Estimates of non-communicable disease expenditure by disease phase, sex, and age group for all OECD countries

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NCD health expenditure estimates are necessary to estimate future health expenditure trajectories for different prevention and treatment policies. However, no dataset of comparable estimates exists across OECD countries. This study generates disease expenditure estimates in all 38 OECD member countries in 2019, for 80 major NCDs by disease phase, sex, and age group – filling a critical information gap in global health data. Methods: Health expenditure per person with disease by sex and age group was taken from a comprehensive Australian disease expenditure study and disaggregated by disease phase (first year of diagnosis, last year of life if dying of disease, otherwise prevalent) using Global Burden of Disease data and New Zealand estimates of relative expenditure ratios by phase. These estimates were applied to case numbers in each OECD country and scaled to each country’s total health system expenditure to estimate total NCD expenditure in 2019 United States dollars by disease phase. Estimates were compared with pre-existing disease expenditure estimates for Norway, Switzerland, and the United States of America. Results: Average health expenditure on NCDs across OECD countries was US$207 million per 100,000 population. Pooled across countries, musculoskeletal disorders contributed to the highest proportion of total health expenditure (17.4%), followed by cancer and other neoplasms (9.4%), and CVD (9.1%). The highest proportion expenditure conditions for females were musculoskeletal disorders (56.1%), mental and substance use disorders (55.8%), and neurological conditions (54.8%). For males it was kidney and urinary diseases (63.8%), cancer and other neoplasms (58.3%), and cardiovascular diseases (50.7%). The first year of diagnosis represented on average 36.8% of total NCD expenditure, while last year of life expenditure attributable to disease causing death accounted for 2.6%. Similarities and differences were observed between our estimates and pre-existing country-specific estimates. Conclusions: Our estimates represent a starting point for understanding the impact of NCDs on health system expenditure. We recommend evolving our paper’s methods to include multiple country-level studies as inputs – augmented by covariates (e.g. GDP, public/private split) to better predict disease expenditure. disease expenditure health expenditure OECD countries non-communicable diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Non-communicable diseases (NCDs) are the leading cause of death and disability worldwide, responsible for nearly 75% of all yearly deaths and 80% of years lived with a disability [ 1 , 2 ]. Across the 38 Organisation for Economic Co-operation and Development (OECD) member countries (34 high-income and 4 upper-middle income economies), more than 10 million people died from NCDs in 2019 – equivalent to 89% of all deaths. Cardiovascular disease (CVD) and cancer accounted for most of this burden (32% and 28% of total deaths respectively), followed by chronic respiratory diseases (6%), Alzheimer’s and dementia (6%), and diabetes (3%) [ 2 ]. These conditions also represent a significant disability burden, contributing a total of 216 million disability-adjusted life years (DALYs) – equivalent to 54% of all DALYs across member countries [ 2 ]. There is an increasing demand for internationally comparable disease costing data to aid policy makers in understanding where to allocate health system expenditure, improving efficiency, and conducting health economic evaluations of public health policies and interventions in the face of growing NCD burden and aging populations [ 3 ]. Efforts to quantify the financial and health system impacts of NCDs are limited by data availability, comparability, and scope. Disease expenditure studies having only been undertaken comprehensively by a handful of countries, including Australia, New Zealand (NZ), Norway, Switzerland, and the United States of America (USA) [ 4 – 8 ]. The OECD generated preliminary disease expenditure estimates based on comparable health expenditure estimates using the System of Health Accounts (SHA) framework [ 9 , 10 ]. However, less than half of all OECD countries were able to provide estimates, and differences in data coverage led to large unallocated expenditure, but their analysis showed most allocated health expenditure had gone to major NCDs [ 9 ]. Women accounted for more than half of this spending due to higher expenditure on mental health and musculoskeletal conditions, while men had more hospital spending due to their increased expenditure on CVD, injuries, and mental health conditions [ 9 ]. Our study aims to estimate comparable NCD expenditure estimates by disease phase, sex and age group for all 38 OECD member countries (all ages) – filling a critical information gap in global health metrics. It is a first attempt at generating a single dataset of comparable disease expenditure estimates across multiple countries, and one that we hope encourages others to improve on. Our methodology uses comprehensive Australasian health expenditure estimates as the starting point: Australian disease expenditure estimates and NZ relative expenditure ratios for NCDs in the first year of diagnosis, last year of life if dying of that disease, and otherwise prevalent. This data was combined with epidemiological case numbers for each OECD country to approximate aggregate expenditure with an Australian cost base. It was then scaled to each country’s total health system expenditure to estimate total NCD expenditure by disease phase, sex, and age group. Methods The study’s data inputs included population estimates, and disease incidence, prevalence and mortality rates from the GBD 2019 for each OECD country [ 2 ]; total health expenditure for each disease by sex and age group from the Australian Institute of Health and Welfare (AIHW) [ 4 ]; relative NCD expenditure ratios for each disease phase from NZ [ 5 ]; and total health expenditure for each member country from the OECD [ 11 ]. All statistical analyses were performed using R (version 2022.12.0 + 353) and expenditure estimates are reported as 2019 purchasing power parity (PPP)-adjusted United States dollars (US $ ) using OECD PPPs. Study population The female and male populations of each OECD country were categorised into 19 age groups (< 1 year, 1–4 years, 5–9 years, …, 85 + years) using the GBD 2019 population estimates. Disease groupings for OECD member countries There were 80 Level 3 NCD causes from the GBD 2019 used in our study (see Table S2). Concordance of Australian conditions to GBD causes There are 91 AIHW conditions that correspond to the 80 level 3 NCD causes from the GBD 2019 across 10 disease groups (see Table S2). Inflammatory heart disease (one of the AIHW conditions) corresponds with two GBD causes: cardiomyopathy and myocarditis, and endocarditis. Therefore, inflammatory heart disease’s expenditure was disaggregated across the two GBD causes based on the number of prevalent cases for each cause. In each disease group, there were AIHW conditions that did not have a corresponding level 3 NCD cause from the GBD 2019. Therefore, an additional NCD cause was created for each disease group (i.e. 10 additional NCD causes in total) that corresponded to these remaining AIHW conditions, such as ‘all other cancer conditions’ and ‘all other CVD conditions’. An ‘all other’ cause was also created to allocate remaining health expenditure from conditions not included in either the 80 level 3 NCD causes or the additional 10 NCD causes. These include injuries; communicable, maternal, neonatal and nutritional diseases; and minor NCDs (e.g. congenital birth defects, oral disorders). Disease phase The 80 level 3 NCD causes were assigned relative disease phase costing ratios from Blakely et al (2019) for first year of diagnosis, last year of diagnosis if dying of that disease, and otherwise prevalent [ 5 ]. Where there was not a matching phase ratio from Blakely et al (2019), NCD causes were assigned either a ‘flat’ ratio (i.e. a 1 : 1 : 1 ratio; for diseases whose treatment is expected to be similar across phase) or an ‘average’ ratio (i.e. a 2.40 : 1: 2.72 ratio; remaining diseases) which is the average of all NCD costing ratios from the Blakely et al analysis)[ 5 ]. OECD health system expenditure For the OECD dataset containing health system expenditure by health care functions, the governance and health system and financing administration, long-term care, and preventative care function expenditure estimates were subtracted from the total health system expenditure estimate for each member country as the AIHW’s disease expenditure 2018-19 study did not include these functions in their estimates [ 12 ]. We conceptualised the OECD estimated percentage of each country’s national health accounts due to ‘health care functions’ as equivalent to expenditure that can be disaggregated by disease, or put another way that expenditure that is marginal given the number of people with disease. However, Colombia, Israel, New Zealand, and Turkey did not have total health system expenditure disaggregated by