Estimating incremental healthcare costs of coexisting type 2 Diabetes among patients with coronary heart disease in Australia: a linked data analysis

Estimating incremental healthcare costs of coexisting type 2 Diabetes among patients with coronary heart disease in Australia: a linked data analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating incremental healthcare costs of coexisting type 2 Diabetes among patients with coronary heart disease in Australia: a linked data analysis Sangita Shakya, Sean Randall, Suzanne Robinson, Crystal Man Ying Lee, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9561362/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM) impose substantial health and economic burdens, and their coexistence represents a high-risk population. This study aimed to estimate direct healthcare costs among patients with CHD and T2DM, quantify incremental costs associated with comorbid T2DM, and identify key cost drivers. Methods We used longitudinal linked administrative health data from Western Australia. A total of 998 patients with CHD and T2DM were matched 1:1 to CHD alone using propensity score matching. Healthcare utilisation costs, including hospital admissions, emergency department visits, general practice visits, medications, and pathology tests, were estimated over a three-year post-discharge period (expressed in 2024 AU $ ). Cost drivers were examined using generalised linear models with a gamma distribution and log link. Results The estimated mean three-year healthcare costs per patient were AU $ 50,841 (AU $ 16,947 annually) for CHD with T2DM, compared with AU $ 39,856 (AU $ 13,286 annually) for CHD-only. The incremental cost associated with T2DM was AU $ 10,984 (AU $ 3,661 annually), with a cost ratio of 1.28. Incremental cost varied across age, sex, remoteness, socioeconomic status, and smoking status. Higher costs were observed for hospitalisations, medications, and emergency department use. Conclusions Patients with CHD and T2DM incur substantially higher healthcare costs than those with CHD alone. These findings highlight the economic burden associated with cardiometabolic multimorbidity and support the need for targeted, integrated care strategies to improve outcomes and optimise healthcare resource use. coronary heart disease T2DM linked data incremental costs cardiometabolic multimorbidity Figures Figure 1 1. Introduction Cardiovascular disease (CVD), including coronary heart disease (CHD), and type 2 diabetes mellitus (T2DM), are major drivers of morbidity, mortality, and healthcare costs globally and in Australia. 1 – 5 In Australia, almost two-thirds of adults with T2DM also have CVD. 6 CVD is the main cause of death in this population, accounting for 65% of all CVD-related deaths among those with diabetes or pre-diabetes. 7 CHD affects approximately 600,000 Australian adults and was the leading cause of death in 2022. 8 About 1.2 million Australians were living with T2DM in 2021. 9 The coexistence of CHD and T2DM represents a common form of cardiometabolic multimorbidity, requiring complex care and increasing healthcare use. This combination is associated with poorer outcomes, including more hospital readmissions, higher mortality, and greater economic burden. 10 , 11 Together, these conditions nearly double the mortality, and their economic impact on the healthcare system is substantial. 12 In 2020–2021, healthcare expenditure attributed to CVD was estimated at AU $ 14.3 billion, including AU $ 2.5 billion for CHD, 8 and AU $ 2.3 billion was allocated for T2DM. 13 Recent modelling indicates that CVD among people with T2DM will continue to impose a substantial burden, with total costs projected to exceed AU $ 18.66 billion by 2031. 14 These findings highlight the significant strain imposed by cardiometabolic conditions on both healthcare systems and society. 14 , 15 Previous studies have evaluated healthcare costs from a diabetes-indexed perspective, focusing on the additional costs of cardiovascular complications in individuals with T2DM. 10 , 15 – 19 However, comparatively little attention has been given to the economic burden of diabetes among patients with established CHD. 18 , 20 This is important because these patients are clinically complex and use more healthcare, driven by multimorbidity and ongoing disease management needs. Studying costs from a CHD perspective provides important insights into the economic burden of cardiometabolic multimorbidity, which is increasingly relevant for healthcare systems managing patients with multiple chronic conditions. Unlike previous studies, this study quantifies the incremental healthcare costs associated with T2DM among patients with CHD, providing evidence to inform resource allocation for integrated chronic disease management. Moreover, existing cost studies are often limited to a single service type, such as hospitalisations, 18, 21 so evidence on CHD patients across healthcare settings is limited. Addressing this gap is crucial for planning targeted and integrated care. This study used large linked datasets from Western Australia to estimate direct healthcare costs for patients with CHD and T2DM, and the associated incremental costs attributable to T2DM over three years following index CHD discharge. The analysis also identified the key drivers of cost differences. Estimating the yearly and cumulative costs over the first three years post-index CHD discharge provides a comprehensive view of cost trajectories by capturing both acute and medium-term healthcare utilisation, thereby informing resource planning and budget impact assessments. 2. Methods and materials 2.1 Data sources This study used an observational longitudinal linked administrative dataset from Western Australia (WA), comprising MedicineInsight general practice (GP) records, secondary care data (emergency department (ED) and inpatient), and mortality records. The MedicineInsight dataset exhibits a strong representation of individuals from metropolitan, regional, and remote areas, representing diverse socio-economic backgrounds. 22 Linked data sources included: (i) Hospital Morbidity Data Collection (HMDC) that contains inpatient care data from public and private hospitals (Jan 2010-July 2023); (ii) Emergency Department Data Collection (EDDC) containing presentations to public EDs (Jan 2010-Oct 2023); (iii) the WA Death Register (Jan 2010-Sept 2023); and (iv) MedicineInsight – a national GP database developed by NPS MedicineWise and maintained by the Australian Commission on Safety and Quality in Health Care containing patient records (Apr 1999-Jan 2022). 23 , 24 The GP datasets included de-identified patient health records from 39 practices in WA, covering demographics, encounters, diagnoses, prescriptions, and pathology tests. The study received ethics approval from Western Australia Health Human Research Ethics Committee (HREC) (RGS0000005409), HREC (HRE2019-0619), and data access to MedicineInsight was approved under project 2020-033. Reporting of this study followed the Reporting of Studies Conducted using Observational Routinely Collected Data (RECORD) statement. 2.2 Study participants The study cohort comprised adults (≥ 18 years) discharged alive following an index CHD event identified from an ED presentation. Patients were required to survive the full 3-year follow-up period post-discharge for inclusion in the cost analysis. (Fig. 1 ) To identify incident cases, a 1-year washout period was applied prior to the index event, excluding individuals with prior CHD diagnosis or hospitalisations. CHD was identified using ICD-10-AM codes I20-I25, and T2DM, ICD-10-AM code E11 or diagnoses recorded in hospital, ED or GP data. 25 Cases were defined as individuals with CHD with comorbid T2DM, while controls included CHD patients without evidence of T2DM. 2.3 Estimating incremental healthcare costs A bottom-up microcosting approach was used from a government healthcare perspective. Direct healthcare costs included ED presentations, hospital admissions, GP visits, pathology tests, and medications. Unit costs were obtained from the Pharmaceutical Benefits Scheme (PBS), 26 the Medicare Benefits Schedule (MBS) 27 , and hospital costing data, and were expressed in 2024 Australian dollars. Where required, earlier cost estimates (e.g., 2021 ED costs) were inflated to 2024 values using the Consumer Price Index (CPI). Incremental costs were stratified by sex (male, female), age groups (18–39, 40–49, 50–59, 60–69, 60–69,70–79, 80+), remoteness (major cities, inner/outer regional areas and remote areas), socio-economic status (SES), smoking status (smoker, past-smoker, non-smoker, unknown), and comorbidities (chronic kidney disease ( (CKD), heart failure (HF), hypertension, and dyslipidaemia). Remoteness was classified according to the Australian Statistics Geography Standard (ASGS), 28 while SES was determined using the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) quintiles. 29 2.3.1 Hospital and emergency department costs Hospital separation costs were based on Australian Refined Diagnosis-Related Groups (AR-DRG) 30 codes for 2024. ED costs were based on the National Hospital Cost Data Collection, 31 with costs stratified by episode end status (admitted, not admitted, and death), and 2021 cost estimates inflated to AU $ 2024. 2.3.2 GP, Medication, and Pathology costs GP costs were estimated using MBS item numbers for 2024. Prescription costs were obtained from PBS, including dispensing, handling and PBS safety net recording fee. Government expenditure on medication depended on each patient’s concessional status and whether the safety-net threshold had been reached (a set amount spent on medications per year), after which patient medication fees are reduced/fully subsidised. To account for this, an average cost per medication was calculated using a weighted average of concessional and non-concessional prices, weighted by the frequency of prescriptions under each category in Australia. It was assumed that all prescribed medications were dispensed. Pathology costs were derived from the MBS, including collection and bulk billing fees and the three most expensive MBS items within a collection. All unit costs were standardised to 2024 values for GP, prescription and pathology services. 2.4 Statistical analysis Descriptive statistics, including frequencies and percentages, were used to present categorical variables and means and standard deviations were used for continuous variables. We used a propensity score matching (PSM) approach to estimate the incremental healthcare cost associated with T2DM among patients with CHD. The propensity score, the probability of having T2DM given baseline characteristics, was estimated using a logistic regression model, adjusting for age, sex, SES, remoteness, smoking status, and comorbidities. The PSM was used to create comparable groups; therefore, estimates apply to the matched population rather than the entire CHD cohort. Patients with CHD and T2DM (cases) were matched 1:1 to CHD-only (controls) using propensity score nearest neighbour matching without replacement. Matching was performed based on age, sex, remoteness, SES, smoking status, and comorbidities. Balance was assessed with standardised mean differences (SMD), with a threshold of < 0.1 considered acceptable. Of the initial eligible cohort (1006 CHD with T2DM and 3240 CHD-only patients), 30 patients (8 CHD with T2DM and 22 CHD-only) were excluded due to missing data on socioeconomic and remoteness variables (IRSAD and ASGS). The final analytical sample included 998 patients with CHD and T2DM (cases) and 3,218 patients with CHD-only (controls). Propensity score matching (1:1) resulted in 998 matched pairs. We then estimated the average treatment effect on the treated (ATT) as the difference in mean 3-year healthcare costs between the cases (treated) and their matched controls. This difference was defined as the cost attributable to T2DM. Statistical significance of the cost difference was tested using a Wald test (z-test), and robust standard errors were calculated. Second, to explore the independent predictors of higher healthcare costs, we fitted a generalised linear model (GLM) with a gamma distribution and log link function using the full cohort (n = 4216). The GLM enabled us to estimate the adjusted association between T2DM and healthcare costs while simultaneously examining the effects of other covariates. The GLM-based estimate of incremental cost was slightly higher than the ATT from the matching analysis, likely reflecting differences in estimation methods and the broader model-based extrapolation in GLM. Together, these approaches provided both an estimate of the incremental cost due to T2DM and insights into which patient-level factors are associated with increased healthcare expenditure. All analyses were performed using STATA 18 ( StataCorp, College Station, TX ). 