBT scans) in Australia due to the policy changes? and ii) Was there a significant change in the costs of MBS reimbursements of breast screening services as a result of the policy changes? The study separately evaluated the impacts of both MBS policy changes.\u003c/p\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eThe study used an interrupted time series (ITS) analysis design. The ITS design relies on data acquired at multiple points over a period of time (often referred to as time series data) prior to and following an intervention to determine a link of causality between an intervention and an outcome of significance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). ITS design entails comparing the observed outcome post intervention with the counterfactual (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most commonly used statistical methods for modelling ITS analysis is the segmented regression model (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This model is often defined as follows (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e): The segmented regression model used in interrupted time series analysis typically involves two segments: one before the intervention (the \"pre-intervention\" period) and one after the intervention (the \"post-intervention\" period). The equation for such a model can be represented as follows:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β_0\u0026thinsp;+\u0026thinsp;β_1.t\u0026thinsp;+\u0026thinsp;β_2.intervention\u0026thinsp;+\u0026thinsp;β_3.t_2 + ℇ\u003c/p\u003e \u003cp\u003eWhere Y represents the value of the outcome variable at time t; β0 is the intercept term, representing the baseline level of the outcome variable at the beginning of the pre-intervention period; β1 represents the slope of the trend of time during the pre-intervention period; Intervention is a binary variable that indicates whether the intervention has occurred (usually coded as 0 for pre-intervention and 1 for post-intervention); β2 represents the immediate effect of the intervention on the outcome variable (the change in the intercept at the time of the intervention); β3 represents the change in the slope of the trend after the intervention; the time point at the moment in question is represented by the continuous variable time, t; t2 reflects the time after intervention has commenced, 0 before the intervention, t minus time elapsed (in measured units) since intervention will be the time after intervention commencement); ℇ signifies the error term at time t, assumed to be normally distributed with mean 0 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The use of segmented regression analysis has been criticized in literature as health data patterns may be obscure or difficult to identify, with substantial variation. Hence, outcomes of health interventions may not be linear. Furthermore, issues of autocorrelation and distributed residuals may be major limitations to the use of segmented regression in ITS analysis in the evaluation of health interventions (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eARIMA modeling is a statistical technique within ITS analysis that assesses intervention effects while considering time trends and confounding factors (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The standard notation for ARIMA models is ARIMA (p,d,q), where p represents the order of the autoregressive process, d, the level of differencing, and q, the ordering of the moving average process. These are positive integer parameters. The ARIMA model allows for autocorrelation, seasonality, as well as other structured variations in outcomes. The intervention-based analysis in the ARIMA model is not limited to modeling variations in level and slope alone; rather, it can be employed to evaluate complex trends that occur as a result of the intervention (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). ARIMA modeling consists of three stages for identifying the best model for estimation and explanation. First, we determine the differencing order, denoted by d. The second step diagnoses and determines the autoregressive and moving-average parameters (p and q terms) of the ARIMA model. Third, the residuals (noise) are diagnosed (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterventions and outcomes\u003c/p\u003e \u003cp\u003eThis study assesses the impact of the two MBS policy changes regarding breast cancer screening in Australia from 2017 to date. These include: i) Introduction of MBS items for breast tomosynthesis (1 November 20218) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e); and ii) Revision of the eligibility criteria for MBS reimbursement for breast cancer screening by radiologic imaging (1 May 2020) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBreast cancer monitoring is intended to lower breast cancer deaths. As a screening technique, mammography enables the identification of breast cancer that has not yet manifested symptoms. In the MBS, reimbursements for breast mammography are recorded using items \"59300\" and \"59303\"(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The Medicare billing codes \"59300\" (bilateral mammogram) and \"59303\" (unilateral mammogram) reflect breast cancer screening services. In November 2018, the Australian government amended its policies regarding mammography-based breast cancer surveillance. These modifications involve the addition of two new time-limited MBS items (\"59302\" and \"59305\") for 3D-breast tomography (3DBT) and the removal of two items (\"60100\" and \"60101\") for conventional plain film