Health gains from achieving optimal body mass index in Australia: a simulation study.

Background: We estimated the health gains and health inequality impacts for the Australian population alive in 2021 (n = 25.0 million) in the next 20 years and over their remaining lifespan, from shifting everyone above a BMI of 25 kg/m2 to 25 kg/m2 compared to the BMI distribution in 2021 persisting into the future. Methods: National Health Survey 2017-2018 was used to estimate BMI distributions by sex, age and, socio-economic status (Socio-Economic Indexes for Areas; SEIFA). A proportional multistate life table linking BMI to 19 associated diseases and allowing for time lags and competing morbidity and mortality, was used to estimate the future stream of health adjusted life years (HALYs) gained from eradicating high BMI. Findings: Undiscounted health gains in the first 20 years and lifetime of the population were, respectively, 2.00 million (95% uncertainty interval 1.70-2.32) and 20.4 million (17.0-24.2) (at a 3% annual discount rate, HALY gains were 1.37 and 5.77 million, respectively). Reductions in the incidence of cardio metabolic diseases contributed 61% (95% UI: 54%-68%) of the undiscounted health gains in the first 20 years, musculoskeletal diseases contributed 26% (20%-32%) and cancer 5% (3%-8%). HALY gains in the first 20 years and lifetime, per person alive in 2021, were 2.5 (2.4-2.5) and 1.9 (1.9-2.0) times higher for the most compared to the least deprived SEIFA quintile. Interpretation: The total theoretical envelope of health gains, and health inequality reductions, through eradication of BMI is substantial. Our modeling infrastructure can be used to estimate the health impacts and cost effectiveness of many actual interventions. Funding: No funding was received for the study.

Economic effects for citizens and the government of a country-level tobacco endgame strategy: a modelling study.

BACKGROUND: Aotearoa-New Zealand (A/NZ) was the first country to pass a comprehensive commercial tobacco endgame strategy into law. Key components include the denicotinisation of smoked tobacco products and a major reduction in tobacco retail outlets. Understanding the potential long-term economic impacts of such measures is important for government planning. DESIGN: A tobacco policy simulation model that evaluated the health impacts of the A/NZ Smokefree Action Plan was extended to evaluate the economic effects from both government and citizen perspectives. Estimates were presented in 2021 US$, discounted at 3% per annum. RESULTS: The modelled endgame policy package generates considerable growth in income for the A/NZ population with a total cumulative gain of US$31 billion by 2050. From a government perspective, increased superannuation payments and reduced tobacco excise tax revenue result in a negative net financial position and a cumulative shortfall of US$11.5 billion by 2050. In a sensitivity analysis considering future labour force changes, the government's cumulative net position remained negative by 2050, but only by US$1.9 billion. CONCLUSIONS: A policy such as the A/NZ Smokefree Action Plan is likely to produce substantial economic benefits for citizens, and modest impacts on government finances related to reduced tobacco tax and increases in aged pensions due to increased life expectancy. Such costs can be anticipated and planned for and might be largely offset by future increases in the size of the labour force and the proportion of people 65+ years old working in the formal economy.

The total health gains and cost savings of eradicating cold housing in Australia.

BACKGROUND: Cold indoor temperature (<18 °C) is associated with hypertension-related and respiratory disease, depression, and anxiety. We estimate total health, health expenditure and income impacts of permanently lifting the temperature in living areas of the home to 18 °C in cold homes in South-eastern Australia (N = 17 million). METHODS: A proportional multistate lifetable model was used to estimate health adjusted life years (HALYs), health expenditure and income earnings, over the remainder of the lifespan of the population alive in 2021 (3% discount rate). Multiple data were integrated including the prevalence of cold housing (5.87%; mean temperature 15 °C), the effect of temperature to hypertension-related, respiratory disease, depression and anxiety. FINDINGS: Eradicating cold housing was predicted to lead to 89,600 (95% UI 47,700 to 177,000) lifetime HALYs gained over the population's remaining lifespan, nearly half of which occurred from 2021 to 2040. Respiratory disease (32.4%) and mental illness (60.6%) made large contributions to HALYs gained, but also had large uncertainty (95% UI 30.0%-42.9% and 45.1%-64.6%, respectively) due to uncertain estimates of their magnitude of causal association with cold housing. Health gains per capita were 6.1 times greater (95% UI 4.7 to 8.1) among the most compared to least deprived quintile. From 2021 to 2040, health expenditure decreased by AUD$0.87 billion (0.35-1.98) and income earnings increased by AUD$4.35 billion (1.89-9.81). INTERPRETATION: Eliminating cold housing would lead to substantial health gains, reductions in health inequalities, savings in health expenditure, and productivity gains. Next steps require research to reduce uncertainty about the magnitude of causal associations of cold with mental and respiratory health.

Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high-income countries using street images.

Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00394-6.

Fifty-year forecasts of daily smoking prevalence: can Australia reach 5% by 2030?

OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030. METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed. RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066. CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.

The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study.

BACKGROUND: Long COVID symptoms occur for a proportion of acute COVID-19 survivors, with reduced risk among the vaccinated and for Omicron compared with Delta variant infections. The health loss attributed to pre-Omicron long COVID has previously been estimated using only a few major symptoms. METHODS: The years lived with disability (YLDs) due to long COVID in Australia during the 2021-22 Omicron BA.1/BA.2 wave were calculated using inputs from previously published case-control, cross-sectional or cohort studies examining the prevalence and duration of individual long COVID symptoms. This estimated health loss was compared with acute SARS-CoV-2 infection YLDs and years of life lost (YLLs) from SARS-CoV-2. The sum of these three components equals COVID-19 disability-adjusted life years (DALYs); this was compared with DALYs from other diseases. RESULTS: A total of 5200 [95% uncertainty interval (UI) 2200-8300] YLDs were attributable to long COVID and 1800 (95% UI 1100-2600) to acute SARS-CoV-2 infection, suggesting long COVID caused 74% of the overall YLDs from SARS-CoV-2 infections in the BA.1/BA.2 wave. Total DALYs attributable to SARS-CoV-2 were 50 900 (95% UI 21 000-80 900), 2.4% of expected DALYs for all diseases in the same period. CONCLUSION: This study provides a comprehensive approach to estimating the morbidity due to long COVID. Improved data on long COVID symptoms will improve the accuracy of these estimates. As data accumulate on SARS-CoV-2 infection sequelae (e.g. increased cardiovascular disease rates), total health loss is likely to be higher than estimated in this study. Nevertheless, this study demonstrates that long COVID requires consideration in pandemic policy planning, given it is responsible for the majority of direct SARS-CoV-2 morbidity, including during an Omicron wave in a highly vaccinated population.

Epidemiologic and economic modelling of optimal COVID-19 policy: public health and social measures, masks and vaccines in Victoria, Australia.

Background: Identifying optimal COVID-19 policies is challenging. For Victoria, Australia (6.6 million people), we evaluated 104 policy packages (two levels of stringency of public health and social measures [PHSMs], by two levels each of mask-wearing and respirator provision during large outbreaks, by 13 vaccination schedules) for nine future SARS-CoV-2 variant scenarios. Methods: We used an agent-based model to estimate morbidity, mortality, and costs over 12 months from October 2022 for each scenario. The 104 policies (each averaged over the nine future variant scenarios) were ranked based on four evenly weighted criteria: cost-effectiveness from (a) health system only and (b) health system plus GDP perspectives, (c) deaths and (d) days exceeding hospital occupancy thresholds. Findings: More compared to less stringent PHSMs reduced cumulative infections, hospitalisations and deaths but also increased time in stage ≥3 PHSMs. Any further vaccination from October 2022 decreased hospitalisations and deaths by 12% and 27% respectively compared to no further vaccination and was usually a cost-saving intervention from a health expenditure plus GDP perspective. High versus low vaccine coverage decreased deaths by 15% and reduced time in stage ≥3 PHSMs by 20%. The modelled mask policies had modest impacts on morbidity, mortality, and health system pressure. The highest-ranking policy combination was more stringent PHSMs, two further vaccine doses (an Omicron-targeted vaccine followed by a multivalent vaccine) for ≥30-year-olds with high uptake, and promotion of increased mask wearing (but not Government provision of respirators). Interpretation: Ongoing vaccination and PHSMs continue to be key components of the COVID-19 pandemic response. Integrated epidemiologic and economic modelling, as exemplified in this paper, can be rapidly updated and used in pandemic decision making. Funding: Anonymous donation, University of Melbourne funding.