health care function, therefore the average proportion of remaining health expenditure from total health system expenditure (i.e., on average, 81% of health system expenditure was remaining once expenditure estimates from the three health care functions were subtracted from the total health system expenditure estimate) was applied to these countries (see Figure S1 ). Generating epidemiological case numbers for each disease phase by sex and age group for each OECD member country For each OECD country, GBD incidence, prevalence, and mortality rates for each of the 80 NCDs were converted to per person values. Case fatality rates were then calculated for each disease by dividing their mortality rate by their prevalence. Aortic aneurism only had mortality data, so a mortality risk was calculated instead (i.e., 1-exponent (-[mortality rate]). For each disease, sex, and age group combination, incidence and prevalence rates, and mortality risk were multiplied by population estimates to generate the number of cases for each OECD country. Case fatality rates were multiplied by the number of prevalent cases for that disease to get the number of cases in their last year of life with the disease. As the GBD uses point prevalence, the finalised number of period (annual) prevalent cases was calculated by subtracting half the number of incidence cases and half the number of cases in their last year of life with the disease. Some diseases did not have corresponding GBD data. For each of the 10 additional NCDs created for AIHW conditions without a corresponding level 3 NCD cause from the GBD 2019, a prevalent case number was calculated for each for each OECD country, sex and age group combination based on the prevalence of specified conditions within that group, assuming the ratio of the ‘other’ groups to named NCD categories in Australia applied to other OECD countries. For the ‘all other’ disease created for conditions not included in either the 80 level 3 NCD causes or the additional 10 NCD causes, the prevalent case number for each sex and age group combination was the GBD population estimate for that country’s sex and age group combination. Generating health expenditure estimates per person by disease, disease phase, sex, and age group for Australia For each disease, sex and age group combination, a weighted expenditure value was calculated. The expenditure (in USD) per prevalent case in Australia of disease (d), within each sex (s) and age group (a) was: $$\:{Exp\:prev\:cases}_{Aus,\:dsa}=\:\:\frac{{Exp\:all\:cases}_{Aus,\:dsa}}{\sum\:_{phase}\left({ratio}_{phase,\:Aus,\:dsa}\times\:{N}_{phase,Aus,\:dsa}\right)}$$ where ratio is the relative ratio of expenditure for incident or last year of life cases compared to prevalent cases from NZ (where the ratio for prevalent cases is 1), and N is the number of disease cases in Australia in 2019 by phase estimated from GBD data (above). The expenditure per incident and last year of life (if dying of that disease) was the above prevalent estimate multiplied by its phase ratio. For the ‘all other’ disease, its scaled expenditure value was divided by population estimate. Generating health expenditure estimates per person by disease, disease phase, sex, and age group for each OECD member country A country-specific scalar (c) was calculated, which adjusted for all country-level total health expenditure, demography and disease epidemiology: $$\:{Scalar}_{c}=\:\:\frac{{Exp\:all\:diseases\:from\:national\:health\:accounts}_{c}}{\sum\:_{phase,dsa}\left({N}_{phase,c,\:dsa}\times\:\:{Exp}_{phase,Aus,dsa}\right)}$$ This country specific scalar was multiplied by disease expenditure per person estimates (calculated in the previous section) to generate expenditure (in USD) by disease phase, sex and age group for each OECD member country. The sum of these estimates in each country will not be the same as their total health expenditure estimates from the OECD [ 11 ]. Therefore, our study’s country estimates were scaled to ensure they equalled the relevant ‘health care functions’ OECD expenditure estimates for each country. Comparisons with pre-existing health expenditure estimates We compared our country-level estimates with three separate country studies that used a bottom up costing of services by diagnosis using similar methods to the Australian disease expenditure study: Norway [ 6 ], Switzerland [ 7 ] and the USA [ 8 ]. Results Health spending on NCDs across OECD member countries In 2019, the average health expenditure on NCDs across OECD countries was US $ 207 million per 100,000 population (see Fig. 1). The United States of America (USA) clearly had the highest relative spending at US $ 599 million per 100,000 while Turkey had the lowest expenditure at US $ 56 million. Figure 1. Total disease expenditure per 100,000 population for non-communicable diseases across OECD member countries in 2019 (OECD average = US $ 207 million per 100,000 population). For all OECD countries pooled, musculoskeletal disorders contributed to the highest proportion of total health expenditure (17.4%), followed by cancer and other neoplasms (9.4%), CVD (9.1%), mental and substance use disorders (6.1%), gastrointestinal disorders (5.0%), skin disorders (4.7%), endocrine disorders (3.9%), kidney and urinary diseases (3.5%), neurological conditions (3.2%), and respiratory diseases (2.5%). The remaining 35.3% of total health expenditure was attributable to other diseases (see Fig. 2 and Supplementary Table 3). For the top three expenditure conditions, the following percentage expenditure variations were observed between countries: 18.6% in the United Kingdom to 12.3% in Costa Rica for musculoskeletal disorders; 15.5% in Canada to 3.0% in Mexico for cancer and other neoplasms; and 14.1% in Estonia to 5.4% in Colombia for CVD. Figure 2. Proportion of total health expenditure by disease groups across OECD member countries in 2019. Figure 3 shows the proportion of health expenditure on NCDs by sex: females had relatively higher expenditure than males for musculoskeletal disorders (1.28 fold greater or 12.2 percentage points greater), mental and substance use disorders (1.26 fold greater or 11.6 percentage points greater), and neurological conditions (1.21 fold greater or 9.7 percentage points greater) (see Fig. 3). Males had significantly higher expenditure on kidney and urinary diseases (1.76 fold greater or 27.6 percentage points greater), and cancer and other neoplasms (1.40 fold greater or 16.6 percentage points greater). Figure 3. Health expenditure on non-communicable diseases by disease groups and sex across OECD member countries in 2019; all countries pooled, crude analyses (i.e. no age-standardisation). Across the three disease phases, the majority of total NCD expenditure went to the prevalent phase (at an average across NCDs of 60.7%), followed by the first year of diagnosis (at an average 36.8%), and the last year of life (at 2.6%; noting that this is expenditure attributed to the cause of death, not all expenditure by the health system in the last year of life). Comparisons with pre-existing health expenditure estimates Figure 4 shows the percentage of Norway’s total health expenditure in 2019 across 7 aggregated disease groups that were similar between our study and the most recent Norwegian-specific study by Kinge et al (2023) [ 6 ]. There was poor agreement for neurological (3.9% of all health expenditure using our method, compared to 15.4% in the Kinge et al study), mental and substance use (20.7% and 6.3%) and musculoskeletal (6.7% and 16.0%), but reasonably good agreement for the four other diseases shown. Figure 4. Scatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Norway, Kinge et al (2023) [ 6 ]. Figure 5 shows a similar comparison for Switzerland, for eight comparable disease groupings with Weiser et al (2018) [ 7 ]. There was poor agreement for CVD (7.1% our method, 15.6% Weiser et al). There was moderate agreement for mental and substance use (7.3% and 10.6%), cancer and other neoplasms (8.3% and 6.0%) and musculoskeletal (19.6% and 13.4%). The remaining four conditions had good agreement. Figure 5. Scatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Switzerland, Wieser et al (2018) [ 7 ]. Figure 6 shows a comparison for the USA, for seven comparable groupings with Dieleman et al (2016)[ 8 ]. There was poor agreement for musculoskeletal (17.3% our method, 8.7% Dieleman et al (2016)), cancer and other neoplasms (10.6% and 5.5%) and respiratory (3.0% and 6.3%). The other four conditions had moderate to good (gastrointestinal) agreement. Figure 6. Scatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with USA, Dieleman et al (2016) [ 8 ]. Looking across these three country comparisons, our method consistently estimates greater percentage musculoskeletal disease expenditure than the country-specific studies. Less notable general differences include our method estimating greater cancer and other neoplasm expenditure, and estimating less mental and substance use expenditure. The Dieleman et al (2016) study also permitted a comparison of 20 more disaggregated conditions (Fig. 7, log scale both axes). For nine of the 20 conditions, our method gave an estimate of the percentage of all expenditure that was within 50–200% of the estimate from