3. Results 3.1 Characteristics of participants and covariate balance The baseline characteristics of unmatched cases and controls are provided in Table 1 . The mean age of the participants was 63.5 (SD = 12.4) years for cases and 63.0 (SD = 13.3) years for controls. Most of the participants in both groups were males (65.5% and 63.6%), resided in a major city (74.9% and 72.5%), smokers (15.5% and 14.7%), but the proportion of comorbidities (CKD, HF, hypertension, dyslipidaemia) was higher among cases. Before matching, there was an imbalance between groups for several variables, particularly comorbidities such as dyslipidaemia (SMD = 0.21), CKD (SMD = 0.33), HF (SMD = 0.24), and hypertension (SMD = 0.31). After PSM, covariate balance improved substantially, with most SMD reduced to < 0.1, indicating adequate balance between groups. A minor residual imbalance was observed for dyslipidaemia (SMD = -0.010), while all other covariates demonstrated good balance (Supplementary table S1 ). Table 1 Characteristics of included participants Characteristics Cases (n = 998) Controls (n = 3,218) n (%) n (%) Sex Male 654 (65.53) 2,046 (63.58) Female 344 (34.47) 1,170 (36.36) Age in years (mean ± SD) 63.43 ± 12.37 63 ± 13.3 Age groups (in years) 18–39 28 (2.81) 117 (3.64) 40–49 114(11.42) 400 (12.43) 50–59 239 (23.95) 821 (25.51) 60–69 281 (28.16) 837 (26.01) 70–79 227(22.75) 636 (19.76) 80+ 109 (10.92) 407 (12.65) Remoteness Major city of Australia 747 (74.9) 2,332 (72.5) Inner/outer regional Australia 199 (19.9) 767 (23.8) Remote/very remote Australia 52 (5.2) 119 (3.7) IRSAD Quintile Quintile 1 102 (10.2) 252 (7.8) Quintile 2 231 (23.2) 696 (21.6) Quintile 3 318 (31.9) 937 (29.1) Quintile 4 194 (19.4) 705 (21.9) Quintile 5 153 (15.3) 628 (19.5) Smoking status Smokers 154 (15.43) 474 (14.73) Past smoker 259 (25.95) 763 (23.71) Non-smokers 307 (30.76) 950 (29.52) Unknown 278 (27.86) 1031 (32.04) Comorbidities CKD 117 (11.72) 102 (3.17) Heart Failure 135 (13.53) 207 (6.43) Hypertension 598 (59.92) 1,430 (44.44) Dyslipidaemia 338 (33.87) 779 (24.21) Legend : The table shows the baseline characteristics of patients with CHD and T2DM (cases) and CHD only (controls)..The values are n (%). AU$, Australian dollar; CKD, Chronic kidney disease; IRSAD, Index of relative socio-economic advantage and disadvantage; SD, standard deviation 3.2 Incremental costs of comorbid T2DM Over a 3-year follow-up period, patients with CHD and T2DM incurred mean costs of AU $ 50,841 (AU $ 16,947 annually) per person, compared with AU $ 39,856 (AU $ 13,286 annually) among those with CHD alone. The incremental cost associated with T2DM was AU $ 10,984 (95% CI: 6,471 − 15,497; p < 0.001) over three years, or AU $ 3,661 (95% CI: 2,157-5,166) annually. Hospital admissions contributed the largest share of the incremental difference (80%; AU $ 8,756; 95% CI: 4,815 − 12,699; p < 0.001), followed by ED costs (15.2%; AU $ 1,668; 95% CI: 836-2,499; p < 0.001), while, pathology costs were slightly lower among patients with T2DM (-AU $ 33; 95% CI: -57 to -8; p = 0.008) (Table 2 ). Costs were highest in the first-year post-event and gradually decreased, whereas the incremental difference peaked in the second year (AU $ 4,199; 95% CI: 2,076 − 5,887; p < 0.0001) (Table 3 ). Table 2 Mean adjusted 3-year and annualised costs and incremental healthcare costs per patient by cost service components (n = 998, matched per group) Service type 3-year mean costs (AU $ ) Annualised mean costs (AU $ ) Significance CHD +T2DM CHD-only Incremental costs (95% CI) CHD +T2DM CHD-only Incremental costs (95% CI) p-value Emergency 7,293 5,626 1,668 (836, 2,499) 2,431 1,875 556 (279, 833) < 0.0001 Hospital (inpatient) 41,570 32,813 8,757 (4,815, 12,699) 13,857 10,938 2,919 (1,605, 4,233) < 0.0001 GP 1,016 739 277 (100, 454) 339 246 92 (33, 151) 0.002 Pathology 97 130 -33 (-57, -8) 33 43 -11 (-19, 3) 0.008 Medication 864 549 316 (180, 451) 288 183 105.17(60.15, 150.19) < 0.0001 Total 50,841 39,857 10,984 (6,471, 15,498) 16,947 13,286 3,661 (2,157, 5,166) < 0.0001 Legend The table shows the costs and incremental costs for patients with CHD and T2DM, and for those with CHD alone, over a 3-year period, by health service type. The estimates were adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). Each matched group contains n = 998 patients. AU $ , Australian dollar; CHD, Coronary heart disease; GP, general practice; T2DM, type 2 diabetes. Statistical significance is defined as p < 0.05. Table 3 Mean adjusted costs and incremental costs of 3-year post-CHD follow-up by year (AU $ ) Year 1 CHD +T2DM CHD-only Incremental costs (95% CI) p-value 25,891 23,728 2,673 (675, 4,671) 0.009 Year 2 12,307 8,897 4,199 (2,273, 6,125) < 0.0001 Year 3 12,405 9,003 3,982 (2,076, 5,887) < 0.0001 Legend : The table shows mean adjusted incremental costs for patients with CHD and T2DM, and for those with CHD alone, by year over the 3-year follow-up period. The estimates were adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). Each matched group contains n = 998 patients. AU$, Australian dollar; CHD, Coronary heart disease; CI, Confidence interval; T2DM, type 2 diabetes. Statistical significance is defined as p < 0.05 Incremental costs varied across subgroups (Table 4 ), with higher costs observed consistently among patients with T2DM. The largest differences were seen in remote areas, age groups (40–49 years), higher socio-economic groups, and smokers. Costs were also higher among females than males, and increased with remoteness, while no clear pattern was observed for socio-economic status. Among comorbidities, hypertension was associated with higher incremental costs, whereas CKD, HF, and dyslipidaemia showed no significant differences. Table 4 Mean adjusted 3-year and annualised costs and incremental costs per patient by subgroup (n = 998, matched per group) Characteristics 3-year mean costs (AU $ ) Annualised mean costs (AU $ ) Significance CHD+T2DM CHD-only Incremental costs (95% CI) CHD+T2DM CHD-only Incremental costs (95% CI) p-value Sex Male 49,382 47,493 1,889 (-5,706, 9,483) 16,461 15,831 630 (-1,902, 3,161) 0.626 Female 53,613 40,083 13,530 (5,275, 21,785) 17,872 13,362 4,510 (1,758, 7,262) 0.001 Age groups (year) 18–39 40,053 35,221 4,832 (-17,301, 26,964) 13,351 11,740 1,611 (-5,767, 8,988) 0.669 40–49 54,794 31,776 23,018 (6,437, 39,598) 18,265 10,819 7,445 (1,673, 13,217) 0.007 50–59 51,852 34,516 17,336 (6,232, 28,440) 17,284 10,371 6,913 (3,911, 9,915) 0.002 60–69 47,096 39,784 7,312 (201, 14,423) 15,699 13,289 2,410 (-134, 4,953) 0.044 70–79 49,741 46,755 2,986 (-4,750, 10,722) 16,580 14,146 2,435 (83, 4,785) 0.449 80+ 59,056 47,090 11,966 (398, 23,533) 19,685 16,528 3,157 (-202, 6,516) 0.043 Remoteness Major city of Australia 49,246 41,161 8,085 (2,827,13,343) 16,415 13,720 2,695 (942, 4,448) 0.003 Inner/outer regional Australia 52,828 45,127 7,701 (-3,385,18,787) 17,609 15,042 2,567 (-1,128, 6,262) 0.173 Remote/very remote Australia 63,555 36,193 27,362 (5,360, 49,364) 21,185 12,064 9,121 (1,787, 16,455) 0.015 IRSAD quintile Quintile 1 46,684 38,939 7,746 (-2,342, 17,833) 15,561 12,980 2,582 (-781, 5,944) 0.132 Quintile 2 55,306 40,821 14,485 (4,126, 24,844) 18,435 13,607 4,828 (1,375, 8,281) 0.006 Quintile 3 46,221 36,689 9,532 (3,111, 15,953) 15,407 12,230 3,177 (1,037, 5,318) 0.004 Quintile 4 45,184 45,294 -110 (-11,337, 11,116) 15,061 15,098 -37 (-3,779, 3,705) 0.985 Quintile 5 62,762 41,047 21,715 (7,748, 35,682) 20,921 13,682 7,238 (2,583, 11,894) 0.002 Smoking status Smoker 62,119 40,523 21,596 (8,848, 34,343) 20,707 13,508 7,199 (2,949, 11,448) 0.001 Past smokers 57,038 41,744 15,295 (6,341, 24,248) 19,013 13,915 5,098 (2,114, 8,083) 0.001 Non-smokers 41,921 38,046 3,875 (-4,827,12,578) 13,974 12,682 1,292 (-1,609, 4,193) 0.383 Comorbidities (yes) CKD 87,485 81,082 6,403 (-32,291, 45,096) 29,162 27,027 2,134(-10,765, 15,032) 0.746 Heart Failure 71,925 76,601 4,676 (-36,742, 27,391) 23,975 25,534 -1,559 (-12,247, 9,130) 0.775 Hypertension 52,468 43,355 9,112 (2,735, 15,489) 17,489 14,452 3,037 (912, 5,163) 0.005 Dyslipidaemia 47,809 40,907 6,902 (-1,808, 15,613) 15,936 13,636 2,301 (-603, 5,204) 0.12 3.3 Cost drivers of the cost difference between cohorts Table 5 presents adjusted cost ratios from the GLM analysis by service category. Patients with CHD and T2DM had higher total healthcare costs than those with CHD alone (cost ratio (CR) = 1.17, 95%CI: 1.09–1.25; p < 0.0001). This difference was primarily driven by medication (CR = 1.43, 95% CI:1.19–1.71; p < 0.0001), and ED costs (CR = 1.22, 95%CI: 1.11–1.35; p < 0.0001). Medication expenditure was mainly related to cardiovascular therapies (e.g., statins, antiplatelets, beta-blockers, and ACE/ARB inhibitors), alongside commonly used drugs, such as opioids, iron injections, and proton pump inhibitors. Diabetes-specific therapies, such as insulin, GLP-1 receptor agonists, SGLT2 inhibitors, and DPP-4 inhibitors, accounted for 10% of the total medication cost (Supplementary table S2). Table 5 Adjusted cost ratios from generalised linear models for healthcare utilisation among patients with CHD and with and without T2DM (n = 4216) Predictor Total healthcare use (Cost ratios, 95% CI) ED Visits (Cost ratios, 95% CI) Hospital (Cost ratios, 95% CI) GP Visits (Cost ratios, 95% CI) Medications (Cost ratios, 95% CI) Pathology (Cost ratios, 95% CI) CHD+T2DM vs CHD 1.17(1.09–1.25) *** 1.22 (1.11–1.35) *** 1.15(1.07–1.24) *** 1.30(0.99–1.72) 1.43(1.19–1.71) *** 0.67(0.53–0.85) ** GP visit (No visit-reference) 1–5 visits 1.01(0.91–1.09) 1.04(0.94–1.15) 0.99(0.91–1.06) - - - 6–11 visits 0.97(0.86–1.09) 0.90(0.76–1.06) 0.91(0.81–1.3) - - - 12–19 visits 0.99(0.87–1.14) 1.04(0.86–1.25) 0.89(0.77–1.02) - - - 20 + visits 1.30(1.17–1.45) * 1.20(1.04–1.42) * 1.14(1.02–1.27) * - - - Age group (18–39 year-reference) 40–49 1.06(0.88–1.27) 0.98(0.76–1.26) 1.06(0.88–1.28) 1.53(0.76–3.1) 1.25(0.79–1.96) 1.51(0.85–2.7) 50–59 0.99(0.84–1.18) 0.72 (0.56–0.91) ** 1.06(0.89–1.26) 1.79 (0.92–3.48) 1.22(0.79–1.86) 1.6(0.95–2.85) 60–69 1.11(0.94–1.32) 0.74 (0.58–0.94) * 1.20(1.01–1.43) 1.95 (1.01–3.75) * 1.25(0.82–1.92) 2.17(1.26–3.7) ** 70–79 1.23(1.03–1.5) * 0.78(0.61–0.99) * 1.35(1.13–1.62) ** 2.47 (1.26–4.83) ** 1.31(0.85–2.03) 2.98(1.70–5.23) *** 80+ 1.35(1.12–1.62) ** 1.0(0.78–1.3) 1.44(1.19–1.73) *** 4.05 (1.97–8.29) *** 1.71(1.07–2.72) * 3.46(1.90–6.27) *** Sex (Male-reference) Female 1.05(0.99–1.12) 1.22 (1.2–1.33) *** 1.02(0.96–1.09) 1.29 (1.01–1.65) * 1.12(0.69–1.31) 1.24 (1.01–1.52) * IRSAD (Quintile 1-reference) Quintile 2 1.05(0.99–1.18) 1.08(0.91–1.28) 1.05(0.92–1.19) 1.21(0.74–1.98) 0.96(0.69–1.32) 1.34(0.89–2.01) Quintile 3 0.98(0.88–1.10) 1.07(0.92–1.27) 0.97(0.86–1.08) 0.99(0.63–1.56) 0.93(0.69–1.24) 1.17(0.80–1.71) Quintile 4 1.03(0.91–1.17) 0.95(0.80–1.12) 1.04(0.92–1.2) 1.14(0.72–1.83) 0.94(0.69–1.27) 1.57(1.07–2.32) * Quintile 5 1.0(0.89–1.14) 0.92(0.77–1.09) 1.02(0.9–1.15) 0.77(0.48–1.25) 0.89(0.66–1.22) 1.42(0.95–2.11) Remoteness (cities-reference) Inner/outer regional Australia 1.04(0.96–1.12) 1.14 (1.02–1.27) * 1.02(0.94–1.10) 0.86(0.63–1.17) 0.83(0.68–1.01) 1.05(0.81–1.36) Remote Australia 1.13(0.97–1.32) 1.32 (1.07–1.64) ** 1.10(0.94–1.29) 1.03(0.56–1.89) 1.05(0.71–1.54) 0.96(0.59–1.59) Smoking status (smoker reference) Past smoker 0.87(0.79–0.97) ** 0.86(0.75–0.99) * 0.87(0.78–0.96) ** 1.26(0.86–1.85) 1.13(0.88–1.45) 1.42(1.03–1.95) * non- smoker 0.83(0.76–0.91) *** 0.82(0.71–0.93) ** 0.82(0.75–0.91) *** 0.94(0.65–1.36) 0.73(0.57–0.94) * 1.11(0.81–1.50) Comorbidities (Yes) CKD 1.66(1.44–1.90) *** 1.33(1.06–1.68) ** 1.74(1.51–2.01) *** 1.29(0.76–2.21) 1.24(0.87–1.75) 1.80(1.15–2.82) * Heart Failure 1.08(0.94–1.24) 1.28(1.09–1.5) * 1.36(1.21–1.5) *** 1.39(0.89–2.16) 1.85(1.4–2.45) *** 1.92(1.34–2.74) *** Hypertension 1.0(0.92–1.08) 1.0(0.92–1.1) 1.0(0.92–1.10) 1.52(1.19–1.93) ** 1.34(1.15–1.57) *** 1.22(0.99–1.50) Dyslipidaemia 0.93(0.86–0.99) * 0.87(0.97 − 0.96) ** 0.93(0.92-1.0) 1.89(1.45–2.47) *** 2.11(1.77–2.50) *** 1.87(1.5–2.35) *** Significance level: ***p < 0.0001, **p < 0.01, *p < 0.05; The table shows the adjusted cost ratio analysis for healthcare utilisation among patients with CHD and with and without T2DM using a generalised linear model. The model was adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). The estimate was calculated from the total cohort n = 4216. CHD, Coronary heart disease; CI, Confidence interval; CKD, Chronic kidney disease; ED, Emergency department; GP, General practice; IRSAD, Index of relative socio-economic advantage and disadvantage; T2DM, type 2 diabetes. Significant p-values are indicated with asterisks (*) In adjusted GLM analysis, higher GP visits (20 + visits) were associated with 30% higher total (CR = 1.30, 95%CI: 1.17–1.45; p < 0.05), 20% higher ED visit (CR = 1.20, 95%CI: 1.04–1.42; P < 0.05), and a 14% higher hospital (CR = 1.14, 95%CI: 1.02–1.27; p < 0.05) costs. Costs increased with age across most components, with those aged 80 + years having substantially higher GP and pathology costs compared with those aged 18–39 years. Female patients had higher ED, GP, and pathology costs than males. Higher ED costs were observed among patients in remote and regional areas, whereas higher pathology costs were observed in the more socioeconomically advantaged group. Both past smokers and non-smokers had reduced costs for all healthcare use except for pathology costs, while GP visit costs showed non-significant results. Comorbid CKD and HF were associated with higher healthcare costs across most services, while hypertension was associated with higher GP and medication costs. Those with comorbid dyslipidaemia showed a mixed pattern, with lower overall and ED costs but higher GP, medication and pathology costs. 