Tobacco endgame intervention impacts on health gains and Maori:non-Maori health inequity: a simulation study of the Aotearoa/New Zealand Tobacco Action Plan.

BACKGROUND: The Aotearoa/New Zealand Government is aiming to end the tobacco epidemic and markedly reduce Maori:non-Maori health inequalities by legislating: (1) denicotinisation of retail tobacco, (2) 95% reduction in retail outlets and (c) a tobacco free-generation whereby people born after 2005 are unable to legally purchase tobacco. This paper estimates future smoking prevalence, mortality inequality and health-adjusted life year (HALY) impacts of these strategies. METHODS: We used a Markov model to estimate future yearly smoking and vaping prevalence, linked to a proportional multistate life table model to estimate future mortality and HALYs. RESULTS: The combined package of strategies (plus media promotion) reduced adult smoking prevalence from 31.8% in 2022 to 7.3% in 2025 for Maori, and 11.8% to 2.7% for non-Maori. The 5% smoking prevalence target was forecast to be achieved in 2026 and 2027 for Maori males and females, respectively.The HALY gains for the combined package over the population's remaining lifespan were estimated to be 594 000 (95% uncertainty interval (UI): 443 000 to 738 000; 3% discount rate). Denicotinisation alone achieved 97% of these HALYs, the retail strategy 19% and tobacco-free generation 12%.By 2040, the combined package was forcat to reduce the gap in Maori:non-Maori all-cause mortality rates for people 45+ years old by 22.9% (95% UI: 19.9% to 26.2%) for females and 9.6% (8.4% to 11.0%) for males. CONCLUSION: A tobacco endgame strategy, especially denicotinisation, could deliver large health benefits and dramatically reduce health inequities between Maori and non-Maori in Aotearoa/New Zealand.

Prioritization of intervention domains to prevent cardiovascular disease: a country-level case study using global burden of disease and local data.

AIM: We aimed to combine Global Burden of Disease (GBD) Study data and local data to identify the highest priority intervention domains for preventing cardiovascular disease (CVD) in the case study country of Aotearoa New Zealand (NZ). METHODS: Risk factor data for CVD in NZ were extracted from the GBD using the "GBD Results Tool." We prioritized risk factor domains based on consideration of the size of the health burden (disability-adjusted life years [DALYs]) and then by the domain-specific interventions that delivered the highest health gains and cost-savings. RESULTS: Based on the size of the CVD health burden in DALYs, the five top prioritized risk factor domains were: high systolic blood pressure (84,800 DALYs; 5400 deaths in 2019), then dietary risk factors, then high LDL cholesterol, then high BMI and then tobacco (30,400 DALYs; 1400 deaths). But if policy-makers aimed to maximize health gain and cost-savings from specific interventions that have been studied, then they would favor the dietary risk domain (e.g., a combined fruit and vegetable subsidy plus a sugar tax produced estimated lifetime savings of 894,000 health-adjusted life years and health system cost-savings of US$11.0 billion; both 3% discount rate). Other potential considerations for prioritization included the potential for total health gain that includes non-CVD health loss and potential for achieving relatively greater per capita health gain for Maori (Indigenous) to reduce health inequities. CONCLUSIONS: We were able to show how CVD risk factor domains could be systematically prioritized using a mix of GBD and country-level data. Addressing high systolic blood pressure would be the top ranked domain if policy-makers focused just on the size of the health loss. But if policy-makers wished to maximize health gain and cost-savings using evaluated interventions, dietary interventions would be prioritized, e.g., food taxes and subsidies.

Income and economic productivity loss associated with comorbidity: longitudinal analysis of linked individual-level data for a whole country.

