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.

Cost-effectiveness of universal iron supplementation and iron-containing micronutrient powders for anemia among young children in rural Bangladesh: analysis of a randomized, placebo-controlled trial.

BACKGROUND: Universal provision of iron supplements or iron-containing multiple micronutrient powders (MNPs) is widely used to prevent anemia in young children in low- and middle-income countries. The BRISC (Benefits and Risks of Iron Interventions in Children) trial compared iron supplements and MNPs with placebo in children <2 y old in rural Bangladesh. OBJECTIVES: We aimed to assess the cost-effectiveness of iron supplements or iron-containing MNPs among young children in rural Bangladesh. METHODS: We did a cost-effectiveness analysis of MNPs and iron supplements using the BRISC trial outcomes and resource use data, and programmatic data from the literature. Health care costs were assessed from a health system perspective. We calculated incremental cost-effectiveness ratios (ICERs) in terms of US$ per disability-adjusted life-year (DALY) averted. To explore uncertainty, we constructed cost-effectiveness acceptability curves using bootstrapped data over a range of cost-effectiveness thresholds. One- and 2-way sensitivity analyses tested the impact of varying key parameter values on our results. RESULTS: Provision of MNPs was estimated to avert 0.0031 (95% CI: 0.0022, 0.0041) DALYs/child, whereas iron supplements averted 0.0039 (95% CI: 0.0030, 0.0048) DALYs/child, over 1 y compared with no intervention. Incremental mean costs were $0.75 (95% CI: 0.73, 0.77) for MNPs compared with no intervention and $0.64 ($0.62, $0.67) for iron supplements compared with no intervention. Iron supplementation dominated MNPs because it was cheaper and averted more DALYs. Iron supplementation had an ICER of $1645 ($1333, $2153) per DALY averted compared with no intervention, and had a 0% probability of being the optimal strategy at cost-effectiveness thresholds of $200 (reflecting health opportunity costs in Bangladesh) and $985 [half of gross domestic product (GDP) per capita] per DALY averted. Scenario and sensitivity analyses supported the base case findings. CONCLUSIONS: These findings do not support universal iron supplementation or micronutrient powders as a cost-effective intervention for young children in rural Bangladesh. This trial was registered at anzctr.org.au as ACTRN1261700066038 and trialsearch.who.int as U1111-1196-1125.

Association of Simulated COVID-19 Policy Responses for Social Restrictions and Lockdowns With Health-Adjusted Life-Years and Costs in Victoria, Australia.

Importance: Countries have varied enormously in how they have responded to the COVID-19 pandemic, ranging from elimination strategies (eg, Australia, New Zealand, Taiwan) to tight suppression (not aiming for elimination but rather to keep infection rates low [eg, South Korea]) to loose suppression (eg, Europe, United States) to virtually unmitigated (eg, Brazil, India). Weighing the best option, based on health and economic consequences due to lockdowns, is necessary. Objective: To determine the optimal policy response, using a net monetary benefit (NMB) approach, for policies ranging from aggressive elimination and moderate elimination to tight suppression (aiming for 1-5 cases per million per day) and loose suppression (5-25 cases per million per day). Design Setting and Participants: Using governmental data from the state of Victoria, Australia, and other collected data, 2 simulation models in series were conducted of all residents (population, 6.4 million) for SARS-CoV-2 infections for 1 year from September 1, 2020. An agent-based model (ABM) was used to estimate daily SARS-CoV-2 infection rates and time in 5 stages of social restrictions (stages 1, 1b, 2, 3, and 4) for 4 policy response settings (aggressive elimination, moderate elimination, tight suppression, and loose suppression), and a proportional multistate life table (PMSLT) model was used to estimate health-adjusted life-years (HALYs) associated with COVID-19 and costs (health systems and health system plus gross domestic product [GDP]). The ABM is a generic COVID-19 model of 2500 agents, or simulants, that was scaled up to the population of interest. Models were specified with data from 2019 (eg, epidemiological data in the PMSLT model) and 2020 (eg, epidemiological and cost consequences of COVID-19). The NMB of each policy option at varying willingness to pay (WTP) per HALY was calculated: NMB = HALYs × WTP - cost. The estimated most cost-effective (optimal) policy response was that with the highest NMB. Main Outcome and Measures: Estimated SARS-CoV-2 infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses. Results: In 100 runs of both the ABM and PMSLT models for each of the 4 policy responses, 31.0% of SARS-CoV-2 infections, 56.5% of hospitalizations, and 84.6% of deaths occurred among those 60 years and older. Aggressive elimination was associated with the highest percentage of days with the lowest level of restrictions (median, 31.7%; 90% simulation interval [SI], 6.6%-64.4%). However, days in hard lockdown were similar across all 4 strategies. The HALY losses (compared with a scenario without COVID-19) were similar for aggressive elimination (median, 286 HALYs; 90% SI, 219-389 HALYs) and moderate elimination (median, 314 HALYs; 90% SI, 228-413 HALYs), and nearly 8 and 40 times higher for tight suppression and loose suppression, respectively. The median GDP loss was least for moderate elimination (median, $41.7 billion; 90% SI, $29.0-$63.6 billion), but there was substantial overlap in simulation intervals between the 4 strategies. From a health system perspective, aggressive elimination was optimal in 64% of simulations above a WTP of $15 000 per HALY, followed by moderate elimination in 35% of simulations. Moderate elimination was optimal from a GDP perspective in half of the simulations, followed by aggressive elimination in a quarter. Conclusions and Relevance: In this simulation modeling economic evaluation of estimated SARS-CoV-infection rates, time under 5 stages of restrictions, HALYs, health expenditure, and GDP losses in Victoria, Australia, an elimination strategy was associated with the least health losses and usually the fewest GDP losses.