3.4 Sensitivity analysis Sensitivity analysis of healthcare cost estimates by varying ± 10% showed that the magnitude and direction of incremental costs remained consistent across all health service components. The statistical significance of the findings was unchanged, indicating robustness of the results (Supplementary table S3) 4. Discussion To our knowledge, no published study has directly quantified the incremental healthcare cost associated with comorbid T2DM among patients with CHD. Most existing literature adopts a diabetes-centred perspective, examining cardiovascular complications among individuals with T2DM or reporting overall costs for CHD patients, without directly comparing CHD patients with and without T2DM. 16 , 17 , 32 , 33 This limits understanding of the additional economic burden associated with T2DM in patients with CHD. Our study addresses this evidence gap by estimating the incremental healthcare costs associated with comorbid T2DM among patients with, using large-scale linked administrative data from WA encompassing ED presentations, hospital admissions, GP visits, medications, and pathology tests. This perspective is particularly relevant for health system planning, as it highlights the extent of increased healthcare utilisation among patients with cardiometabolic multimorbidity. In doing so, our findings complement the existing diabetes-centred literature by providing a CHD-focused view of healthcare costs. Over 3 years, patients with CHD and T2DM incurred direct healthcare costs that were 1.28 times higher than those with CHD alone, resulting in incremental T2DM-associated costs of AU $ 10,984 (AU $ 3,661 annually) per person. These findings are consistent with previous studies in broader cardiovascular populations. 18 , 21 , 34 For example, Straka et al. reported an additional 3-year CVD-related cost of approximately US $ 10,131 among patients with T2DM compared to those without, 18 while other U.S. and European studies have similarly reported higher cardiovascular and all-cause costs among patients with diabetes. 20 , 21 , 34 Although absolute estimates vary due to differences in healthcare systems and costing approaches, the overall evidence consistently indicates a substantial economic burden associated with comorbid T2DM in patients with CVD. Our findings are broadly aligned with a recent WA-linked data study, although their annual excess cost was higher (AU $ 5,135), likely reflecting differences in the comparator group. 35 Although all healthcare service costs were higher for patients with CHD and T2DM, pathology costs were lower. The possible explanation could be that most of the pathological tests for patients with T2DM are performed during inpatient stays/ED presentations and are bundled into the hospital DRG payment, so they do not appear under MBS pathology claims. By contrast, patients with CHD-only may undergo more outpatient tests billed to MBS, inflating pathology costs. 36 The year-wise trends showed that total costs for both groups were highest in the first year following discharge, while the incremental cost differences were greater in the second and third years, consistent with international studies. 18 , 20 This pattern suggests that although overall healthcare utilisation declines after the acute phase, patients with T2DM continue to have higher ongoing healthcare needs related to secondary prevention and complication management compared with those without T2DM. Our findings suggest that in a universal healthcare context, the absolute costs diminish after the acute phase, but the relative excess cost burden of T2DM becomes more pronounced over the long term. Our subgroup analysis showed consistently higher healthcare costs among patients with CHD and T2DM across most groups, with costs increasing with age, in line with previous studies from the U.S. and Europe. 20 21 However, higher costs observed among females than males contrasts with the findings from the European study, reporting high costs among males. 21 This difference may reflect variations in healthcare-seeking behaviour, disease presentations, and service utilisation patterns between sexes, as well as differences in the underlying health system context. The higher incremental costs observed among patients with CHD and T2DM were largely driven by medication use, ED visits, and hospitalisation. Notably, patients with high GP visits (≥ 20 visits annually) had markedly higher ED, hospital and total costs, likely reflecting greater morbidity and care complexity. Older age was associated with substantially higher costs for GP services, medications, pathology and hospitalisation, consistent with prior studies demonstrating increased healthcare use with advancing age. 20 , 21 Female patients incurred higher ED, GP and pathology costs than males, possibly reflecting differences in healthcare-seeking behaviour and preventive services utilisation, contrasting with a European study that reported higher costs among men. 21 Remoteness was another important determinant, with regional and remote residents experiencing higher ED costs compared with those in a major city. This aligns with Australian Institute for Health and Welfare data showing greater acute care spending and greater service delivery costs in remote Australia. 37 Past-smokers and non-smokers had significantly reduced costs compared to smokers across all service types, highlighting the importance of public health awareness in preventing tobacco use. Multimorbidity was a strong driving factor of costs, especially CKD for ED and hospitalisation and total costs, HF for medication, pathology and hospitalisation costs, while hypertension and dyslipidaemia drove the cost for GP visits, medication and pathology tests. However, interestingly, comorbid dyslipidaemia reduced the cost for total healthcare use and ED care. This likely reflects the active management of dyslipidaemia (e.g., regular GP follow-up and statin therapy), shifting care from emergency to ambulatory settings and reducing acute events. 38 Sensitivity analysis confirmed the robustness of the findings with incremental cost estimates remaining consistent in magnitude and direction under ± 10% variation in cost inputs, with no change in statistical significance. This indicates that the observed differences in healthcare costs are stable and insensitive to underlying cost assumptions, thereby strengthening confidence in the conclusion drawn from this analysis. 4.2 Policy implications of the study findings The three-year incremental healthcare costs associated with comorbid T2DM among CHD patients are primarily driven by medications, ED visits and hospitalisations, highlighting clear targets for cost containment and efficiency improvement in secondary prevention. As hospitalisations accounted for nearly 80% of the excess cost burden, preventing avoidable readmissions through structured transitional care, improved chronic disease coordination, and better primary and secondary coordination are necessary to reduce economic burden. The proportionally higher medication and ED visit costs in the CHD with comorbid T2DM group suggest the importance of optimising prescribing practices, supporting medication adherence, and ensuring GP visits are used effectively for proactive risk management rather than reactive care. Importantly, part of this rising medication burden reflects increasing use of newer glucose-lowering therapies such as SGLT2-inhibitors (e.g. Empagliflozin, dapagliflozin) and GLP-1 receptor agonists, which provide proven cardiovascular and renal benefits but also add substantially to the treatment costs. Therefore, consideration should be given to cost-effectiveness assessments of these medicines and PBS subsidy optimisation. The observed cost disparities by age, sex, remoteness, and SES highlight the need for targeted interventions for high-cost groups. Notably, costs were higher among women with CHD and T2DM, whereas among CHD-only patients, costs were higher among men. This sex-specific pattern warrants further investigation. The substantially higher cost observed among current and past-smokers highlights the sustained public health campaigns and strict tobacco control measures. Similarly, the markedly higher incremental costs associated with comorbid CKD and HF suggest the need for multimorbidity-focused health planning and integrated care models. Policymakers and health system managers should focus on integrating these insights into funding models, care pathways, and workforce planning to reduce long-term costs and improve patient outcomes. 4.3 Strengths and limitations A major strength of this study is the use of a large population-based cohort, derived from linked administrative health data from primary and secondary care in Western Australia. Second, the inclusion of hospitalisations, ED presentations, GP services, medications, and pathology costs over a three-year follow-up provides a comprehensive assessment of direct healthcare expenditure. Additionally, the application of 1:1 nearest neighbour matching on key demographic and clinical variables minimised baseline differences between groups, and service-specific costing enabled the identification of distinct cost drivers. However, some limitations to the study should be acknowledged. First, while MedicineInsight provides broad GP coverage, not all general practices in WA were included, which suggests the possibility of underestimating GP presentation costs. Second, the analysis was restricted to direct healthcare costs, excluding indirect costs such as productivity losses and out-of-pocket expenses, and non-MBS/PBS-covered services. Third, this study restricted the cohort to individuals who survived the full three-year follow-up period to ensure completed cost capture. However, this may have introduced survivorship bias, as patients with more severe disease who died earlier were excluded, potentially leading to an underestimation of healthcare costs. Future studies could address this by incorporating methods such as inverse probability weighting or modelling costs conditionally on survival time. And although we adjusted for a range of demographic, socioeconomic, behavioural, and comorbidity factors, some important clinical variables, such as glycaemic control (e.g., HbA1c), body mass index, and blood pressure, were not included in the analysis. The HbA1c data were available for only a subset of patients, with approximately 58% missing among those with CHD and T2DM. Given the high level of missingness, inclusion of HbA1c in the analysis was not considered appropriate, as it could introduce bias. Consequently, the absence of these variables may lead to residual confounding, and the findings should be interpreted as associations rather than causal effects. The use of PSM improves internal validity by balancing observed covariates, but it may limit the generalisability, as the analysis is restricted to patients with comparable characteristics. Conclusion This study shows that patients with CHD and comorbid T2DM incur substantially greater direct healthcare costs over a three-year post-discharge period compared with those with CHD-only, with the excess burden observed for hospitalisations, medications and ED visits. Costs were consistently higher across demographic and socioeconomic subgroups, with notable variations by age, sex, and remoteness. These findings highlight the economic impact of comorbid T2DM in the secondary prevention setting. Further, it highlights opportunities to reduce healthcare costs by identifying cost-saving measures for the government, such as preventing avoidable hospital readmissions, improving medication adherence, and strengthening post-discharge primary care follow-up. Implementation of tailored interventions focused on high-cost subgroups, alongside integrated multi-morbidity management, may help reduce long-term healthcare costs while improving outcomes in this high-risk population. Declarations Ethics approval and consent to participate The study received ethics approval, with a waiver of consent granted by Western Australia Health Human Research Ethics Committee (HREC) (RGS0000005409), and Curtin University HREC (HRE2019-0619), in accordance with the Declaration of Helsinki and data access to MedicineInsight was approved under project 2020-033 Consent for publication: Not applicable Clinical trial number: Not applicable Data sharing statement The datasets used here are not publicly available due to privacy considerations. Researchers may apply to the relevant data custodians for access Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Sources of funding : Not applicable Authorship contribution statement Sangita Shakya: Writing – original draft, Project administration, Methodology, Software, Investigation, Formal analysis, Data curation, Conceptualisation, Writing-review & editing. Sean Randall: Writing – review and editing, Validation, Supervision, Methodology, Data curation, Conceptualization. Suzanne Robinson: Writing – review and editing, Validation, Supervision, Methodology, Conceptualization. Crystal M.Y. Lee, Bernard K.Y. Asiamah-Asare, James H. Boyd, Kevin E.K. Chai, and Richard Varhol: Writing – review and editing, Validation, Methodology. Lan Gao: Writing – review and editing, Validation, Supervision, Methodology, Conceptualization. Acknowledgements The authors wish to thank the staff at the Western Australian Data Linkage Services and the Hospital Morbidity and Emergency Department Data Collections, the Death Registry dataset, and staff at the Centre for Data Linkage at Curtin University. 