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Background Front-of-pack nutrition labelling (FoPL) of packaged foods can promote healthier diets. Australia and New Zealand (NZ) adopted the voluntary Health Star Rating (HSR) scheme in 2014. We studied the impact of voluntary adoption of HSR on food reformulation relative to unlabelled foods and examined differential impacts for more-versus-less healthy foods. Methods and findings Annual nutrition information panel data were collected for nonseasonal packaged foods sold in major supermarkets in Auckland from 2013 to 2019 and in Sydney from 2014 to 2018. The analysis sample covered 58,905 unique products over 14 major food groups. We used a difference-in-differences design to estimate reformulation associated with HSR adoption. Healthier products adopted HSR more than unhealthy products: >35% of products that achieved 4 or more stars displayed the label compared to <15% of products that achieved 2 stars or less. Products that adopted HSR were 6.5% and 10.7% more likely to increase their rating by ≥0.5 stars in Australia and NZ, respectively. Labelled products showed a −4.0% [95% confidence interval (CI): −6.4% to −1.7%, p = 0.001] relative decline in sodium content in NZ, and there was a −1.4% [95% CI: −2.7% to −0.0%, p = 0.045] sodium change in Australia. HSR adoption was associated with a −2.3% [−3.7% to −0.9%, p = 0.001] change in sugar content in NZ and a statistically insignificant −1.1% [−2.3% to 0.1%, p = 0.061] difference in Australia. Initially unhealthy products showed larger reformulation effects when adopting HSR than healthier products. No evidence of a change in protein or saturated fat content was observed. A limitation of our study is that results are not sales weighted. Thus, it is not able to assess changes in overall nutrient consumption that occur because of HSR-caused reformulation. Also, participation into labelling and reformulation is jointly determined by producers in this observational study, impacting its generalisability to settings with mandatory labelling. Conclusions In this study, we observed that reformulation changes following voluntary HSR labelling are small, but greater for initially unhealthy products. Initially unhealthy foods were, however, less likely to adopt HSR. Our results, therefore, suggest that mandatory labelling has the greatest potential for improving the healthiness of packaged foods.

BackgroundWe compared the health and economic consequences for the State of Victoria, Australia, of four COVID-19 strategies: aggressive and moderate elimination, tight suppression (aiming for 1 to 5 cases per million per day) and loose suppression (5 to 25 cases per million per day). The strategies shifted up and down through five levels of policy stringency based on the number of cases per day, for one year.MethodsAn agent-based model (ABM) generated 100 runs of daily SARS-CoV-2 case numbers, that then fed into a proportional multistate lifetable to estimate health adjusted life years (HALYs) and costs. We used a net monetary benefit approach to estimate the optimal strategy.FindingsAggressive elimination resulted in the highest percentage of days with the lowest level of restrictions (median 31.7%, 90% simulation interval 6.6% to 64.4%). However, days in hard lockdown were similar across all four strategies (medians 27.5% to 36.1%).HALY losses (compared to a no-COVID-19 scenario) were similar for moderate elimination (286, 219 to 389) and moderate elimination (314, 228 to 413), and nearly eight and 40-times higher for tight and loose suppression. The median GDP loss was least for moderate elimination ($US41.7 billion, $29.0 to $63.6 billion), but there was substantial overlap in simulation intervals between the four strategies.From a health system perspective aggressive elimination was optimal in 64% of simulations above a willingness to pay of $15,000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a partial societal perspective in half the simulations followed by aggressive elimination in a quarter.Shortening the pandemic duration to 6 months saw loose suppression become preferable under a partial societal perspective.InterpretationFor this single high-income jurisdiction, elimination strategies were preferable over a 1-year pandemic duration.FundingAnonymous philanthropic donation to the University of Melbourne.Research in contextEvidence before this studyThere have been varying approaches across countries and jurisdictions as to how to manage the COVID-19 pandemic, ranging from elimination of community transmission (e.g. Australasia, Taiwan and other East Asian and Pacific Island countries) to loose suppression or mitigation that attempts to keep the case numbers within health services capacity (e.g. Sweden, USA, India, UK and some continental European countries). The best or optimal approach is unknown, and involves an invidious balancing of health, social and economic consequences of the pandemic. But it has become apparent that in high income countries using loose suppression that one still has to use lock-downs from time to time to keep case numbers within health service capacity, raising the question as to what is the best for the economy – attempting elimination, tight suppression or loose suppression?One approach to integrating the health and economic consequences is cost effectiveness analysis, but to date such approaches have mainly been focused on SARS-CoV-2 treatments rather than societal intervention, and have not incorporated a counterfactual approach to compare the same jurisdiction across the many (stochastically varying) realizations for different policy options.Added value of this studyThis study uses one high-income jurisdiction, the state of Victoria in Australia as it exited its second wave, to estimate the health and economic consequences of four policy options: aggressive and moderate elimination strategies, and tight and loose suppression strategies. The modeling is done in two steps: first, an agent-based model to simulate 100 possible trajectories of daily SARS-CoV-2 infections over one year for each of the four policy options; and second, an integrated epidemiological and economic model that estimates health and economic costs. Whilst there is considerable uncertainty in outcomes for all of the four policy options, the two elimination options are usually optimal from both a health system and a partial societal (health expenditure plus GDP cost) perspective. However, if the remaining duration of the pandemic is lessened from 1 year to half a year (as may be the case with vaccine roll-outs), loose suppression becomes more favorable – suggesting countries with already high infection rates ‘ride it out’ till vaccination coverage is adequate.

This economic evaluation determines the optimal policy response to the COVID-19 pandemic in Victoria, Australia, using a net monetary benefit approach for policies ranging from aggressive elimination and moderate elimination to tight suppression and loose suppression.

Background Reducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). Methods and findings We used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to “healthy” person time. Conclusions In this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.