tomography. The 3DBT is a relatively novel digital mammography technique that generates a 3D image of the breast using multiple X-rays taken from various angles. Despite being an established practice, this technological advancement has not yet been evaluated for safety, efficacy, or cost-effectiveness. Although some patients access 3DBT services, there were no corresponding MBS items until November of 2018(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior to the MBS policy change in 2020, the criteria for claiming MBS items for mammography and 3DBT were as follows: i) a previous diagnosis of the cancer of the breast in the patient or relatives; or ii) manifestations or signs of breast malignancy discovered during the healthcare visit of the patient (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). By 1 May 2020, the requirement for claiming MBS rebates for breast cancer imaging screening services included a restriction of the past occurrence of a malignancy to the patient, such that incidence of breast malignancy in a first-degree relative became more significant than incidence of the same in the grandmother (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Hence, the interventions considered were the two MBS policy changes already described above.\u003c/p\u003e \u003cp\u003eThe primary outcomes considered in this study were the aggregate sums of Australian dollars spent on bilateral and unilateral mammograms, as well as 3DBT scans performed during the defined periods. The benefits (in Australian dollars) paid by Services Australia (the institution responsible for Medicare administration) were also considered in the index period as a primary outcome in the study.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003ePublicly available MBS items labelled \"59300\" (unilateral mammogram), \"59303\" (bilateral mammogram), \"59302\" (unilateral 3DBT), and \"59305\" (bilateral 3DBT) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) were the outcomes used in study. Both mammograms and 3DBT scans are being used for breast screening imaging since 1 November 2018 when 3DBT MBS items were introduced, aggregate sum of \"59300\"and \"59302\u0026rdquo; as well as that of \"59303\" and \"59305\" were calculated. Furthermore, the respective costs (benefits \u0026ndash; Medicare contribution to service) were also estimated for the defined period.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe objective of ITS research when used for evaluating policies is to estimate the \"effect of intervention\" or the impact of implementing the intervention on a specific outcome. While there are numerous categories of impacts that can be observed, this study will focus on the three of the most common impacts: step change, pulse, and ramp. Step change refers to a level shift characterized by an abrupt, persistent change in which the time series is either shifted up or down by a specific amount immediately after the intervention has occurred. Pulse refers to a sudden, transient change that becomes apparent for one or more-time intervals shortly after the intervention was performed and then returns to baseline level, whereas Ramp refers to a change in slope that occurs immediately after the intervention (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In this study, we postulated that both MBS policy changes will result in step (level shift) and ramp changes in the number of unilateral and bilateral breast cancer screening scans conducted during the specified time periods, as well as in the Medicare benefits paid by Services Australia. In addition, we postulated that there will be an increase in the number of breast cancer screening scans as well as the associated benefits.\u003c/p\u003e \u003cp\u003eStatistical models\u003c/p\u003e \u003cp\u003eWe created an ARIMA model using publicly-available datasets obtained from the MBS items reports, including unilateral and bilateral mammograms and 3DBT scans from May 2017 to April 2020 (18 months pre-post intervention I) on one hand and from November 2018 to April 2021 on the other hand. In developing ARIMA models for the study, the following steps: i) Time series graphs were plotted for pattern visualization; ii) The time series was tested for stationarity using the augmented Dickey-Fuller (ADF) test; iii) With the appropriate order of differencing (d) determined, the order of autoregression (p), and the order of moving average (q) using techniques such as autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were determined; iv) ARIMA models were then fitted; and v) Residual diagnostics were checked to ensure that the model assumptions were met. All analyses were conducted using Microsoft Excel and Python 3.10.12 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also employed a counterfactual analysis to determine the impact of MBS policy changes on imaging-based breast cancer screening. The purpose of counterfactual analysis was to determine what would have occurred if the index policy changes had not been implemented. This was accomplished using ARIMA model estimation. The estimated models were then applied to the prediction of the dependent variable. The assumption for the counterfactual analysis in this study is that MBS policy change will result in increased breast cancer screening mammogram scans.