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Diabetes Metab Syndr. 2025;19(9):103302. Department of Health and Aged Care. Medicare billing in public hositals-overview In; 2023. Australian Institute of Health and Welfare. Rural and remote health. In; 2025. Strikić D, Vujević A, Perica D, Leskovar D, Paponja K, Pećin I, et al. Importance of Dyslipidaemia Treatment in Individuals with Type 2 Diabetes Mellitus—A. Narrative Rev Diabetol. 2023;4(4):538–52. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 May, 2026 Editor invited by journal 05 May, 2026 Editor assigned by journal 05 May, 2026 Submission checks completed at journal 05 May, 2026 First submitted to journal 29 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9561362","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640006727,"identity":"f3ae5a86-0922-4a8c-9ff0-105f838d5919","order_by":0,"name":"Sangita Shakya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNklEQVRIie2QMUvDQBTHXwyky3PPEvMVTg5Ch5DPciFwXYIIXQQFA4IupVlbxO8Q8QN4cJBO2jVQB0WoDh0qhZJJvQtol6s4CuYH9/jDez/e3QG0tPxB/EwVppMLtqqhlYEDQiUr26IQ3WXsW+G/UJq6UWSjwI9K506+PtUR+JdnzxRPpl7eKWN5CKFXCHvumt4yOOBdxhIgDyVNsJzR8YALOQJOC+EEJgVEGhDGbCAuoxKdWVyIXiYRpApgVqYLrZyCP+qtJL7fx7fTF618KKWzNipVSh8Zk01Ids9FXLjqYggqCDRuIdUiAMYnSKq0v381TOiomjOJJKFjif2u6fl5St/q8HhPXezGXawjL885XeFR5A0nF9eV+Z8dvR112sHNdnVs87zuLL+SVW8damlpafnPfAIxjmzZgiQpPAAAAABJRU5ErkJggg==","orcid":"","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Sangita","middleName":"","lastName":"Shakya","suffix":""},{"id":640006728,"identity":"eec42551-19e6-422f-ad5f-22030ec5b083","order_by":1,"name":"Sean Randall","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"Randall","suffix":""},{"id":640006729,"identity":"53a8a87d-acfd-446e-97b5-0f191308d0c1","order_by":2,"name":"Suzanne Robinson","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Robinson","suffix":""},{"id":640006730,"identity":"4d494e37-a5de-499f-9895-092d33ea5624","order_by":3,"name":"Crystal Man Ying Lee","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Crystal","middleName":"Man Ying","lastName":"Lee","suffix":""},{"id":640006731,"identity":"c5cb524e-1104-4436-80c7-026d348f1b44","order_by":4,"name":"Bernard Kwadwo Yeboah Asiamah-Asare","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Bernard","middleName":"Kwadwo Yeboah","lastName":"Asiamah-Asare","suffix":""},{"id":640006732,"identity":"cf80b943-d748-41c2-b238-150c163d74bc","order_by":5,"name":"James H. Boyd","email":"","orcid":"","institution":"La Trobe University","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"H.","lastName":"Boyd","suffix":""},{"id":640006735,"identity":"6f9c1e92-aaa7-4b85-be05-b7ab1ea3a75d","order_by":6,"name":"Kevin E.K Chai","email":"","orcid":"","institution":"La Trobe University","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"E.K","lastName":"Chai","suffix":""},{"id":640006737,"identity":"70eaebc8-5b73-44d6-9ac1-e1768d2a0545","order_by":7,"name":"Richard Varhol","email":"","orcid":"","institution":"Curtin University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Varhol","suffix":""},{"id":640006741,"identity":"6c1f4751-1096-4af1-9c1c-1550baf7303b","order_by":8,"name":"Lan Gao","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2026-04-29 06:39:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9561362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9561362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109286619,"identity":"785c6a49-b2f6-41ed-b14b-caa7df313c81","added_by":"auto","created_at":"2026-05-15 02:35:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow of participants into the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e· The figure illustrates the flow of participants into the study, with the final propensity score-matched cohort comprising n=998 participants with CHD with T2DM and n=998 with CHD alone.\u003c/p\u003e\n\u003cp\u003e· Abbreviations: CHD, Coronary heart disease; GP, General practice; T2DM, Type 2 diabetes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9561362/v1/563fe75744baaf0f9c2f90ff.png"},{"id":109296764,"identity":"00d49f94-f4fa-4ee4-af75-5bf147cdc1a9","added_by":"auto","created_at":"2026-05-15 08:51:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9561362/v1/162cf55f-23c3-486a-bf5a-382fdeca0986.pdf"},{"id":109286617,"identity":"35e72d08-3c21-4f47-9229-b5c852217a70","added_by":"auto","created_at":"2026-05-15 02:35:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26898,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9561362/v1/ff2b33fec0056056416e0ffa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating incremental healthcare costs of coexisting type 2 Diabetes among patients with coronary heart disease in Australia: a linked data analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular disease (CVD), including coronary heart disease (CHD), and type 2 diabetes mellitus (T2DM), are major drivers of morbidity, mortality, and healthcare costs globally and in Australia.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In Australia, almost two-thirds of adults with T2DM also have CVD.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e CVD is the main cause of death in this population, accounting for 65% of all CVD-related deaths among those with diabetes or pre-diabetes.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e CHD affects approximately 600,000 Australian adults and was the leading cause of death in 2022.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e About 1.2\u0026nbsp;million Australians were living with T2DM in 2021.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The coexistence of CHD and T2DM represents a common form of cardiometabolic multimorbidity, requiring complex care and increasing healthcare use. This combination is associated with poorer outcomes, including more hospital readmissions, higher mortality, and greater economic burden.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Together, these conditions nearly double the mortality, and their economic impact on the healthcare system is substantial.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In 2020\u0026ndash;2021, healthcare expenditure attributed to CVD was estimated at AU\u003cspan\u003e$\u003c/span\u003e14.3\u0026nbsp;billion, including AU\u003cspan\u003e$\u003c/span\u003e2.5\u0026nbsp;billion for CHD,\u003csup\u003e8\u003c/sup\u003e and AU\u003cspan\u003e$\u003c/span\u003e2.3\u0026nbsp;billion was allocated for T2DM.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Recent modelling indicates that CVD among people with T2DM will continue to impose a substantial burden, with total costs projected to exceed AU\u003cspan\u003e$\u003c/span\u003e18.66\u0026nbsp;billion by 2031.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e These findings highlight the significant strain imposed by cardiometabolic conditions on both healthcare systems and society.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies have evaluated healthcare costs from a diabetes-indexed perspective, focusing on the additional costs of cardiovascular complications in individuals with T2DM.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, comparatively little attention has been given to the economic burden of diabetes among patients with established CHD.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e This is important because these patients are clinically complex and use more healthcare, driven by multimorbidity and ongoing disease management needs. Studying costs from a CHD perspective provides important insights into the economic burden of cardiometabolic multimorbidity, which is increasingly relevant for healthcare systems managing patients with multiple chronic conditions. Unlike previous studies, this study quantifies the incremental healthcare costs associated with T2DM among patients with CHD, providing evidence to inform resource allocation for integrated chronic disease management. Moreover, existing cost studies are often limited to a single service type, such as hospitalisations,\u003csup\u003e18, 21\u003c/sup\u003e so evidence on CHD patients across healthcare settings is limited. Addressing this gap is crucial for planning targeted and integrated care.\u003c/p\u003e \u003cp\u003eThis study used large linked datasets from Western Australia to estimate direct healthcare costs for patients with CHD and T2DM, and the associated incremental costs attributable to T2DM over three years following index CHD discharge. The analysis also identified the key drivers of cost differences. Estimating the yearly and cumulative costs over the first three years post-index CHD discharge provides a comprehensive view of cost trajectories by capturing both acute and medium-term healthcare utilisation, thereby informing resource planning and budget impact assessments.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eThis study used an observational longitudinal linked administrative dataset from Western Australia (WA), comprising MedicineInsight general practice (GP) records, secondary care data (emergency department (ED) and inpatient), and mortality records. The MedicineInsight dataset exhibits a strong representation of individuals from metropolitan, regional, and remote areas, representing diverse socio-economic backgrounds.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Linked data sources included: (i) Hospital Morbidity Data Collection (HMDC) that contains inpatient care data from public and private hospitals (Jan 2010-July 2023); (ii) Emergency Department Data Collection (EDDC) containing presentations to public EDs (Jan 2010-Oct 2023); (iii) the WA Death Register (Jan 2010-Sept 2023); and (iv) MedicineInsight \u0026ndash; a national GP database developed by NPS MedicineWise and maintained by the Australian Commission on Safety and Quality in Health Care containing patient records (Apr 1999-Jan 2022).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The GP datasets included de-identified patient health records from 39 practices in WA, covering demographics, encounters, diagnoses, prescriptions, and pathology tests. The study received ethics approval from Western Australia Health Human Research Ethics Committee (HREC) (RGS0000005409), HREC (HRE2019-0619), and data access to MedicineInsight was approved under project 2020-033. Reporting of this study followed the Reporting of Studies Conducted using Observational Routinely Collected Data (RECORD) statement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study participants\u003c/h2\u003e \u003cp\u003eThe study cohort comprised adults (\u0026ge;\u0026thinsp;18 years) discharged alive following an index CHD event identified from an ED presentation. Patients were required to survive the full 3-year follow-up period post-discharge for inclusion in the cost analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) To identify incident cases, a 1-year washout period was applied prior to the index event, excluding individuals with prior CHD diagnosis or hospitalisations. CHD was identified using ICD-10-AM codes I20-I25, and T2DM, ICD-10-AM code E11 or diagnoses recorded in hospital, ED or GP data.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Cases were defined as individuals with CHD with comorbid T2DM, while controls included CHD patients without evidence of T2DM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Estimating incremental healthcare costs\u003c/h2\u003e \u003cp\u003eA bottom-up microcosting approach was used from a government healthcare perspective. Direct healthcare costs included ED presentations, hospital admissions, GP visits, pathology tests, and medications. Unit costs were obtained from the Pharmaceutical Benefits Scheme (PBS),\u003csup\u003e26\u003c/sup\u003e the Medicare Benefits Schedule (MBS)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and hospital costing data, and were expressed in 2024 Australian dollars. Where required, earlier cost estimates (e.g., 2021 ED costs) were inflated to 2024 values using the Consumer Price Index (CPI).