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BACKGROUND: The COVID-19 pandemic has strained health system capacity worldwide due to a surge of hospital admissions, while mitigation measures have simultaneously reduced patients' access to health care, affecting the diagnosis and treatment of other diseases such as cancer. We estimated the impact of delayed diagnosis on cancer outcomes in Chile using a novel modelling approach to inform policies and planning to mitigate the forthcoming cancer-related health impacts of the pandemic in Chile. METHODS: We developed a microsimulation model of five cancers in Chile (breast, cervix, colorectal, prostate, and stomach) for which reliable data were available, which simulates cancer incidence and progression in a nationally representative virtual population, as well as stage-specific cancer detection and survival probabilities. We calibrated the model to empirical data on monthly detected cases, as well as stage at diagnosis and 5-year net survival. We accounted for the impact of COVID-19 on excess mortality and cancer detection by month during the pandemic, and projected diagnosed cancer cases and outcomes of stage at diagnosis and survival up to 2030. For comparison, we simulated a no COVID-19 scenario in which the impacts of COVID-19 on excess mortality and cancer detection were removed. FINDINGS: Our modelling showed a sharp decrease in the number of diagnosed cancer cases during the COVID-19 pandemic, with a large projected short-term increase in future diagnosed cases. Due to the projected backlog in diagnosis, we estimated that in 2021 there will be an extra 3198 cases (95% uncertainty interval [UI] 1356-5017) diagnosed among the five modelled cancers, an increase of nearly 14% compared with the no COVID-19 scenario, falling to a projected 10% increase in 2022 with 2674 extra cases (1318-4032) diagnosed. As a result of delayed diagnosis, we found a worse stage distribution for detected cancers in 2020-22, which is estimated to lead to 3542 excess cancer deaths (95% UI 2236-4816) in 2022-30, compared with the no COVID-19 scenario, among the five modelled cancers, most of which (3299 deaths, 2151-4431) are projected to occur before 2025. INTERPRETATION: In addition to a large projected surge in diagnosed cancer cases, we found that delays in diagnosis will result in worse cancer stage at presentation, leading to worse survival outcomes. These findings can help to inform surge capacity planning and highlight the importance of ensuring appropriate health system capacity levels to detect and care for the increased cancer cases in the coming years, while maintaining the timeliness and quality of cancer care. Potential delays in treatment and adverse impacts on quality of care, which were not considered in this model, are likely to contribute to even more excess deaths from cancer than projected. FUNDING: Harvard TH Chan School of Public Health. TRANSLATIONS: For the Spanish and Portuguese translations of the abstract see Supplementary Materials section.

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An excise tax of 1 peso per liter on sugar-sweetened beverages was implemented in Mexico in 2014. We estimated the cost-effectiveness of this tax and an alternative tax scenario of 2 pesos per liter. We developed a cohort simulation model calibrated for Mexico to project the impact of the tax over ten years. The current tax is projected to prevent 239,900 cases of obesity, 39 percent of which would be among children. It could also prevent 61,340 cases of diabetes, lead to gains of 55,300 quality-adjusted life-years, and avert 5,840 disability-adjusted life-years. The tax is estimated to save $3.98 per dollar spent on its implementation. Doubling the tax to 2 pesos per liter would nearly double the cost savings and health impact. Countries with comparable conditions could benefit from implementing a similar tax.

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BACKGROUND: Evidence on immunization costs is a critical input for cost-effectiveness analysis and budgeting, and can describe variation in site-level efficiency. The Expanded Program on Immunization Costing and Financing (EPIC) Project represents the largest investigation of immunization delivery costs, collecting empirical data on routine infant immunization in Benin, Ghana, Honduras, Moldova, Uganda, and Zambia. METHODS: We developed a pooled dataset from individual EPIC country studies (316 sites). We regressed log total costs against explanatory variables describing service volume, quality, access, other site characteristics, and income level. We used Bayesian hierarchical regression models to combine data from different countries and account for the multi-stage sample design. We calculated output elasticity as the percentage increase in outputs (service volume) for a 1% increase in inputs (total costs), averaged across the sample in each country, and reported first differences to describe the impact of other predictors. We estimated average and total cost curves for each country as a function of service volume. RESULTS: Across countries, average costs per dose ranged from $2.75 to $13.63. Average costs per child receiving diphtheria, tetanus, and pertussis ranged from $27 to $139. Within countries costs per dose varied widely-on average, sites in the highest quintile were 440% more expensive than those in the lowest quintile. In each country, higher service volume was strongly associated with lower average costs. A doubling of service volume was associated with a 19% (95% interval, 4.0-32) reduction in costs per dose delivered, (range 13% to 32% across countries), and the largest 20% of sites in each country realized costs per dose that were on average 61% lower than those for the smallest 20% of sites, controlling for other factors. Other factors associated with higher costs included hospital status, provision of outreach services, share of effort to management, level of staff training/seniority, distance to vaccine collection, additional days open per week, greater vaccination schedule completion, and per capita gross domestic product. CONCLUSIONS: We identified multiple features of sites and their operating environment that were associated with differences in average unit costs, with service volume being the most influential. These findings can inform efforts to improve the efficiency of service delivery and better understand resource needs.

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