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOverall breast screening scans and benefits paid in Australia\u003c/p\u003e\n\u003cp\u003eBreast cancer screening scans performed in Australia, captured by the MBS items reports from May 2017 to October 2021 are shown in Figures 1 \u0026amp; 2. Similarly, the Medicare benefits due to breast cancer screening scans within the same period are shown in Figure 3. Over the 54-month period, a total of about 1.61 million unilateral breast cancer screening scans (mammography and 3DBT) were conducted (about 30,000 scans per month, on average) while about 241,315 unilateral breast cancer screening scans (4,469 scans per month) were performed in the same period. The 3DBT scans constituted 87% of the total breast cancer screening imaging conducted. Furthermore, a total of about 247 million AUD (about 4.6 million AUD per month, on average) was paid as benefits due to breast cancer screening imaging in Australia in the index period and 3DBT scans represented 94% of the total benefits paid in the same period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePolicy change 1: Introduction of 3D Breast Tomosynthesis in MBS items, 1 November 2018\u003c/p\u003e\n\u003cp\u003eTo assess the impact of the MBS policy changes on breast cancer screening imaging in Australia (2017 to 2021) based on the study\u0026apos;s hypotheses, we examined the level shifts, and post intervention slopes (\u0026quot;time after intervention\u0026quot;) coefficients in Tables 1 and 2. The estimate or coefficient of the \u0026quot;time after intervention\u0026quot; variable represents the change in slope or trend of the mean number of breast cancer screening scans conducted post-intervention, relative to the slope or trend of these numbers pre-intervention. The \u0026quot;Intervention Indicator\u0026quot; describes the \u0026quot;level shift\u0026quot; brought about by the interventions. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the MBS policy change on breast cancer screening by imaging (intervention 1), there was a significant decrease in the number of mammography scans (bilateral and unilateral scans) performed in Australia (Figure 1, Table 1). The final model (Table 1) estimated that the number of mammography scans decreased significantly (p=0.004) by about 23,000 after the intervention. Considering the total number of scans conducted (mammography and 3DBT), there was no statistically significant difference pre- and post-intervention. There was no statistically significant change in trend (total number of scans per month) before and after intervention when we evaluated mammography on one hand and mammography as well as 3DBT scans together on the other hand (Table 1, Figure 2). Furthermore, the MBS policy change (intervention 1) was associated with an upward level shift (immediate effect) in the benefits paid due to breast cancer screenings (mammography and 3DBT scans), increasing by about 3,250,000 AUD (95CI, 1,350,000-5,150,000) immediately post intervention (p=0.001). The counterfactual graphs provide a clear explanation of the impact of intervention on each of the variables evaluated (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe intervention indicator coefficient estimates suggest significant changes in breast cancer screening outcomes following the introduction of 3DBT. There was a significant decrease in mammography scans and a significant increase in benefits paid immediately after the intervention. This was thought to be due to health providers\u0026rsquo; preference for the newly-introduced 3DBT investigation. The optimal ARIMA model specifications appear to adequately capture the autocorrelation structure of the data, as indicated by the non-significant Ljung-Box statistics. \u0026nbsp;The model demonstrates good accuracy in capturing the trends and effects of the introduction of 3DBT on breast cancer screening outcomes in Australia. The optimal ARIMA model specifications suggest a good fit to the data, as supported by the diagnostic tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy change 2: Revision of criteria for Medicare reimbursement for breast cancer screening imaging, 1 May 2020\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second MBS policy revision (Intervention II) on breast cancer screening by imaging revealed that the number of mammograms administered in Australia following the intervention did not change significantly (Table 2, Figure 4). In addition, neither the level nor the trend of the number of scans (mammography and 3DBT) changed as a consequence of the intervention. In addition, the intervention had no statistically significant effect on the amount of benefits (AUD) paid due to mammography and 3DBT scans (Table 2, Figure 4). The counterfactual graphs associated with Intervention II provide a clear explanation of the intervention\u0026apos;s effect on each of the evaluated variables (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe significant coefficients for time in the number of scans conducted indicate a decreasing trend over the evaluation period. The narrow confidence intervals and low p-values suggest high precision and statistical significance of these estimates. The diagnostic tests, such as the Ljung-Box statistic, indicate that the ARIMA models adequately capture the autocorrelation structure of the residuals, suggesting good model fit. While the models demonstrate good accuracy in capturing the trends in the number of scans conducted, there may be limitations in explaining the variation in benefits paid.