\u003c/p\u003e \u003cp\u003eIncremental costs were stratified by sex (male, female), age groups (18\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59, 60\u0026ndash;69, 60\u0026ndash;69,70\u0026ndash;79, 80+), remoteness (major cities, inner/outer regional areas and remote areas), socio-economic status (SES), smoking status (smoker, past-smoker, non-smoker, unknown), and comorbidities (chronic kidney disease ( (CKD), heart failure (HF), hypertension, and dyslipidaemia). Remoteness was classified according to the Australian Statistics Geography Standard (ASGS),\u003csup\u003e28\u003c/sup\u003e while SES was determined using the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) quintiles.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Hospital and emergency department costs\u003c/h2\u003e \u003cp\u003eHospital separation costs were based on Australian Refined Diagnosis-Related Groups (AR-DRG)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e codes for 2024. ED costs were based on the National Hospital Cost Data Collection,\u003csup\u003e31\u003c/sup\u003e with costs stratified by episode end status (admitted, not admitted, and death), and 2021 cost estimates inflated to AU\u003cspan\u003e$\u003c/span\u003e2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 GP, Medication, and Pathology costs\u003c/h2\u003e \u003cp\u003eGP costs were estimated using MBS item numbers for 2024. Prescription costs were obtained from PBS, including dispensing, handling and PBS safety net recording fee. Government expenditure on medication depended on each patient\u0026rsquo;s concessional status and whether the safety-net threshold had been reached (a set amount spent on medications per year), after which patient medication fees are reduced/fully subsidised. To account for this, an average cost per medication was calculated using a weighted average of concessional and non-concessional prices, weighted by the frequency of prescriptions under each category in Australia. It was assumed that all prescribed medications were dispensed. Pathology costs were derived from the MBS, including collection and bulk billing fees and the three most expensive MBS items within a collection. All unit costs were standardised to 2024 values for GP, prescription and pathology services.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including frequencies and percentages, were used to present categorical variables and means and standard deviations were used for continuous variables. We used a propensity score matching (PSM) approach to estimate the incremental healthcare cost associated with T2DM among patients with CHD. The propensity score, the probability of having T2DM given baseline characteristics, was estimated using a logistic regression model, adjusting for age, sex, SES, remoteness, smoking status, and comorbidities. The PSM was used to create comparable groups; therefore, estimates apply to the matched population rather than the entire CHD cohort.\u003c/p\u003e \u003cp\u003ePatients with CHD and T2DM (cases) were matched 1:1 to CHD-only (controls) using propensity score nearest neighbour matching without replacement. Matching was performed based on age, sex, remoteness, SES, smoking status, and comorbidities. Balance was assessed with standardised mean differences (SMD), with a threshold of \u0026lt;\u0026thinsp;0.1 considered acceptable. Of the initial eligible cohort (1006 CHD with T2DM and 3240 CHD-only patients), 30 patients (8 CHD with T2DM and 22 CHD-only) were excluded due to missing data on socioeconomic and remoteness variables (IRSAD and ASGS). The final analytical sample included 998 patients with CHD and T2DM (cases) and 3,218 patients with CHD-only (controls). Propensity score matching (1:1) resulted in 998 matched pairs. We then estimated the average treatment effect on the treated (ATT) as the difference in mean 3-year healthcare costs between the cases (treated) and their matched controls. This difference was defined as the cost attributable to T2DM. Statistical significance of the cost difference was tested using a Wald test (z-test), and robust standard errors were calculated.\u003c/p\u003e \u003cp\u003eSecond, to explore the independent predictors of higher healthcare costs, we fitted a generalised linear model (GLM) with a gamma distribution and log link function using the full cohort (n\u0026thinsp;=\u0026thinsp;4216). The GLM enabled us to estimate the adjusted association between T2DM and healthcare costs while simultaneously examining the effects of other covariates. The GLM-based estimate of incremental cost was slightly higher than the ATT from the matching analysis, likely reflecting differences in estimation methods and the broader model-based extrapolation in GLM. Together, these approaches provided both an estimate of the incremental cost due to T2DM and insights into which patient-level factors are associated with increased healthcare expenditure. All analyses were performed using STATA 18 (\u003cem\u003eStataCorp, College Station, TX\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of participants and covariate balance\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of unmatched cases and controls are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the participants was 63.5 (SD\u0026thinsp;=\u0026thinsp;12.4) years for cases and 63.0 (SD\u0026thinsp;=\u0026thinsp;13.3) years for controls. Most of the participants in both groups were males (65.5% and 63.6%), resided in a major city (74.9% and 72.5%), smokers (15.5% and 14.7%), but the proportion of comorbidities (CKD, HF, hypertension, dyslipidaemia) was higher among cases. Before matching, there was an imbalance between groups for several variables, particularly comorbidities such as dyslipidaemia (SMD\u0026thinsp;=\u0026thinsp;0.21), CKD (SMD\u0026thinsp;=\u0026thinsp;0.33), HF (SMD\u0026thinsp;=\u0026thinsp;0.24), and hypertension (SMD\u0026thinsp;=\u0026thinsp;0.31). After PSM, covariate balance improved substantially, with most SMD reduced to \u0026lt;\u0026thinsp;0.1, indicating adequate balance between groups. A minor residual imbalance was observed for dyslipidaemia (SMD = -0.010), while all other covariates demonstrated good balance (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of included participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;998)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;3,218)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e654 (65.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,046 (63.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344 (34.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,170 (36.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117 (3.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114(11.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400 (12.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239 (23.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e821 (25.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281 (28.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e837 (26.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227(22.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e636 (19.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 (10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e407 (12.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemoteness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor city of Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747 (74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,332 (72.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInner/outer regional Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e767 (23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote/very remote Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRSAD Quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e696 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e937 (29.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e705 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e628 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (15.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e474 (14.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259 (25.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e763 (23.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307 (30.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e950 (29.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278 (27.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1031 (32.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117 (11.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102 (3.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135 (13.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (6.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e598 (59.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,430 (44.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338 (33.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e779 (24.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLegend\u003c/span\u003e: The table shows the baseline characteristics of patients with CHD and T2DM (cases) and CHD only (controls)..The values are n (%). AU$, Australian dollar; CKD, Chronic kidney disease; IRSAD, Index of relative socio-economic advantage and disadvantage; SD, standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Incremental costs of comorbid T2DM\u003c/h2\u003e \u003cp\u003eOver a 3-year follow-up period, patients with CHD and T2DM incurred mean costs of AU\u003cspan\u003e$\u003c/span\u003e50,841 (AU\u003cspan\u003e$\u003c/span\u003e16,947 annually) per person, compared with AU\u003cspan\u003e$\u003c/span\u003e39,856 (AU\u003cspan\u003e$\u003c/span\u003e13,286 annually) among those with CHD alone. The incremental cost associated with T2DM was AU\u003cspan\u003e$\u003c/span\u003e10,984 (95% CI: 6,471\u0026thinsp;\u0026minus;\u0026thinsp;15,497; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) over three years, or AU\u003cspan\u003e$\u003c/span\u003e3,661 (95% CI: 2,157-5,166) annually. Hospital admissions contributed the largest share of the incremental difference (80%; AU\u003cspan\u003e$\u003c/span\u003e8,756; 95% CI: 4,815\u0026thinsp;\u0026minus;\u0026thinsp;12,699; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by ED costs (15.2%; AU\u003cspan\u003e$\u003c/span\u003e1,668; 95% CI: 836-2,499; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while, pathology costs were slightly lower among patients with T2DM (-AU\u003cspan\u003e$\u003c/span\u003e33; 95% CI: -57 to -8; p\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Costs were highest in the first-year post-event and gradually decreased, whereas the incremental difference peaked in the second year (AU\u003cspan\u003e$\u003c/span\u003e4,199; 95% CI: 2,076\u0026thinsp;\u0026minus;\u0026thinsp;5,887; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean adjusted 3-year and annualised costs and incremental healthcare costs per patient by cost service components (n\u0026thinsp;=\u0026thinsp;998, matched per group)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eService type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3-year mean costs (AU\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAnnualised mean costs (AU\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD +T2DM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHD-only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental costs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCHD +T2DM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHD-only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncremental costs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,668 (836, 2,499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e556 (279, 833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital (inpatient)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,757 (4,815, 12,699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,919 (1,605, 4,233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e277 (100, 454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92 (33, 151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-33 (-57, -8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11 (-19, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316 (180, 451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.17(60.15, 150.