Dieleman et al (2016). Figure 7. Scatterplot comparing the USA’s proportion of total health expenditure on select NCDs using health expenditure estimates from this study and Dieleman et al (2016) [ 8 ]. Discussion Our method estimated an average health expenditure on major NCDs across OECD member countries of US $ 207 million per 100,000 population. The USA had the highest relative expenditure at US $ 599 million, and (according to our method) musculoskeletal disorders topped the list of NCDs with the highest proportion of total health expenditure across member countries. After pooling health expenditure on NCDs across countries, females had significantly higher expenditure on musculoskeletal disorders, as well as mental and substance use disorders, and neurological conditions. Males had significantly higher expenditure on kidney and urinary diseases, and cancer and other neoplasms. OECD member countries were chosen for this study as we assumed that health expenditure in Australia and NZ would be similar in relative pattern to these countries, given similarities in disease burden patterns (i.e. NCDs are responsible for most of the disease burden in high-income countries). However, for the three country-specific studies that we could compare our method to – Norway, Switzerland, and the USA [ 6 – 8 ] (Figs. 4 and 5) – the agreement on the percentage of expenditure by disease was often poor. Comparing between expenditure studies is challenging, due to differences in disease group classifications, health care function expenditure areas, and years that the data was based on – as well as ‘actual’ variation in expenditure by disease between Australia (the base of our method) with other countries. In Norway a large amount of expenditure on long term care is considered health care expenditure – whereas in Australia long term care is generally not considered health expenditure and is excluded from disease expenditure estimates. The Norwegian analysis by Kinge et al (2023) included service data on general practitioners, physiotherapists and chiropractors, day patient, specialised outpatient, inpatient, prescription drugs, home-based long-term care, and nursing homes [ 6 ]. The Australian analysis included expenditure on hospital admitted patients, outpatients and emergency departments, government subsidised medical services (GP, specialist, allied health, medical imaging, and pathology), prescription pharmaceuticals, and dental. Given the differences in how health system expenditure is classified between these countries, the Norwegian study had much higher estimated expenditure for dementia than our study. The USA analysis by Dieleman et al (2016) included service data on ambulatory care, inpatient care, pharmaceuticals, emergency care, and nursing facility care [ 8 ]. While there were differences in service data used in the USA and Australian analyses, differences in medication costs between the two countries would have likely contributed to differences in cost estimates for diseases like diabetes. The OECD has undertaken initial comparisons of disease expenditure across member countries. While this used different methods, data sources, and had a limited scope, it found that CVD was estimated to be the highest expenditure group, while in our study musculoskeletal was the highest, followed by cancer and other neoplasms then CVD [ 9 ]. Females still accounted for higher expenditure on musculoskeletal disorders, and mental and substance use disorders, while males now accounted for higher expenditure on kidney and urinary diseases, and cancer and other neoplasms [ 9 ]. Strengths and limitations Our study leveraged high quality, comprehensive data from internationally renowned data sources to generate disease expenditure estimates across OECD countries. The GBD is the most current comprehensive observational epidemiological study worldwide, while the OECDs online database is the most comprehensive source of comparable statistics on health systems across industrialised countries. The Blakely et al. (2019) analysis utilised linked health data from Statistics NZ, and both that source and the AIHW are known for their comprehensive datasets that capture comorbidity-adjusted costs for each disease and costs to all payers for expenditure areas [ 4 , 5 ]. There are several key limitations of this study. Only 73% of recurrent expenditure in Australia was allocated in the AIHWs disease expenditure 2018-19 study [ 12 ]. Areas of expenditure not included were over-the-counter pharmaceuticals, other health practitioners, community health, public health, and research remaining unallocated [ 12 ]. An assumption of our study is that this missing disease-related expenditure is distributed similarly to the included expenditure (this missing gap being assumed to be another 8% of all health expenditure using the 81% average across OECD countries of national health accounts expenditure on ‘health care functions’ that we assumed equated to the marginal expenditure driven by diseases). Other key concerns include other OECD countries having different relative expenditure per capita than Australia. For example, the cost and modality (inpatient versus outpatient) of chemotherapy delivery may differ for the same cancer between countries, altering the relative expenditure per capita within cancers. Similarly, the NZ relative costing ratios by disease phase may not be applicable to all OECD countries due to differing medical care and treatment strategies by disease phases, such as approaches to end of life care (expensive treatments versus palliative care). The OECD’s health expenditure accounts may not be capturing all health expenditure within member countries due to within-country limitations on data availability and extent to which data is captured. There is also the possibility of measurement error caused from expenditure estimates being miscalculated across one or more data sources, though this error would be consistent throughout our study as the same data sources are used throughout. Data was also used from 2019, and health system expenditure and disease epidemiology may have changed since across OECD countries. Research and policy implications This is the first known study to have produced health expenditure estimates stratified by disease, disease phase, sex, and age group across many countries. These estimates provide governments and researchers with a tool to quantify the economic and health system impacts of NCDs, identifying priority diseases attributing the most burden to national health expenditure, informing the allocation of health system expenditure, and improving health system efficiency. But they are merely a ‘first attempt’ at comparable cross-country disease expenditure estimation – improvements are required. Future research should look further into the reasons for the residual variation between health expenditure across countries, between our study and all three of the Norwegian, Swiss and US studies [ 6 – 8 ]. On the one hand, progress is needed to make country-level expenditure comparisons less prone to methodological and data differences – meaning any remaining differences are ‘true’ and due to (say) differences in pharmaceutical expenditure, patient care pathways, and any number of other factors. On the other hand, as we gain confidence in what the ‘true’ differences in country-level expenditure are, then predictors of such variation should be identified. For example, private versus public expenditure, greater or lesser focus on new technologies, GDP per capita, per capita pharmaceutical expenditure, and such like. We envisage such predictors being used in predictive algorithms (e.g. machine or ensemble learning) that takes what data we can collate between countries and ‘fills in the gaps’ to give better estimates of expenditure by disease in all countries. Conclusion Having access to a comparable set of disease expenditure estimates across OECD countries provides a starting point to understand which diseases contribute the most economic burden, informing decisions on health expenditure allocation and prevention priorities to improve population health and accelerate progress towards achieving international health targets. Nevertheless, the estimates in this study are rudimentary; more research is required to incrementally move to more accurate estimates of expenditure by disease across countries. Abbreviations AIHW Australian Institute of Health and Welfare CVD Cardiovascular disease DALY Disability-adjusted life year GBD Global Burden of Disease GHDx Global Health Data Exchange ICD-10 International Classification of Diseases 10 th Revision IHD Ischaemic heart disease NCD Non-communicable disease NZ New Zealand OECD Organisation for Economic Co-operation and Development PMSLT Proportional multistate lifetable PPP Purchasing power parity SHA System of Health Accounts USA United States of America US$ United States dollars WHO World Health Organisation Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Data generated during this study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Authors’ contributions SG, EB and TB designed the study and conducted formal analyses. SG and EB drafted the manuscript. TB provided review and guidance. All authors read and approved the final transcript. The views expressed in this article are those of the authors alone and do not necessarily represent the views of the institutions with which they are affiliated. Acknowledgements Comments on a draft of this manuscript were received from Professor Philip Clarke, University of Oxford. References World Health Organisation. Noncommunicable diseases progress monitor 2022. Geneva: World Health Organisation; 2022. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019). Seattle: Institute for Health Metrics and Evaluation; 2020. Bourke E, Grimshaw S, Blakely T. What is the value of disease expenditure studies? An argument for an international database of spending estimates. BMC Global Public Health. 2023;1:23. Australian Institute of Health and Welfare. Disease expenditure in Australia 2018-19. (Welfare AIoHa ed. Canberra; 2021. Blakely T, Kvizhinadze G, Atkinson J, Dieleman J, Clarke P. Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand. PLoS Med. 2019;16:e1002716. Kinge JM, Dieleman JL, Karlstad Ø, Knudsen AK, Klitkou ST, Hay SI, Vos T, Murray CJL, Vollset SE. Disease-specific health spending by age, sex, and type of care in Norway: a national health registry study. BMC Med. 2023;21:201. Wieser S, Riguzzi M, Pletscher M, Huber CA, Telser H, Schwenkglenks M. How much does the treatment of each major disease cost? A decomposition of Swiss National Health Accounts. Eur J Health Econ. 2018;19:1149–61. Dieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A, Hamavid H, Horst C, Johnson EK, Joseph J, et al. US Spending on Personal Health Care and Public Health, 1996–2013. JAMA. 2016;316:2627–46. Organisation for Economic Co-operation and Development. Expenditure by disease, age and gender - focus on health spending. Paris; 2016. Organisation for Economic Co-operation and, Development E. World Health Organisation,: A System of Health Accounts 2011. Paris; 2017. Organisation for Economic Co-operation. and Development: OECD Health Data. 2019. Australian Institute of Health and Welfare. Disease Expenditure Study: Overview of analysis and methodology 2018–2019. Canberra: Australian Institute of Health and Welfare; 2021. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Population Health Metrics → Version 1 posted Editorial decision: Revision requested 04 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 23 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 31 Jul, 2024 Submission checks completed at journal 31 Jul, 2024 First submitted to journal 24 Jul, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4798785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339738228,"identity":"97a09fff-ed89-4558-8652-d2032511f155","order_by":0,"name":"Samantha Grimshaw","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Grimshaw","suffix":""},{"id":339738232,"identity":"5c323122-4e14-4b7e-b50b-7b40b85f5568","order_by":1,"name":"Emily Bourke","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Bourke","suffix":""},{"id":339738233,"identity":"aacb08bb-2068-47df-a4a6-9068b44ba1b2","order_by":2,"name":"Tony Blakely","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3PMUvDQBTA8ReEdAlkfRm0X+GkUCIWxG+SW3S5ZglIhwyvBOIiugb8Ei1C58aDTPkAJ3bp3kERBJfinbbjEd0E7z/kXiA/Xg7A5fqL4QF5BDGEel4CxIk+WQfxDEGI6IvgLwhbfr92k/B+On2tJsgf1Lh+VBNMoVcsEHJpX7Kqi2jWIl+oNJGixQyC5gqhsRNQnKJ1aYhgcqwHQjFE8O2kr3jxsd7qH6sM0QP1N5ps7YQpXkZzQj5DQ8hsCYbolXZyvKrLk6rBQdVumBQNZn5wkcX89tJKjp4L+XSTjw7vrsXgTeSjNOzJuXp5P7Vff9c57SffPJJOAHD2g29cLpfrv/YJechcSA4T/kYAAAAASUVORK5CYII=","orcid":"","institution":"University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Tony","middleName":"","lastName":"Blakely","suffix":""}],"badges":[],"createdAt":"2024-07-25 04:00:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4798785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4798785/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12963-025-00418-5","type":"published","date":"2025-10-08T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64005797,"identity":"78b5af93-85fd-4712-8b78-55880542eb99","added_by":"auto","created_at":"2024-09-04 21:41:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":429460,"visible":true,"origin":"","legend":"\u003cp\u003eTotal disease expenditure per 100,000 population for non-communicable diseases across OECD member countries in 2019 (OECD average = US$207 million per 100,000 population).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/e62a1ab54d9d680bd9bc14ea.png"},{"id":64005798,"identity":"1ee685c1-46f5-4712-9ecc-df54e70a3fcb","added_by":"auto","created_at":"2024-09-04 21:41:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":624950,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of total health expenditure by disease groups across OECD member countries in 2019.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/1a9b370a434013ca5676a1ef.png"},{"id":64006299,"identity":"99e9400d-9b08-4067-8286-325428c48b83","added_by":"auto","created_at":"2024-09-04 21:49:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":481291,"visible":true,"origin":"","legend":"\u003cp\u003eHealth expenditure on non-communicable diseases by disease groups and sex across OECD member countries in 2019; all countries pooled, crude analyses (i.e. no age-standardisation).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/869f7b992f23075db35953ef.png"},{"id":64005800,"identity":"5b1936e4-1654-42d4-9633-4a728520e3e6","added_by":"auto","created_at":"2024-09-04 21:41:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228925,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Norway, Kinge et al (2023) [6].\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/e34a4bc7939c19a41227a5e2.png"},{"id":64005801,"identity":"60f731a4-0abe-4e3e-8e32-cdc9d279d58c","added_by":"auto","created_at":"2024-09-04 21:41:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":332085,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Switzerland,\u003cstrong\u003e \u003c/strong\u003eWieser et al (2018) [7].\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/6f1eab69acd40d261b476fb4.png"},{"id":64005804,"identity":"2dd1ec88-520f-434c-ba2a-021f61b654d8","added_by":"auto","created_at":"2024-09-04 21:41:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":228016,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with USA, Dieleman et al (2016) [8].\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/216e68deb79a00852c7ba839.png"},{"id":64006287,"identity":"c2590167-3618-4a63-a32e-24286cf6674e","added_by":"auto","created_at":"2024-09-04 21:49:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":642934,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot comparing the USA’s proportion of total health expenditure on select NCDs using health expenditure estimates from this study and Dieleman et al (2016) [8].\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/33a60fad3932628a4063f7bd.png"},{"id":93421155,"identity":"4fcb1b29-aea0-4ddb-8295-5a5d478c405b","added_by":"auto","created_at":"2025-10-13 16:10:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2861530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/bfc06967-0672-4730-a900-33c0901693df.pdf"},{"id":64006288,"identity":"bd026f6b-6977-46a4-84d1-117ffb8a478d","added_by":"auto","created_at":"2024-09-04 21:49:13","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":450432,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4798785/v1/c5955f8cd775f7f86c14c982.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimates of non-communicable disease expenditure by disease phase, sex, and age group for all OECD countries","fulltext":[{"header":"Background","content":"\u003cp\u003eNon-communicable diseases (NCDs) are the leading cause of death and disability worldwide, responsible for nearly 75% of all yearly deaths and 80% of years lived with a disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Across the 38 Organisation for Economic Co-operation and Development (OECD) member countries (34 high-income and 4 upper-middle income economies), more than 10\u0026nbsp;million people died from NCDs in 2019 \u0026ndash; equivalent to 89% of all deaths. Cardiovascular disease (CVD) and cancer accounted for most of this burden (32% and 28% of total deaths respectively), followed by chronic respiratory diseases (6%), Alzheimer\u0026rsquo;s and dementia (6%), and diabetes (3%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These conditions also represent a significant disability burden, contributing a total of 216\u0026nbsp;million disability-adjusted life years (DALYs) \u0026ndash; equivalent to 54% of all DALYs across member countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is an increasing demand for internationally comparable disease costing data to aid policy makers in understanding where to allocate health system expenditure, improving efficiency, and conducting health economic evaluations of public health policies and interventions in the face of growing NCD burden and aging populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEfforts to quantify the financial and health system impacts of NCDs are limited by data availability, comparability, and scope. Disease expenditure studies having only been undertaken comprehensively by a handful of countries, including Australia, New Zealand (NZ), Norway, Switzerland, and the United States of America (USA) [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The OECD generated preliminary disease expenditure estimates based on comparable health expenditure estimates using the System of Health Accounts (SHA) framework [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, less than half of all OECD countries were able to provide estimates, and differences in data coverage led to large unallocated expenditure, but their analysis showed most allocated health expenditure had gone to major NCDs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Women accounted for more than half of this spending due to higher expenditure on mental health and musculoskeletal conditions, while men had more hospital spending due to their increased expenditure on CVD, injuries, and mental health conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study aims to estimate comparable NCD expenditure estimates by disease phase, sex and age group for all 38 OECD member countries (all ages) \u0026ndash; filling a critical information gap in global health metrics. It is a first attempt at generating a single dataset of comparable disease expenditure estimates across multiple countries, and one that we hope encourages others to improve on.