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effect of two significant MBS policy changes on breast cancer screening by imaging modalities in Australia from 2017 to 2021. Introduction of 3D Breast Tomosynthesis (3DBT) to the list of MBS items eligible for Medicare benefits (Intervention I) in Australia resulted in a significant decrease in mammograms performed during the index period. This change was not associated with a significant increase or decrease in the total number of breast cancer screening scans performed. This indicates a mere replacement of mammography by 3D Breast Tomosynthesis. As expected, this policy change was also associated with significant upward and sustained shift in the amount of Medicare benefits paid due to breast cancer screening by imaging modalities. This was thought to be due to increasing proportion of breast cancer screening scans that were conducted using 3DBT as the benefits due to 3DBT screening were more than twice those of related mammography (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MBS policy change restricting the use of imaging for breast cancer screening to a set of defined criteria (Intervention II) was motivated by the need to assure evaluation of breast lumps while discouraging individual, group, or opportunistic screening of asymptomatic patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Hence, the implementation of the intervention was expected to be associated with significant decline in the number of breast cancer screening scans performed with a corresponding reduction in the associated benefit due to the imaging procedures. However, the study revealed that there was no significant effect of the policy change on these variables.\u003c/p\u003e \u003cp\u003eThe current study is one of the few studies evaluating MBS policy changes in Australia (\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, this study appears to be the first one evaluating MBS policy changes on breast cancer screening in Australia. A similar study conducted in the United States showed that the elimination of cost-sharing arrangements under the Affordable Care Act did not increase mammography screening rates (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Similarly, a retrospective review of breast cancer patients treated at a hospital in the US showed that there were no significant differences in the number of breast screening mammogram scans performed, the age at diagnosis of cancer, the quantity of cancers detected by mammography, or the clinical stage at diagnosis(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In Chicago, chart-based reminders increased the adherence of physicians to mammography guidelines (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe research has several limitations. The coverage of the publicly accessible MBS datasets used in this study is limited, as some healthcare services and providers are not adequately represented in the MBS data collection platform. In addition, we analysed data in this study considering only the national sums of data collected. This implies that we were unable to explore differences between States and Territories. Similarly, the open access MBS datasets used in this study were not disaggregated by rural and remote areas. Hence, the differences between rural and remote areas and cities could not be described. ARIMA models are limited by stationarity, non-linearity, and the presence of outliers. In this study, the data are non-stationary and has limited number of outliers.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study evaluated the impact of MBS policy changes on breast cancer screening using imaging modalities. This study demonstrated that while the introduction of 3DBT scans for breast cancer screening were in keeping with best practices, it was associated with significant cost increases to the government. Furthermore, intervention to restrict the use of breast cancer screening by imaging modalities did not appear to reduce costs. As breast cancer screening costs continue to increase, government needs to find alternative funding sources while seeking innovative ways to reduce healthcare costs. Government should increase public awareness campaigns to educate women about the importance of regular breast cancer screening and self-examination. These campaigns can be conducted through various media channels, including television, radio, social media, and community outreach programmes. In addition, government should promote routine clinical breast examinations (CBEs) by healthcare professionals during regular check-ups and encourage women to undergo CBEs at least once every 1 to 3 years starting in their 20s and annually for women aged 40 and older.\u003c/p\u003e \u003cp\u003eFurther studies are recommended to identify disparities in access to breast cancer screening, investigating rural, remote and regional variations in the uptake of breast cancer screening services following the MBS policy changes. Furthermore, evaluation of the impact of the MBS policy changes on patient outcomes including the rates of early detection, stage at diagnosis, and the rates of survival. This will provide further understanding of how changes in screening modalities and eligibility criteria affect patient outcomes which is important for evaluating the overall effectiveness of the changes in policy. It is important to assess the impact of the MBS policy changes on health equity, particularly among underserved or marginalized populations in terms of widened or narrowed existing disparities in breast cancer screening access and outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThere is no conflict of interest in the conduct and writing of this research.\u003c/p\u003e\n\u003ch2\u003eEthics Declaration\u003c/h2\u003e\n\u003cp\u003eThis study is part of a larger study which has received ethical clearance and approval (ETH2023-0357) from the UniSQ Human Research Ethics Committees (HREC), Toowoomba, Australia.