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,984 (6,471, 15,498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,661 (2,157, 5,166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLegend\u003c/strong\u003e \u003cp\u003eThe table shows the costs and incremental costs for patients with CHD and T2DM, and for those with CHD alone, over a 3-year period, by health service type. The estimates were adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). Each matched group contains n\u0026thinsp;=\u0026thinsp;998 patients. AU\u003cspan\u003e$\u003c/span\u003e, Australian dollar; CHD, Coronary heart disease; GP, general practice; T2DM, type 2 diabetes. Statistical significance is defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean adjusted costs and incremental costs of 3-year post-CHD follow-up by year (AU\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD +T2DM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHD-only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental costs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,891\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,728\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,673 (675, 4,671)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,199 (2,273, 6,125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,982 (2,076, 5,887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLegend\u003c/span\u003e: The table shows mean adjusted incremental costs for patients with CHD and T2DM, and for those with CHD alone, by year over the 3-year follow-up period. The estimates were adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). Each matched group contains n\u0026thinsp;=\u0026thinsp;998 patients. AU$, Australian dollar; CHD, Coronary heart disease; CI, Confidence interval; T2DM, type 2 diabetes. Statistical significance is defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIncremental costs varied across subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with higher costs observed consistently among patients with T2DM. The largest differences were seen in remote areas, age groups (40\u0026ndash;49 years), higher socio-economic groups, and smokers. Costs were also higher among females than males, and increased with remoteness, while no clear pattern was observed for socio-economic status. Among comorbidities, hypertension was associated with higher incremental costs, whereas CKD, HF, and dyslipidaemia showed no significant differences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean adjusted 3-year and annualised costs and incremental costs per patient by subgroup (n\u0026thinsp;=\u0026thinsp;998, matched per group)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3-year mean costs (AU\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAnnualised mean costs (AU\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCHD+T2DM\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCHD-only\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIncremental costs (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCHD+T2DM\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCHD-only\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eIncremental costs (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,889 (-5,706, 9,483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e630 (-1,902, 3,161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53,613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,530 (5,275, 21,785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,510 (1,758, 7,262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,832 (-17,301, 26,964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,611 (-5,767, 8,988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,018 (6,437, 39,598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,445 (1,673, 13,217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51,852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,336 (6,232, 28,440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,913 (3,911, 9,915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,312 (201, 14,423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,410 (-134, 4,953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46,755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,986 (-4,750, 10,722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,435 (83, 4,785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,966 (398, 23,533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,157 (-202, 6,516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemoteness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor city of Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49,246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,085 (2,827,13,343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,695 (942, 4,448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInner/outer regional Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,701 (-3,385,18,787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,567 (-1,128, 6,262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote/very remote Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27,362 (5,360, 49,364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,121 (1,787, 16,455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRSAD quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,746 (-2,342, 17,833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,582 (-781, 5,944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,485 (4,126, 24,844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,828 (1,375, 8,281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36,689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,532 (3,111, 15,953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,177 (1,037, 5,318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45,294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-110 (-11,337, 11,116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-37 (-3,779, 3,705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,715 (7,748, 35,682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,238 (2,583, 11,894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,596 (8,848, 34,343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,199 (2,949, 11,448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,295 (6,341, 24,248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,098 (2,114, 8,083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,875 (-4,827,12,578)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,292 (-1,609, 4,193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,403 (-32,291, 45,096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,134(-10,765, 15,032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,676 (-36,742, 27,391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25,534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1,559 (-12,247, 9,130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,112 (2,735, 15,489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,037 (912, 5,163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40,907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,902 (-1,808, 15,613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,301 (-603, 5,204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Cost drivers of the cost difference between cohorts\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents adjusted cost ratios from the GLM analysis by service category. Patients with CHD and T2DM had higher total healthcare costs than those with CHD alone (cost ratio (CR)\u0026thinsp;=\u0026thinsp;1.17, 95%CI: 1.09\u0026ndash;1.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This difference was primarily driven by medication (CR\u0026thinsp;=\u0026thinsp;1.43, 95% CI:1.19\u0026ndash;1.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and ED costs (CR\u0026thinsp;=\u0026thinsp;1.22, 95%CI: 1.11\u0026ndash;1.35; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Medication expenditure was mainly related to cardiovascular therapies (e.g., statins, antiplatelets, beta-blockers, and ACE/ARB inhibitors), alongside commonly used drugs, such as opioids, iron injections, and proton pump inhibitors. Diabetes-specific therapies, such as insulin, GLP-1 receptor agonists, SGLT2 inhibitors, and DPP-4 inhibitors, accounted for 10% of the total medication cost (Supplementary table S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted cost ratios from generalised linear models for healthcare utilisation among patients with CHD and with and without T2DM (n\u0026thinsp;=\u0026thinsp;4216)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal healthcare use (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eED Visits (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospital (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGP Visits (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedications (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePathology (Cost ratios, 95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD+T2DM vs CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17(1.09\u0026ndash;1.25) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.11\u0026ndash;1.35) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(1.07\u0026ndash;1.24) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30(0.99\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43(1.19\u0026ndash;1.71) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67(0.53\u0026ndash;0.85) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP visit (No visit-reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.91\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04(0.94\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.91\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;11 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97(0.86\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.76\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91(0.81\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;19 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99(0.87\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04(0.86\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.77\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026thinsp;+\u0026thinsp;visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.17\u0026ndash;1.45) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20(1.04\u0026ndash;1.42) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14(1.02\u0026ndash;1.27) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (18\u0026ndash;39\u0026nbsp;year-reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.88\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98(0.76\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.88\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53(0.76\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25(0.79\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.51(0.85\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99(0.84\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.56\u0026ndash;0.91) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.89\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79 (0.92\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(0.79\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6(0.95\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.94\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.58\u0026ndash;0.94) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20(1.01\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95 (1.01\u0026ndash;3.75) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25(0.82\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.17(1.26\u0026ndash;3.7) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23(1.03\u0026ndash;1.5) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78(0.61\u0026ndash;0.99) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35(1.13\u0026ndash;1.62) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47 (1.26\u0026ndash;4.83) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(0.85\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.98(1.70\u0026ndash;5.23) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35(1.12\u0026ndash;1.62) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0(0.78\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44(1.19\u0026ndash;1.73) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.05 (1.97\u0026ndash;8.29) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71(1.07\u0026ndash;2.72) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.46(1.90\u0026ndash;6.27) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male-reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.99\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.2\u0026ndash;1.33) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.96\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29 (1.01\u0026ndash;1.65) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12(0.69\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24 (1.01\u0026ndash;1.52) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRSAD (Quintile 