\u003c/p\u003e \u003cp\u003eOur methodology uses comprehensive Australasian health expenditure estimates as the starting point: Australian disease expenditure estimates and NZ relative expenditure ratios for NCDs in the first year of diagnosis, last year of life if dying of that disease, and otherwise prevalent. This data was combined with epidemiological case numbers for each OECD country to approximate aggregate expenditure with an Australian cost base. It was then scaled to each country\u0026rsquo;s total health system expenditure to estimate total NCD expenditure by disease phase, sex, and age group.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eThe study\u0026rsquo;s data inputs included population estimates, and disease incidence, prevalence and mortality rates from the GBD 2019 for each OECD country [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; total health expenditure for each disease by sex and age group from the Australian Institute of Health and Welfare (AIHW) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; relative NCD expenditure ratios for each disease phase from NZ [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; and total health expenditure for each member country from the OECD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. All statistical analyses were performed using R (version 2022.12.0\u0026thinsp;+\u0026thinsp;353) and expenditure estimates are reported as 2019 purchasing power parity (PPP)-adjusted United States dollars (US\u003cspan\u003e$\u003c/span\u003e) using OECD PPPs.\u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe female and male populations of each OECD country were categorised into 19 age groups (\u0026lt;\u0026thinsp;1 year, 1\u0026ndash;4 years, 5\u0026ndash;9 years, \u0026hellip;, 85\u0026thinsp;+\u0026thinsp;years) using the GBD 2019 population estimates.\u003c/p\u003e \u003cp\u003eDisease groupings for OECD member countries\u003c/p\u003e \u003cp\u003eThere were 80 Level 3 NCD causes from the GBD 2019 used in our study (see Table S2).\u003c/p\u003e \u003cp\u003eConcordance of Australian conditions to GBD causes\u003c/p\u003e \u003cp\u003eThere are 91 AIHW conditions that correspond to the 80 level 3 NCD causes from the GBD 2019 across 10 disease groups (see Table S2). Inflammatory heart disease (one of the AIHW conditions) corresponds with two GBD causes: cardiomyopathy and myocarditis, and endocarditis. Therefore, inflammatory heart disease\u0026rsquo;s expenditure was disaggregated across the two GBD causes based on the number of prevalent cases for each cause.\u003c/p\u003e \u003cp\u003eIn each disease group, there were AIHW conditions that did not have a corresponding level 3 NCD cause from the GBD 2019. Therefore, an additional NCD cause was created for each disease group (i.e. 10 additional NCD causes in total) that corresponded to these remaining AIHW conditions, such as \u0026lsquo;all other cancer conditions\u0026rsquo; and \u0026lsquo;all other CVD conditions\u0026rsquo;.\u003c/p\u003e \u003cp\u003eAn \u0026lsquo;all other\u0026rsquo; cause was also created to allocate remaining health expenditure from conditions not included in either the 80 level 3 NCD causes or the additional 10 NCD causes. These include injuries; communicable, maternal, neonatal and nutritional diseases; and minor NCDs (e.g. congenital birth defects, oral disorders).\u003c/p\u003e \u003cp\u003eDisease phase\u003c/p\u003e \u003cp\u003eThe 80 level 3 NCD causes were assigned relative disease phase costing ratios from Blakely et al (2019) for first year of diagnosis, last year of diagnosis if dying of that disease, and otherwise prevalent [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Where there was not a matching phase ratio from Blakely et al (2019), NCD causes were assigned either a \u0026lsquo;flat\u0026rsquo; ratio (i.e. a 1 : 1 : 1 ratio; for diseases whose treatment is expected to be similar across phase) or an \u0026lsquo;average\u0026rsquo; ratio (i.e. a 2.40 : 1: 2.72 ratio; remaining diseases) which is the average of all NCD costing ratios from the Blakely et al analysis)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOECD health system expenditure\u003c/p\u003e \u003cp\u003eFor the OECD dataset containing health system expenditure by health care functions, the governance and health system and financing administration, long-term care, and preventative care function expenditure estimates were subtracted from the total health system expenditure estimate for each member country as the AIHW\u0026rsquo;s disease expenditure 2018-19 study did not include these functions in their estimates [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We conceptualised the OECD estimated percentage of each country\u0026rsquo;s national health accounts due to \u0026lsquo;health care functions\u0026rsquo; as equivalent to expenditure that can be disaggregated by disease, or put another way that expenditure that is marginal given the number of people with disease. However, Colombia, Israel, New Zealand, and Turkey did not have total health system expenditure disaggregated by health care function, therefore the average proportion of remaining health expenditure from total health system expenditure (i.e., on average, 81% of health system expenditure was remaining once expenditure estimates from the three health care functions were subtracted from the total health system expenditure estimate) was applied to these countries (see Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerating epidemiological case numbers for each disease phase by sex and age group for each OECD member country\u003c/p\u003e \u003cp\u003eFor each OECD country, GBD incidence, prevalence, and mortality rates for each of the 80 NCDs were converted to per person values. Case fatality rates were then calculated for each disease by dividing their mortality rate by their prevalence. Aortic aneurism only had mortality data, so a mortality risk was calculated instead (i.e., 1-exponent (-[mortality rate]).\u003c/p\u003e \u003cp\u003eFor each disease, sex, and age group combination, incidence and prevalence rates, and mortality risk were multiplied by population estimates to generate the number of cases for each OECD country. Case fatality rates were multiplied by the number of prevalent cases for that disease to get the number of cases in their last year of life with the disease. As the GBD uses point prevalence, the finalised number of period (annual) prevalent cases was calculated by subtracting half the number of incidence cases and half the number of cases in their last year of life with the disease.\u003c/p\u003e \u003cp\u003eSome diseases did not have corresponding GBD data. For each of the 10 additional NCDs created for AIHW conditions without a corresponding level 3 NCD cause from the GBD 2019, a prevalent case number was calculated for each for each OECD country, sex and age group combination based on the prevalence of specified conditions within that group, assuming the ratio of the \u0026lsquo;other\u0026rsquo; groups to named NCD categories in Australia applied to other OECD countries.\u003c/p\u003e \u003cp\u003eFor the \u0026lsquo;all other\u0026rsquo; disease created for conditions not included in either the 80 level 3 NCD causes or the additional 10 NCD causes, the prevalent case number for each sex and age group combination was the GBD population estimate for that country\u0026rsquo;s sex and age group combination.\u003c/p\u003e \u003cp\u003eGenerating health expenditure estimates per person by disease, disease phase, sex, and age group for Australia\u003c/p\u003e \u003cp\u003eFor each disease, sex and age group combination, a weighted expenditure value was calculated. The expenditure (in USD) per prevalent case in Australia of disease (d), within each sex (s) and age group (a) was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Exp\\:prev\\:cases}_{Aus,\\:dsa}=\\:\\:\\frac{{Exp\\:all\\:cases}_{Aus,\\:dsa}}{\\sum\\:_{phase}\\left({ratio}_{phase,\\:Aus,\\:dsa}\\times\\:{N}_{phase,Aus,\\:dsa}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere ratio is the relative ratio of expenditure for incident or last year of life cases compared to prevalent cases from NZ (where the ratio for prevalent cases is 1), and N is the number of disease cases in Australia in 2019 by phase estimated from GBD data (above). The expenditure per incident and last year of life (if dying of that disease) was the above prevalent estimate multiplied by its phase ratio.