OBJECTIVE: To estimate the income loss from having two or more diseases, over and above the independent and separate effects of having a single disease. METHODS: We used linked health income data from 2006-2007 to 2015-2016 for 25-64 years, for the entire New Zealand population. Fixed effects OLS regression was used to estimate within-individual income loss for diseases separately, and to estimate if having two or more diseases together resulted in reduced (subadditive) or additional (superadditive) income impacts (relative to adding together the income impacts for each disease when experienced singly). RESULTS: Of the 169 comorbidity pairs for both sexes, 28 (17%) had a statistically significant superadditive (n=14) or subadditive (n=14) effect of having two diseases. The combined total income gain from deleting all diseases and comorbidities was US$2.269 billion (95% CI US$$2.125 to US$2.389 billion), or a 3.61% (95% CI 3.38% to 3.80%) increase in income. Of this, 8.8% or US$200 million (95% CI US$193 to US$207 million) was attributable to a tendency for comorbidity interactions to increase income loss more than expected for common disease pairings. CONCLUSIONS: This national longitudinal study found that disease is associated with income loss, but most of this impact is due to the distinct and independent impact of separate diseases. Nevertheless, there was a tendency for two or more diseases to disproportionately increase income loss more than the summed impacts of each of these diseases if experienced singly.

Disease-Related Loss to Government Funding: Longitudinal Analysis of Individual-Level Health and Tax Data for an Entire Country.

OBJECTIVES: The objective of this longitudinal analysis was to estimate funding loss in terms of tax revenue to the New Zealand (NZ) government from disease and injury among working age adults. METHODS: Linked national health and tax data sets of the usually resident population between 2006 and 2016 were used to model 40 disease states simultaneously in a fixed-effects regression analysis to estimate population-level tax loss from disease and injury. To estimate tax revenue loss to the NZ government, we modeled a counterfactual scenario where all disease/injury was cause deleted. RESULTS: The estimated tax paid by all 25- to 64-year-olds in the eligible NZ population was $15 773 million (m) per annum (US dollar 2021), or $16 446 m for a counterfactual as though no one had any disease disease-related income loss (a 4.3% or $672.9 m increase in tax revenue per annum). The disease that-if it had no impact on income-generated the greatest impact was mental illness, contributing 34.7% ($233.3 m) of all disease-related tax loss, followed by cardiovascular (14.7%, $99.0 m) and endocrine (10.2%, $68.8 m). Tax revenue gains after deleting all disease/injury increased up to 65 years of age, with the largest contributor occurring among 60- to 64-year-olds ($131.7 m). Varied results were also observed among different ethnicities and differing levels of deprivation. CONCLUSIONS: This study finds considerable variation by disease on worker productivity and therefore tax revenue in this high-income country. These findings strengthen the economic and government case for prevention, particularly the prevention of mental health conditions and cardiovascular disease.

Comparing health gains, costs and cost-effectiveness of 100s of interventions in Australia and New Zealand: an online interactive league table.

BACKGROUND: This study compares the health gains, costs, and cost-effectiveness of hundreds of Australian and New Zealand (NZ) health interventions conducted with comparable methods in an online interactive league table designed to inform policy. METHODS: A literature review was conducted to identify peer-reviewed evaluations (2010 to 2018) arising from the Australia Cost-Effectiveness research and NZ Burden of Disease Epidemiology, Equity and Cost-Effectiveness Programmes, or using similar methodology, with: health gains quantified as health-adjusted life years (HALYs); net health system costs and/or incremental cost-effectiveness ratio; time horizon of at least 10 years; and 3% to 5% discount rates. RESULTS: We identified 384 evaluations that met the inclusion criteria, covering 14 intervention domains: alcohol; cancer; cannabis; communicable disease; cardiovascular disease; diabetes; diet; injury; mental illness; other non-communicable diseases; overweight and obesity; physical inactivity; salt; and tobacco. There were large variations in health gain across evaluations: 33.9% gained less than 0.1 HALYs per 1000 people in the total population over the remainder of their lifespan, through to 13.0% gaining > 10 HALYs per 1000 people. Over a third (38.8%) of evaluations were cost-saving. CONCLUSIONS: League tables of comparably conducted evaluations illustrate the large health gain (and cost) variations per capita between interventions, in addition to cost-effectiveness. Further work can test the utility of this league table with policy-makers and researchers.