1-reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.99\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08(0.91\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05(0.92\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21(0.74\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96(0.69\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.34(0.89\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.88\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07(0.92\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97(0.86\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.63\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93(0.69\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17(0.80\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.91\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95(0.80\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04(0.92\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.72\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94(0.69\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57(1.07\u0026ndash;2.32) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0(0.89\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92(0.77\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.9\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77(0.48\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89(0.66\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.42(0.95\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemoteness (cities-reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInner/outer regional Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.96\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.02\u0026ndash;1.27) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.94\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86(0.63\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83(0.68\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05(0.81\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemote Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.97\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.07\u0026ndash;1.64) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10(0.94\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03(0.56\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05(0.71\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96(0.59\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (smoker reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.79\u0026ndash;0.97) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.75\u0026ndash;0.99) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87(0.78\u0026ndash;0.96) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26(0.86\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13(0.88\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.42(1.03\u0026ndash;1.95) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon- smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.76\u0026ndash;0.91) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82(0.71\u0026ndash;0.93) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.75\u0026ndash;0.91) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.65\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73(0.57\u0026ndash;0.94) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11(0.81\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.66(1.44\u0026ndash;1.90) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33(1.06\u0026ndash;1.68) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74(1.51\u0026ndash;2.01) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29(0.76\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24(0.87\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80(1.15\u0026ndash;2.82) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08(0.94\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28(1.09\u0026ndash;1.5) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36(1.21\u0026ndash;1.5) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39(0.89\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85(1.4\u0026ndash;2.45) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92(1.34\u0026ndash;2.74) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0(0.92\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0(0.92\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0(0.92\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52(1.19\u0026ndash;1.93) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34(1.15\u0026ndash;1.57) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22(0.99\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.86\u0026ndash;0.99) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.96) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.92-1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89(1.45\u0026ndash;2.47) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11(1.77\u0026ndash;2.50) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.87(1.5\u0026ndash;2.35) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance level: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe table shows the adjusted cost ratio analysis for healthcare utilisation among patients with CHD and with and without T2DM using a generalised linear model. The model was adjusted for age group, sex, socioeconomic status (IRSAD quintiles), remoteness, smoking status, and comorbidities (CKD, heart failure, dyslipidaemia, and hypertension). The estimate was calculated from the total cohort n\u0026thinsp;=\u0026thinsp;4216.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCHD, Coronary heart disease; CI, Confidence interval; CKD, Chronic kidney disease; ED, Emergency department; GP, General practice; IRSAD, Index of relative socio-economic advantage and disadvantage; T2DM, type 2 diabetes. Significant p-values are indicated with asterisks (*)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn adjusted GLM analysis, higher GP visits (20\u0026thinsp;+\u0026thinsp;visits) were associated with 30% higher total (CR\u0026thinsp;=\u0026thinsp;1.30, 95%CI: 1.17\u0026ndash;1.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 20% higher ED visit (CR\u0026thinsp;=\u0026thinsp;1.20, 95%CI: 1.04\u0026ndash;1.42; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a 14% higher hospital (CR\u0026thinsp;=\u0026thinsp;1.14, 95%CI: 1.02\u0026ndash;1.27; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) costs. Costs increased with age across most components, with those aged 80\u0026thinsp;+\u0026thinsp;years having substantially higher GP and pathology costs compared with those aged 18\u0026ndash;39 years. Female patients had higher ED, GP, and pathology costs than males. Higher ED costs were observed among patients in remote and regional areas, whereas higher pathology costs were observed in the more socioeconomically advantaged group. Both past smokers and non-smokers had reduced costs for all healthcare use except for pathology costs, while GP visit costs showed non-significant results. Comorbid CKD and HF were associated with higher healthcare costs across most services, while hypertension was associated with higher GP and medication costs. Those with comorbid dyslipidaemia showed a mixed pattern, with lower overall and ED costs but higher GP, medication and pathology costs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis of healthcare cost estimates by varying\u0026thinsp;\u0026plusmn;\u0026thinsp;10% showed that the magnitude and direction of incremental costs remained consistent across all health service components. The statistical significance of the findings was unchanged, indicating robustness of the results (Supplementary table S3)\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, no published study has directly quantified the incremental healthcare cost associated with comorbid T2DM among patients with CHD. Most existing literature adopts a diabetes-centred perspective, examining cardiovascular complications among individuals with T2DM or reporting overall costs for CHD patients, without directly comparing CHD patients with and without T2DM.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e This limits understanding of the additional economic burden associated with T2DM in patients with CHD. Our study addresses this evidence gap by estimating the incremental healthcare costs associated with comorbid T2DM among patients with, using large-scale linked administrative data from WA encompassing ED presentations, hospital admissions, GP visits, medications, and pathology tests. This perspective is particularly relevant for health system planning, as it highlights the extent of increased healthcare utilisation among patients with cardiometabolic multimorbidity. In doing so, our findings complement the existing diabetes-centred literature by providing a CHD-focused view of healthcare costs.\u003c/p\u003e \u003cp\u003eOver 3 years, patients with CHD and T2DM incurred direct healthcare costs that were 1.28 times higher than those with CHD alone, resulting in incremental T2DM-associated costs of AU\u003cspan\u003e$\u003c/span\u003e10,984 (AU\u003cspan\u003e$\u003c/span\u003e3,661 annually) per person. These findings are consistent with previous studies in broader cardiovascular populations.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e For example, Straka et al. reported an additional 3-year CVD-related cost of approximately US\u003cspan\u003e$\u003c/span\u003e10,131 among patients with T2DM compared to those without,\u003csup\u003e18\u003c/sup\u003e while other U.S. and European studies have similarly reported higher cardiovascular and all-cause costs among patients with diabetes.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Although absolute estimates vary due to differences in healthcare systems and costing approaches, the overall evidence consistently indicates a substantial economic burden associated with comorbid T2DM in patients with CVD. Our findings are broadly aligned with a recent WA-linked data study, although their annual excess cost was higher (AU\u003cspan\u003e$\u003c/span\u003e5,135), likely reflecting differences in the comparator group.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough all healthcare service costs were higher for patients with CHD and T2DM, pathology costs were lower. The possible explanation could be that most of the pathological tests for patients with T2DM are performed during inpatient stays/ED presentations and are bundled into the hospital DRG payment, so they do not appear under MBS pathology claims. By contrast, patients with CHD-only may undergo more outpatient tests billed to MBS, inflating pathology costs.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe year-wise trends showed that total costs for both groups were highest in the first year following discharge, while the incremental cost differences were greater in the second and third years, consistent with international studies.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e This pattern suggests that although overall healthcare utilisation declines after the acute phase, patients with T2DM continue to have higher ongoing healthcare needs related to secondary prevention and complication management compared with those without T2DM. Our findings suggest that in a universal healthcare context, the absolute costs diminish after the acute phase, but the relative excess cost burden of T2DM becomes more pronounced over the long term.\u003c/p\u003e \u003cp\u003eOur subgroup analysis showed consistently higher healthcare costs among patients with CHD and T2DM across most groups, with costs increasing with age, in line with previous studies from the U.S. and Europe.\u003csup\u003e20 21\u003c/sup\u003e However, higher costs observed among females than males contrasts with the findings from the European study, reporting high costs among males.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This difference may reflect variations in healthcare-seeking behaviour, disease presentations, and service utilisation patterns between sexes, as well as differences in the underlying health system context.\u003c/p\u003e \u003cp\u003eThe higher incremental costs observed among patients with CHD and T2DM were largely driven by medication use, ED visits, and hospitalisation. Notably, patients with high GP visits (\u0026ge;\u0026thinsp;20 visits annually) had markedly higher ED, hospital and total costs, likely reflecting greater morbidity and care complexity. Older age was associated with substantially higher costs for GP services, medications, pathology and hospitalisation, consistent with prior studies demonstrating increased healthcare use with advancing age.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Female patients incurred higher ED, GP and pathology costs than males, possibly reflecting differences in healthcare-seeking behaviour and preventive services utilisation, contrasting with a European study that reported higher costs among men.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Remoteness was another important determinant, with regional and remote residents experiencing higher ED costs compared with those in a major city. This aligns with Australian Institute for Health and Welfare data showing greater acute care spending and greater service delivery costs in remote Australia.