\u003c/p\u003e \u003cp\u003eFor the \u0026lsquo;all other\u0026rsquo; disease, its scaled expenditure value was divided by population estimate.\u003c/p\u003e \u003cp\u003eGenerating health expenditure estimates per person by disease, disease phase, sex, and age group for each OECD member country\u003c/p\u003e \u003cp\u003eA country-specific scalar (c) was calculated, which adjusted for all country-level total health expenditure, demography and disease epidemiology:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Scalar}_{c}=\\:\\:\\frac{{Exp\\:all\\:diseases\\:from\\:national\\:health\\:accounts}_{c}}{\\sum\\:_{phase,dsa}\\left({N}_{phase,c,\\:dsa}\\times\\:\\:{Exp}_{phase,Aus,dsa}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis country specific scalar was multiplied by disease expenditure per person estimates (calculated in the previous section) to generate expenditure (in USD) by disease phase, sex and age group for each OECD member country. The sum of these estimates in each country will not be the same as their total health expenditure estimates from the OECD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, our study\u0026rsquo;s country estimates were scaled to ensure they equalled the relevant \u0026lsquo;health care functions\u0026rsquo; OECD expenditure estimates for each country.\u003c/p\u003e \u003cp\u003eComparisons with pre-existing health expenditure estimates\u003c/p\u003e \u003cp\u003eWe compared our country-level estimates with three separate country studies that used a bottom up costing of services by diagnosis using similar methods to the Australian disease expenditure study: Norway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Switzerland [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the USA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eHealth spending on NCDs across OECD member countries\u003c/p\u003e \u003cp\u003e In 2019, the average health expenditure on NCDs across OECD countries was US\u003cspan\u003e$\u003c/span\u003e207\u0026nbsp;million per 100,000 population (see Fig.\u0026nbsp;1). The United States of America (USA) clearly had the highest relative spending at US\u003cspan\u003e$\u003c/span\u003e599\u0026nbsp;million per 100,000 while Turkey had the lowest expenditure at US\u003cspan\u003e$\u003c/span\u003e56\u0026nbsp;million.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTotal disease expenditure per 100,000 population for non-communicable diseases across OECD member countries in 2019 (OECD average\u0026thinsp;=\u0026thinsp;US\u003cspan\u003e$\u003c/span\u003e207\u0026nbsp;million per 100,000 population).\u003c/p\u003e \u003cp\u003eFor all OECD countries pooled, musculoskeletal disorders contributed to the highest proportion of total health expenditure (17.4%), followed by cancer and other neoplasms (9.4%), CVD (9.1%), mental and substance use disorders (6.1%), gastrointestinal disorders (5.0%), skin disorders (4.7%), endocrine disorders (3.9%), kidney and urinary diseases (3.5%), neurological conditions (3.2%), and respiratory diseases (2.5%). The remaining 35.3% of total health expenditure was attributable to other diseases (see Fig.\u0026nbsp;2 and Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFor the top three expenditure conditions, the following percentage expenditure variations were observed between countries: 18.6% in the United Kingdom to 12.3% in Costa Rica for musculoskeletal disorders; 15.5% in Canada to 3.0% in Mexico for cancer and other neoplasms; and 14.1% in Estonia to 5.4% in Colombia for CVD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProportion of total health expenditure by disease groups across OECD member countries in 2019.\u003c/p\u003e \u003cp\u003eFigure 3 shows the proportion of health expenditure on NCDs by sex: females had relatively higher expenditure than males for musculoskeletal disorders (1.28 fold greater or 12.2 percentage points greater), mental and substance use disorders (1.26 fold greater or 11.6 percentage points greater), and neurological conditions (1.21 fold greater or 9.7 percentage points greater) (see Fig.\u0026nbsp;3). Males had significantly higher expenditure on kidney and urinary diseases (1.76 fold greater or 27.6 percentage points greater), and cancer and other neoplasms (1.40 fold greater or 16.6 percentage points greater).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHealth expenditure on non-communicable diseases by disease groups and sex across OECD member countries in 2019; all countries pooled, crude analyses (i.e. no age-standardisation).\u003c/p\u003e \u003cp\u003eAcross the three disease phases, the majority of total NCD expenditure went to the prevalent phase (at an average across NCDs of 60.7%), followed by the first year of diagnosis (at an average 36.8%), and the last year of life (at 2.6%; noting that this is expenditure attributed to the cause of death, not all expenditure by the health system in the last year of life).\u003c/p\u003e \u003cp\u003eComparisons with pre-existing health expenditure estimates\u003c/p\u003e \u003cp\u003eFigure 4 shows the percentage of Norway\u0026rsquo;s total health expenditure in 2019 across 7 aggregated disease groups that were similar between our study and the most recent Norwegian-specific study by Kinge et al (2023) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. There was poor agreement for neurological (3.9% of all health expenditure using our method, compared to 15.4% in the Kinge et al study), mental and substance use (20.7% and 6.3%) and musculoskeletal (6.7% and 16.0%), but reasonably good agreement for the four other diseases shown.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Norway, Kinge et al (2023) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure 5 shows a similar comparison for Switzerland, for eight comparable disease groupings with Weiser et al (2018) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There was poor agreement for CVD (7.1% our method, 15.6% Weiser et al). There was moderate agreement for mental and substance use (7.3% and 10.6%), cancer and other neoplasms (8.3% and 6.0%) and musculoskeletal (19.6% and 13.4%). The remaining four conditions had good agreement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with Switzerland, Wieser et al (2018) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure 6 shows a comparison for the USA, for seven comparable groupings with Dieleman et al (2016)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There was poor agreement for musculoskeletal (17.3% our method, 8.7% Dieleman et al (2016)), cancer and other neoplasms (10.6% and 5.5%) and respiratory (3.0% and 6.3%). The other four conditions had moderate to good (gastrointestinal) agreement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 6.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eScatterplot comparing aggregated disease group expenditure estimates from our study (x-axes; percent) with USA, Dieleman et al (2016) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLooking across these three country comparisons, our method consistently estimates greater percentage musculoskeletal disease expenditure than the country-specific studies. Less notable general differences include our method estimating greater cancer and other neoplasm expenditure, and estimating less mental and substance use expenditure.\u003c/p\u003e \u003cp\u003eThe Dieleman et al (2016) study also permitted a comparison of 20 more disaggregated conditions (Fig.\u0026nbsp;7, log scale both axes). For nine of the 20 conditions, our method gave an estimate of the percentage of all expenditure that was within 50\u0026ndash;200% of the estimate from Dieleman et al (2016).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eScatterplot comparing the USA\u0026rsquo;s proportion of total health expenditure on select NCDs using health expenditure estimates from this study and Dieleman et al (2016) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur method estimated an average health expenditure on major NCDs across OECD member countries of US\u003cspan\u003e$\u003c/span\u003e207\u0026nbsp;million per 100,000 population. The USA had the highest relative expenditure at US\u003cspan\u003e$\u003c/span\u003e599\u0026nbsp;million, and (according to our method) musculoskeletal disorders topped the list of NCDs with the highest proportion of total health expenditure across member countries. After pooling health expenditure on NCDs across countries, females had significantly higher expenditure on musculoskeletal disorders, as well as mental and substance use disorders, and neurological conditions. Males had significantly higher expenditure on kidney and urinary diseases, and cancer and other neoplasms.