\u003c/p\u003e\n\u003ch2\u003eFunding\u003cstrong\u003eDeclaration\u003c/strong\u003e:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eUniversity of Southern Queensland, Toowoomba, Queensland, Australia\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAbiola O. Olaleye: Conceptualization, Methodology, Investigation, Formal Analysis, Writing - Original Draft, Visualization.Enamul Kabir: Conceptualization, Methodology, Investigation, Writing - Review \u0026amp; Editing, Supervision.Rasheda Khanam: Conceptualization, Methodology, Investigation, Writing - Review \u0026amp; Editing, Supervision.All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData used for the preparation of the manuscript was obtained from Services Australia website: http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScope Guidelines\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Descriptors\u003c/strong\u003e submitted to \u003cem\u003eScientific Data\u003c/em\u003e should provide detailed descriptions of valuable research datasets, including the methods used to collect the data and technical analyses supporting the quality of the measureents. Data Descriptors focus on helping others reuse data, rather than testing hypotheses, or presenting new interpretations, methods or in-depth analyses. Relevant datasets must be deposited in an appropriate public repository prior to Data Descriptor submission, and their completeness will be considered during editorial evaluation and peer review. The data must be made publicly available without restriction in the event that the Data Descriptor is accepted for publication (excepting reasonable controls related to human privacy issues or pubc safety).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast Off J Eur Soc Mastology. 2022 Sep 2;66:15\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eBreast Cancer Statistics | Breast Cancer Trials [Internet]. 2021 [cited 2023 Jul 31]. Available from: https://www.breastcancertrials.org.au/breast-cancer-resources/breast-cancer-statistics/\u003c/li\u003e\n\u003cli\u003eBreast cancer | Causes, Symptoms \u0026amp; Treatments [Internet]. [cited 2023 Jul 31]. Available from: https://www.cancer.org.au/cancer-information/types-of-cancer/breast-cancer\u003c/li\u003e\n\u003cli\u003eAustralian Institute of Health and Welfare [Internet]. 2021 [cited 2023 Aug 1]. BreastScreen Australia monitoring report 2021, Summary. Available from: https://www.aihw.gov.au/reports/cancer-screening/breastscreen-australia-monitoring-report-2021/summary\u003c/li\u003e\n\u003cli\u003eMoss SM, Wale C, Smith R, Evans A, Cuckle H, Duffy SW. Effect of mammographic screening from age 40 years on breast cancer mortality in the UK Age trial at 17 years\u0026rsquo; follow-up: a randomised controlled trial. Lancet Oncol. 2015 Sep;16(9):1123\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eNelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L. Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation. Ann Intern Med. 2016 Feb 16;164(4):244\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003ePlevritis SK, Munoz D, Kurian AW, Stout NK, Alagoz O, Near AM, et al. Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012. JAMA. 2018 Jan 9;319(2):154\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eL\u0026oslash;berg M, Lousdal ML, Bretthauer M, Kalager M. Benefits and harms of mammography screening. Breast Cancer Res BCR. 2015 May 1;17(1):63. \u003c/li\u003e\n\u003cli\u003eAngeles MR, Crosland P, Hensher M. Challenges for Medicare and universal health care in Australia since 2000. Med J Aust. 2023;218(7):322\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMBS Review Taskforce. An MBS for the 21st Century Recommendations, Learnings and Ideas for the Future Medicare Benefits Schedule Review Taskforce. Final Report to the Minister for Health. [Internet]. Canberra, Australia; 2020 [cited 2023 Jun 4] p. 88. Available from: https://www.health.gov.au/resources/publications/medicare-benefits-schedule-review-taskforce-final-report\u003c/li\u003e\n\u003cli\u003eHealth AGD of. Changes for diagnostic imaging services factsheet [Internet]. Australian Government Department of Health; [cited 2023 Jun 5]. Available from: http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Factsheet-DIServices\u003c/li\u003e\n\u003cli\u003eMBS Online - Summary of Changes for November 2018 [Internet]. [cited 2023 Jun 8]. Available from: http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/news-2018-11-01-latest-news-November\u003c/li\u003e\n\u003cli\u003eMBS Online - Factsheets for 2020 [Internet]. [cited 2023 Jun 8]. Available from: http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Factsheet-2020\u003c/li\u003e\n\u003cli\u003eHategeka C, Ruton H, Karamouzian M, Lynd LD, Law MR. Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review. BMJ Glob Health. 2020 Oct 14;5(10):e003567. \u003c/li\u003e\n\u003cli\u003eTurner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Med Res Methodol. 2021 Jun 26;21(1):134. \u003c/li\u003e\n\u003cli\u003eBernal JL, Soumerai S, Gasparrini A. A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol. 2018 Nov 1;103:82\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eBeard E, Marsden J, Brown J, Tombor I, Stapleton J, Michie S, et al. Understanding and using time series analyses in addiction research. Addict Abingdon Engl. 2019 Oct;114(10):1866\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eSchaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021 Mar 22;21(1):58. \u003c/li\u003e\n\u003cli\u003eWagner A, Soumerai S, MS F, Ross-Degnan D. Segmented Regression Analysis of Interrupted Time Series Studies in Medication Use Research. J Clin Pharm Ther. 2002 Aug 13;27:299\u0026ndash;309. \u003c/li\u003e\n\u003cli\u003eJarrett JE, Kyper E. ARIMA Modeling with Intervention to Forecast and Analyze Chinese Stock Prices. Int J Eng Bus Manag. 