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Past-smokers and non-smokers had significantly reduced costs compared to smokers across all service types, highlighting the importance of public health awareness in preventing tobacco use. Multimorbidity was a strong driving factor of costs, especially CKD for ED and hospitalisation and total costs, HF for medication, pathology and hospitalisation costs, while hypertension and dyslipidaemia drove the cost for GP visits, medication and pathology tests. However, interestingly, comorbid dyslipidaemia reduced the cost for total healthcare use and ED care. This likely reflects the active management of dyslipidaemia (e.g., regular GP follow-up and statin therapy), shifting care from emergency to ambulatory settings and reducing acute events.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSensitivity analysis confirmed the robustness of the findings with incremental cost estimates remaining consistent in magnitude and direction under \u0026plusmn;\u0026thinsp;10% variation in cost inputs, with no change in statistical significance. This indicates that the observed differences in healthcare costs are stable and insensitive to underlying cost assumptions, thereby strengthening confidence in the conclusion drawn from this analysis.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Policy implications of the study findings\u003c/h2\u003e \u003cp\u003eThe three-year incremental healthcare costs associated with comorbid T2DM among CHD patients are primarily driven by medications, ED visits and hospitalisations, highlighting clear targets for cost containment and efficiency improvement in secondary prevention. As hospitalisations accounted for nearly 80% of the excess cost burden, preventing avoidable readmissions through structured transitional care, improved chronic disease coordination, and better primary and secondary coordination are necessary to reduce economic burden. The proportionally higher medication and ED visit costs in the CHD with comorbid T2DM group suggest the importance of optimising prescribing practices, supporting medication adherence, and ensuring GP visits are used effectively for proactive risk management rather than reactive care. Importantly, part of this rising medication burden reflects increasing use of newer glucose-lowering therapies such as SGLT2-inhibitors (e.g. Empagliflozin, dapagliflozin) and GLP-1 receptor agonists, which provide proven cardiovascular and renal benefits but also add substantially to the treatment costs. Therefore, consideration should be given to cost-effectiveness assessments of these medicines and PBS subsidy optimisation. The observed cost disparities by age, sex, remoteness, and SES highlight the need for targeted interventions for high-cost groups. Notably, costs were higher among women with CHD and T2DM, whereas among CHD-only patients, costs were higher among men. This sex-specific pattern warrants further investigation. The substantially higher cost observed among current and past-smokers highlights the sustained public health campaigns and strict tobacco control measures. Similarly, the markedly higher incremental costs associated with comorbid CKD and HF suggest the need for multimorbidity-focused health planning and integrated care models. Policymakers and health system managers should focus on integrating these insights into funding models, care pathways, and workforce planning to reduce long-term costs and improve patient outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Strengths and limitations\u003c/h2\u003e \u003cp\u003eA major strength of this study is the use of a large population-based cohort, derived from linked administrative health data from primary and secondary care in Western Australia. Second, the inclusion of hospitalisations, ED presentations, GP services, medications, and pathology costs over a three-year follow-up provides a comprehensive assessment of direct healthcare expenditure. Additionally, the application of 1:1 nearest neighbour matching on key demographic and clinical variables minimised baseline differences between groups, and service-specific costing enabled the identification of distinct cost drivers. However, some limitations to the study should be acknowledged. First, while MedicineInsight provides broad GP coverage, not all general practices in WA were included, which suggests the possibility of underestimating GP presentation costs. Second, the analysis was restricted to direct healthcare costs, excluding indirect costs such as productivity losses and out-of-pocket expenses, and non-MBS/PBS-covered services. Third, this study restricted the cohort to individuals who survived the full three-year follow-up period to ensure completed cost capture. However, this may have introduced survivorship bias, as patients with more severe disease who died earlier were excluded, potentially leading to an underestimation of healthcare costs. Future studies could address this by incorporating methods such as inverse probability weighting or modelling costs conditionally on survival time. And although we adjusted for a range of demographic, socioeconomic, behavioural, and comorbidity factors, some important clinical variables, such as glycaemic control (e.g., HbA1c), body mass index, and blood pressure, were not included in the analysis. The HbA1c data were available for only a subset of patients, with approximately 58% missing among those with CHD and T2DM. Given the high level of missingness, inclusion of HbA1c in the analysis was not considered appropriate, as it could introduce bias. Consequently, the absence of these variables may lead to residual confounding, and the findings should be interpreted as associations rather than causal effects. The use of PSM improves internal validity by balancing observed covariates, but it may limit the generalisability, as the analysis is restricted to patients with comparable characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that patients with CHD and comorbid T2DM incur substantially greater direct healthcare costs over a three-year post-discharge period compared with those with CHD-only, with the excess burden observed for hospitalisations, medications and ED visits. Costs were consistently higher across demographic and socioeconomic subgroups, with notable variations by age, sex, and remoteness. These findings highlight the economic impact of comorbid T2DM in the secondary prevention setting. Further, it highlights opportunities to reduce healthcare costs by identifying cost-saving measures for the government, such as preventing avoidable hospital readmissions, improving medication adherence, and strengthening post-discharge primary care follow-up. Implementation of tailored interventions focused on high-cost subgroups, alongside integrated multi-morbidity management, may help reduce long-term healthcare costs while improving outcomes in this high-risk population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethics approval, with a waiver of consent granted by Western Australia Health Human Research Ethics Committee (HREC) (RGS0000005409), and Curtin University HREC (HRE2019-0619), in accordance with the Declaration of Helsinki and data access to MedicineInsight was approved under project 2020-033\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used here are not publicly available due to privacy considerations. Researchers may apply to the relevant data custodians for access\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of funding :\u0026nbsp;\u003c/strong\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSangita Shakya:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; original draft, Project administration, Methodology, Software, Investigation, Formal analysis, Data curation, Conceptualisation, Writing-review \u0026amp; editing. \u003cstrong\u003eSean Randall:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Validation, Supervision, Methodology, Data curation, Conceptualization.\u003cstrong\u003e\u0026nbsp;Suzanne Robinson:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Validation, Supervision, Methodology, Conceptualization.\u003cstrong\u003e\u0026nbsp;Crystal M.Y. Lee, Bernard K.Y. Asiamah-Asare, James H. Boyd, Kevin E.K. Chai, and Richard Varhol:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Validation, Methodology.\u003cstrong\u003e\u0026nbsp;Lan Gao:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Validation, Supervision, Methodology, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the staff at the Western Australian Data Linkage Services and the Hospital Morbidity and Emergency Department Data Collections, the Death Registry dataset, and staff at the Centre for Data Linkage at Curtin University. We would like to thank participating general practices for contributing primary health records to MedicineInsight.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. 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The economic impact of diabetes: Assessing incremental direct costs in Australia using linked administrative data. Diabetes Metab Syndr. 2025;19(9):103302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Health and Aged Care. Medicare billing in public hositals-overview In; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare. Rural and remote health. In; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrikić D, Vujević A, Perica D, Leskovar D, Paponja K, Pećin I, et al. Importance of Dyslipidaemia Treatment in Individuals with Type 2 Diabetes Mellitus\u0026mdash;A. Narrative Rev Diabetol. 2023;4(4):538\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"coronary heart disease, T2DM, linked data, incremental costs, cardiometabolic multimorbidity","lastPublishedDoi":"10.21203/rs.3.rs-9561362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9561362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary heart disease (CHD) and type 2 diabetes mellitus (T2DM) impose substantial health and economic burdens, and their coexistence represents a high-risk population. This study aimed to estimate direct healthcare costs among patients with CHD and T2DM, quantify incremental costs associated with comorbid T2DM, and identify key cost drivers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used longitudinal linked administrative health data from Western Australia. A total of 998 patients with CHD and T2DM were matched 1:1 to CHD alone using propensity score matching. Healthcare utilisation costs, including hospital admissions, emergency department visits, general practice visits, medications, and pathology tests, were estimated over a three-year post-discharge period (expressed in 2024 AU\u003cspan\u003e$\u003c/span\u003e). Cost drivers were examined using generalised linear models with a gamma distribution and log link.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe estimated mean three-year healthcare costs per patient were AU\u003cspan\u003e$\u003c/span\u003e50,841 (AU\u003cspan\u003e$\u003c/span\u003e16,947 annually) for CHD with T2DM, compared with AU\u003cspan\u003e$\u003c/span\u003e39,856 (AU\u003cspan\u003e$\u003c/span\u003e13,286 annually) for CHD-only. The incremental cost associated with T2DM was AU\u003cspan\u003e$\u003c/span\u003e10,984 (AU\u003cspan\u003e$\u003c/span\u003e3,661 annually), with a cost ratio of 1.28. Incremental cost varied across age, sex, remoteness, socioeconomic status, and smoking status. Higher costs were observed for hospitalisations, medications, and emergency department use.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePatients with CHD and T2DM incur substantially higher healthcare costs than those with CHD alone. These findings highlight the economic burden associated with cardiometabolic multimorbidity and support the need for targeted, integrated care strategies to improve outcomes and optimise healthcare resource use.\u003c/p\u003e","manuscriptTitle":"Estimating incremental healthcare costs of coexisting type 2 Diabetes among patients with coronary heart disease in Australia: a linked data analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 02:35:48","doi":"10.21203/rs.3.rs-9561362/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T05:49:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-05T11:30:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T11:24:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-05T11:23:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-29T06:29:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99d23703-d581-439e-993e-2f3d222fb031","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"13","date":"2026-05-06T05:49:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-05T11:30:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T11:24:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-05T11:23:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-29T06:29:14+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T02:35:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 02:35:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9561362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9561362","identity":"rs-9561362","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}