\u003c/p\u003e \u003cp\u003eOECD member countries were chosen for this study as we assumed that health expenditure in Australia and NZ would be similar in relative pattern to these countries, given similarities in disease burden patterns (i.e. NCDs are responsible for most of the disease burden in high-income countries). However, for the three country-specific studies that we could compare our method to \u0026ndash; Norway, Switzerland, and the USA [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (Figs.\u0026nbsp;4 and 5) \u0026ndash; the agreement on the percentage of expenditure by disease was often poor.\u003c/p\u003e \u003cp\u003eComparing between expenditure studies is challenging, due to differences in disease group classifications, health care function expenditure areas, and years that the data was based on \u0026ndash; as well as \u0026lsquo;actual\u0026rsquo; variation in expenditure by disease between Australia (the base of our method) with other countries. In Norway a large amount of expenditure on long term care is considered health care expenditure \u0026ndash; whereas in Australia long term care is generally not considered health expenditure and is excluded from disease expenditure estimates. The Norwegian analysis by Kinge et al (2023) included service data on general practitioners, physiotherapists and chiropractors, day patient, specialised outpatient, inpatient, prescription drugs, home-based long-term care, and nursing homes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Australian analysis included expenditure on hospital admitted patients, outpatients and emergency departments, government subsidised medical services (GP, specialist, allied health, medical imaging, and pathology), prescription pharmaceuticals, and dental. Given the differences in how health system expenditure is classified between these countries, the Norwegian study had much higher estimated expenditure for dementia than our study.\u003c/p\u003e \u003cp\u003eThe USA analysis by Dieleman et al (2016) included service data on ambulatory care, inpatient care, pharmaceuticals, emergency care, and nursing facility care [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While there were differences in service data used in the USA and Australian analyses, differences in medication costs between the two countries would have likely contributed to differences in cost estimates for diseases like diabetes.\u003c/p\u003e \u003cp\u003eThe OECD has undertaken initial comparisons of disease expenditure across member countries. While this used different methods, data sources, and had a limited scope, it found that CVD was estimated to be the highest expenditure group, while in our study musculoskeletal was the highest, followed by cancer and other neoplasms then CVD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Females still accounted for higher expenditure on musculoskeletal disorders, and mental and substance use disorders, while males now accounted for higher expenditure on kidney and urinary diseases, and cancer and other neoplasms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eOur study leveraged high quality, comprehensive data from internationally renowned data sources to generate disease expenditure estimates across OECD countries. The GBD is the most current comprehensive observational epidemiological study worldwide, while the OECDs online database is the most comprehensive source of comparable statistics on health systems across industrialised countries. The Blakely et al. (2019) analysis utilised linked health data from Statistics NZ, and both that source and the AIHW are known for their comprehensive datasets that capture comorbidity-adjusted costs for each disease and costs to all payers for expenditure areas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several key limitations of this study. Only 73% of recurrent expenditure in Australia was allocated in the AIHWs disease expenditure 2018-19 study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Areas of expenditure not included were over-the-counter pharmaceuticals, other health practitioners, community health, public health, and research remaining unallocated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An assumption of our study is that this missing disease-related expenditure is distributed similarly to the included expenditure (this missing gap being assumed to be another 8% of all health expenditure using the 81% average across OECD countries of national health accounts expenditure on \u0026lsquo;health care functions\u0026rsquo; that we assumed equated to the marginal expenditure driven by diseases).\u003c/p\u003e \u003cp\u003eOther key concerns include other OECD countries having different relative expenditure per capita than Australia. For example, the cost and modality (inpatient versus outpatient) of chemotherapy delivery may differ for the same cancer between countries, altering the relative expenditure per capita within cancers. Similarly, the NZ relative costing ratios by disease phase may not be applicable to all OECD countries due to differing medical care and treatment strategies by disease phases, such as approaches to end of life care (expensive treatments versus palliative care).\u003c/p\u003e \u003cp\u003eThe OECD\u0026rsquo;s health expenditure accounts may not be capturing all health expenditure within member countries due to within-country limitations on data availability and extent to which data is captured.\u003c/p\u003e \u003cp\u003eThere is also the possibility of measurement error caused from expenditure estimates being miscalculated across one or more data sources, though this error would be consistent throughout our study as the same data sources are used throughout. Data was also used from 2019, and health system expenditure and disease epidemiology may have changed since across OECD countries.\u003c/p\u003e \u003cp\u003eResearch and policy implications\u003c/p\u003e \u003cp\u003eThis is the first known study to have produced health expenditure estimates stratified by disease, disease phase, sex, and age group across many countries. These estimates provide governments and researchers with a tool to quantify the economic and health system impacts of NCDs, identifying priority diseases attributing the most burden to national health expenditure, informing the allocation of health system expenditure, and improving health system efficiency. But they are merely a \u0026lsquo;first attempt\u0026rsquo; at comparable cross-country disease expenditure estimation \u0026ndash; improvements are required.\u003c/p\u003e \u003cp\u003eFuture research should look further into the reasons for the residual variation between health expenditure across countries, between our study and all three of the Norwegian, Swiss and US studies [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the one hand, progress is needed to make country-level expenditure comparisons less prone to methodological and data differences \u0026ndash; meaning any remaining differences are \u0026lsquo;true\u0026rsquo; and due to (say) differences in pharmaceutical expenditure, patient care pathways, and any number of other factors. On the other hand, as we gain confidence in what the \u0026lsquo;true\u0026rsquo; differences in country-level expenditure are, then predictors of such variation should be identified. For example, private versus public expenditure, greater or lesser focus on new technologies, GDP per capita, per capita pharmaceutical expenditure, and such like. We envisage such predictors being used in predictive algorithms (e.g. machine or ensemble learning) that takes what data we can collate between countries and \u0026lsquo;fills in the gaps\u0026rsquo; to give better estimates of expenditure by disease in all countries.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHaving access to a comparable set of disease expenditure estimates across OECD countries provides a starting point to understand which diseases contribute the most economic burden, informing decisions on health expenditure allocation and prevention priorities to improve population health and accelerate progress towards achieving international health targets. Nevertheless, the estimates in this study are rudimentary; more research is required to incrementally move to more accurate estimates of expenditure by disease across countries.