2011 Jan 1;3:17. \u003c/li\u003e\n\u003cli\u003eItem 59300 | Medicare Benefits Schedule [Internet]. [cited 2023 Aug 4]. Available from: https://www9.health.gov.au/mbs/fullDisplay.cfm?type=item\u0026amp;q=59300\u0026amp;qt=item\u003c/li\u003e\n\u003cli\u003eServices Australia - Statistics - Item Reports [Internet]. [cited 2023 Aug 8]. Available from: http://medicarestatistics.humanservices.gov.au/statistics/mbs_item.jsp\u003c/li\u003e\n\u003cli\u003ePython.org [Internet]. [cited 2023 Aug 9]. Python Release Python 3.10.12. Available from: https://www.python.org/downloads/release/python-31012/\u003c/li\u003e\n\u003cli\u003eHealth AGD of. Items for 3D breast tomosynthesis [Internet]. Australian Government Department of Health; [cited 2023 Aug 4]. Available from: http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Factsheet-3DBT\u003c/li\u003e\n\u003cli\u003eDe Guzman KR, Snoswell CL, Smith AC. The impact of telehealth policy changes on general practitioner consultation activity in Australia: a time-series analysis. Aust Health Rev Publ Aust Hosp Assoc. 2022 Oct;46(5):605\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eLooi JCL, Allison S, Kisely SR, Pring W, Reay RE, Bastiampillai T. Greatly increased Victorian outpatient private psychiatric care during the COVID-19 pandemic: new MBS-telehealth-item and face-to-face psychiatrist office-based services from April-September 2020. Australas Psychiatry Bull R Aust N Z Coll Psychiatr. 2021 Aug;29(4):423\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCare AGD of H and A. Australian Government Department of Health and Aged Care. Australian Government Department of Health and Aged Care; 2020 [cited 2023 Jun 18]. COVID-19: Whole of population telehealth for patients, general practice, primary care and other medical services. Available from: https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/media/covid-19-whole-of-population-telehealth-for-patients-general-practice-primary-care-and-other-medical-services\u003c/li\u003e\n\u003cli\u003eAn MBS for the 21st Century Recommendations, Learnings and Ideas for the Future. \u003c/li\u003e\n\u003cli\u003eAlharbi A, Khan MM, Horner R, Brandt H, Chapman C. Impact of removing cost sharing under the affordable care act (ACA) on mammography and pap test use. BMC Public Health. 2019 Apr 3;19(1):370. \u003c/li\u003e\n\u003cli\u003eMasi CM, Blackman DJ, Peek ME. Interventions to Enhance Breast Cancer Screening, Diagnosis, and Treatment among Racial and Ethnic Minority Women. Med Care Res Rev. 2007 Oct 1;64(5_suppl):195S-242S. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Impact of Introduction of 3D Breast Tomosynthesis in MBS items report (1 November 2018) on breast cancer screening in Australia\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMammography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal \u0026nbsp;imaging \u0026nbsp;scan\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Combined Mammography and 3DBT scan)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits paid (AUD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e36,600\u003c/p\u003e\n \u003cp\u003e(32,300 \u0026nbsp;- 40,800) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e34,740\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(29,500 \u0026ndash; 40,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e258,300\u003c/p\u003e\n \u003cp\u003e(54,700- 462,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChange in number of scans conducted or amount of benefits (AUD) paid per month from the start of the evaluation period to the end.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-381.11 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-721.81- 40.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028 \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-26.15\u003c/p\u003e\n \u003cp\u003e(-550.30 - 498.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e412.94\u003c/p\u003e\n \u003cp\u003e(-284,000.- 284,000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.998\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Indicator :\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChange in\u0026nbsp;number of scans conducted or amount of benefits (AUD) paid\u0026nbsp;when transiting from pre-intervention to post-intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-22,860\u003c/p\u003e\n \u003cp\u003e(-38,300 \u0026nbsp;- \u0026nbsp;\u0026ndash; 7,415.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004 \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1998.96\u003c/p\u003e\n \u003cp\u003e(-4129.79 - 8127.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.523 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e3,010,000\u003c/p\u003e\n \u003cp\u003e(1,480,000 \u0026ndash; 4,540,000 )\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime after Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDifference in trend (number of scans or benefits (AUD) per month) before and after intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e204.68 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(931.06 \u0026nbsp;- 1340.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.724 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-242.97\u003c/p\u003e\n \u003cp\u003e(-824.28- 338.