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIHW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Australian Institute of Health and Welfare\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDALY \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Disability-adjusted life year\u003c/p\u003e\n\u003cp\u003eGBD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eGHDx \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Global Health Data Exchange\u003c/p\u003e\n\u003cp\u003eICD-10 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;International Classification of Diseases 10\u003csup\u003eth\u003c/sup\u003e Revision\u003c/p\u003e\n\u003cp\u003eIHD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ischaemic heart disease\u003c/p\u003e\n\u003cp\u003eNCD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-communicable disease\u003c/p\u003e\n\u003cp\u003eNZ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;New Zealand\u003c/p\u003e\n\u003cp\u003eOECD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Organisation for Economic Co-operation and Development\u003c/p\u003e\n\u003cp\u003ePMSLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Proportional multistate lifetable\u003c/p\u003e\n\u003cp\u003ePPP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Purchasing power parity\u003c/p\u003e\n\u003cp\u003eSHA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;System of Health Accounts\u003c/p\u003e\n\u003cp\u003eUSA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United States of America\u003c/p\u003e\n\u003cp\u003eUS$ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;United States dollars\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organisation\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eData generated during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eSG, EB and TB designed the study and conducted formal analyses. SG and EB drafted the manuscript. TB provided review and guidance. All authors read and approved the final transcript. The views expressed in this article are those of the authors alone and do not necessarily represent the views of the institutions with which they are affiliated.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eComments on a draft of this manuscript were received from Professor Philip Clarke, University of Oxford.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organisation. Noncommunicable diseases progress monitor 2022. Geneva: World Health Organisation; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019). Seattle: Institute for Health Metrics and Evaluation; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourke E, Grimshaw S, Blakely T. What is the value of disease expenditure studies? An argument for an international database of spending estimates. BMC Global Public Health. 2023;1:23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare. Disease expenditure in Australia 2018-19. (Welfare AIoHa ed. Canberra; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlakely T, Kvizhinadze G, Atkinson J, Dieleman J, Clarke P. Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand. PLoS Med. 2019;16:e1002716.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinge JM, Dieleman JL, Karlstad \u0026Oslash;, Knudsen AK, Klitkou ST, Hay SI, Vos T, Murray CJL, Vollset SE. Disease-specific health spending by age, sex, and type of care in Norway: a national health registry study. BMC Med. 2023;21:201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWieser S, Riguzzi M, Pletscher M, Huber CA, Telser H, Schwenkglenks M. How much does the treatment of each major disease cost? A decomposition of Swiss National Health Accounts. Eur J Health Econ. 2018;19:1149\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDieleman JL, Baral R, Birger M, Bui AL, Bulchis A, Chapin A, Hamavid H, Horst C, Johnson EK, Joseph J, et al. US Spending on Personal Health Care and Public Health, 1996\u0026ndash;2013. JAMA. 2016;316:2627\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation for Economic Co-operation and Development. Expenditure by disease, age and gender - focus on health spending. Paris; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation for Economic Co-operation and, Development E. World Health Organisation,: A System of Health Accounts 2011. Paris; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation for Economic Co-operation. and Development: OECD Health Data. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Institute of Health and Welfare. Disease Expenditure Study: Overview of analysis and methodology 2018\u0026ndash;2019. Canberra: Australian Institute of Health and Welfare; 2021.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"population-health-metrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pohm","sideBox":"Learn more about [Population Health Metrics](http://pophealthmetrics.biomedcentral.com/)","snPcode":"12963","submissionUrl":"https://submission.nature.com/new-submission/12963/3","title":"Population Health Metrics","twitterHandle":"@PHMjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"disease expenditure, health expenditure, OECD countries, non-communicable diseases","lastPublishedDoi":"10.21203/rs.3.rs-4798785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4798785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eAcross OECD member countries, non-communicable diseases (NCDs) accounted for nearly 90% of deaths and over half of disability-adjusted life years lost. NCD health expenditure estimates are necessary to estimate future health expenditure trajectories for different prevention and treatment policies. However, no dataset of comparable estimates exists across OECD countries.\u003c/p\u003e\n\u003cp\u003eThis study generates disease expenditure estimates in all 38 OECD member countries in 2019, for 80 major NCDs by disease phase, sex, and age group – filling a critical information gap in global health data.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eHealth expenditure per person with disease by sex and age group was taken from a comprehensive Australian disease expenditure study and disaggregated by disease phase (first year of diagnosis, last year of life if dying of disease, otherwise prevalent) using Global Burden of Disease data and New Zealand estimates of relative expenditure ratios by phase. \u0026nbsp;These estimates were applied to case numbers in each OECD country and scaled to each country’s total health system expenditure to estimate total NCD expenditure in 2019 United States dollars by disease phase. Estimates were compared with pre-existing disease expenditure estimates for Norway, Switzerland, and the United States of America.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eAverage health expenditure on NCDs across OECD countries was US$207 million per 100,000 population. Pooled across countries, musculoskeletal disorders contributed to the highest proportion of total health expenditure (17.4%), followed by cancer and other neoplasms (9.4%), and CVD (9.1%). The highest proportion expenditure conditions for females were musculoskeletal disorders (56.1%), mental and substance use disorders (55.8%), and neurological conditions (54.8%). For males it was kidney and urinary diseases (63.8%), cancer and other neoplasms (58.3%), and cardiovascular diseases (50.7%). The first year of diagnosis represented on average 36.8% of total NCD expenditure, while last year of life expenditure attributable to disease causing death accounted for 2.6%.\u003c/p\u003e\n\u003cp\u003eSimilarities and differences were observed between our estimates and pre-existing country-specific estimates.\u003c/p\u003e\n\u003cp\u003eConclusions:\u003c/p\u003e\n\u003cp\u003eOur estimates represent a starting point for understanding the impact of NCDs on health system expenditure. We recommend evolving our paper’s methods to include multiple country-level studies as inputs – augmented by covariates (e.g. GDP, public/private split) to better predict disease expenditure.\u003c/p\u003e","manuscriptTitle":"Estimates of non-communicable disease expenditure by disease phase, sex, and age group for all OECD countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-04 21:41:08","doi":"10.21203/rs.3.rs-4798785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T21:46:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T14:19:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140463195945743552059331700046747694005","date":"2025-05-23T13:48:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T15:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165187402202708394928303168997723610994","date":"2025-04-03T12:20:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148072691686661986483965088773063614957","date":"2025-04-03T08:11:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T08:01:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-31T14:24:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-31T14:23:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Population Health Metrics","date":"2024-07-25T03:59:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"population-health-metrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pohm","sideBox":"Learn more about [Population Health Metrics](http://pophealthmetrics.biomedcentral.com/)","snPcode":"12963","submissionUrl":"https://submission.nature.com/new-submission/12963/3","title":"Population Health Metrics","twitterHandle":"@PHMjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4fadef25-b522-4100-a943-845546b215b6","owner":[],"postedDate":"September 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:09:29+00:00","versionOfRecord":{"articleIdentity":"rs-4798785","link":"https://doi.org/10.1186/s12963-025-00418-5","journal":{"identity":"population-health-metrics","isVorOnly":false,"title":"Population Health Metrics"},"publishedOn":"2025-10-08 15:58:06","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2024-09-04 21:41:08","video":"","vorDoi":"10.1186/s12963-025-00418-5","vorDoiUrl":"https://doi.org/10.1186/s12963-025-00418-5","workflowStages":[]},"version":"v1","identity":"rs-4798785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4798785","identity":"rs-4798785","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}