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e-158,000\u003c/p\u003e\n \u003cp\u003e(-444,000 \u0026ndash; 128,000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e0.280\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal ARIMA model specification\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p, d, q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4, 0, 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1, 0, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e2, 0, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLjung-Box (L1) (Q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e0.00 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Effects of MBS policy changes (Intervention II) on breast cancer screening in Australia (1 May 2020 \u0026ndash; 31 October 2021)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMammography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal \u0026nbsp;imaging \u0026nbsp;scan\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Combined Mammography and 3DBT scan)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits paid (AUD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e5,558,000\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4,910,000 - \u0026nbsp; \u0026nbsp; 6,200,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e258,300\u003c/p\u003e\n \u003cp\u003e(54,700- 462,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChange in number of scans conducted or amount of benefits (AUD) paid per month from the start of the evaluation period to the end.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e-341.93\u003c/p\u003e\n \u003cp\u003e(-594.476 - \u0026nbsp;-89.387) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e-26,920\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-94,300- 40,400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-341.93(-284,000. - 284,000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.998\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Indicator :\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChange in\u0026nbsp;number of scans conducted or amount of benefits (AUD) paid\u0026nbsp;when transiting from pre-intervention to post-intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e495.60\u003c/p\u003e\n \u003cp\u003e(--1140.06 - 2131.27)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.553 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e647,600\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-351,000 - \u0026nbsp; \u0026nbsp;165,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1,477,000\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1,480,000 \u0026ndash; 4,540,000 )\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime after Intervention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDifference in trend (number of scans or benefits (AUD) per month) before and after intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e273.59\u003c/p\u003e\n \u003cp\u003e(-141.87- 689.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e40,810\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-51,400- \u0026nbsp; 133,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-158,000\u003c/p\u003e\n \u003cp\u003e(-444,000 \u0026ndash; 128,000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e0.280\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal ARIMA model specification\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(p, d, q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3, 1, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1, 0, 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2, 1, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLjung-Box (L1) (Q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5345881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5345881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer is a major global health concern, with substantial mortality and incidence rates. This study aims to assess the impact of these MBS policy changes on breast cancer screening services in Australia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn interrupted time series (ITS) analysis, incorporating segmented regression and Autoregressive Integrated Moving Average (ARIMA) models, was employed to evaluate the impact of MBS policy changes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe introduction of 3DBT led to a significant decrease in mammography scans but did not alter the total number of breast cancer screening scans. The second policy change in May 2020, restricting eligibility criteria, did not show a significant impact on the number of scans or associated benefits.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study suggests that the introduction of 3DBT for breast cancer screening in Australia led to a shift in imaging modalities with associated cost increases. However, the policy change aimed at restricting eligibility criteria did not result in a significant reduction in costs.\u003c/p\u003e","manuscriptTitle":"Exploring the impact of Medicare Benefits Schedule changes on breast cancer screening in Australia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:56:44","doi":"10.21203/rs.3.rs-5345881/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a88c0bb3-4308-4c4e-97f6-555be6652923","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-20T10:56:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 10:56:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5345881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5345881","identity":"rs-5345881","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}