Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts.

Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20-208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.

Modelling the impact of relaxing COVID-19 control measures during a period of low viral transmission.

OBJECTIVES: To assess the risks associated with relaxing coronavirus disease 2019 (COVID-19)-related physical distancing restrictions and lockdown policies during a period of low viral transmission. DESIGN: Network-based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent-based model, Covasim. SETTING: The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March-May 2020, a period of low community viral transmission. INTERVENTION: Policy changes for easing COVID-19-related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine. MAIN OUTCOME MEASURE: Increase in detected COVID-19 cases following relaxation of restrictions. RESULTS: Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID-19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation. CONCLUSIONS: Removing several COVID-19-related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re-opening of social venues.

Ending malnutrition in all its forms requires scaling up proven nutrition interventions and much more: a 129-country analysis.

BACKGROUND: Sustainable Development Goal (SDG) 2.2 calls for an end to all forms of malnutrition, with 2025 targets of a 40% reduction in stunting (relative to 2012), for wasting to occur in less than 5% of children, and for a 50% reduction in anaemia in women (15-49 years). We assessed the likelihood of countries reaching these targets by scaling up proven interventions and identified priority interventions, based on cost-effectiveness. METHODS: For 129 countries, the Optima Nutrition model was used to compare 2019-2030 nutrition outcomes between a status quo (maintained intervention coverage) scenario and a scenario where outcome-specific interventions were scaled up to 95% coverage over 5 years. The average cost-effectiveness of each intervention was calculated as it was added to an expanding package of interventions. RESULTS: Of the 129 countries modelled, 46 (36%), 66 (51%) and 0 (0%) were on track to achieve the stunting, wasting and anaemia targets respectively. Scaling up 18 nutrition interventions increased the number of countries reaching the SDG 2.2 targets to 50 (39%), 83 (64%) and 7 (5%) respectively. Intermittent preventative treatment of malaria during pregnancy (IPTp), infant and young child feeding education, vitamin A supplementation and lipid-based nutrition supplements for children produced 88% of the total impact on stunting, with average costs per case averted of US$103, US$267, US$556 and US$1795 when interventions were consecutively scaled up, respectively. Vitamin A supplementation and cash transfers produced 100% of the total global impact on prevention of wasting, with average costs per case averted of US$1989 and US$19,427, respectively. IPTp, iron and folic acid supplementation for non-pregnant women, and multiple micronutrient supplementation for pregnant women produced 85% of the total impact on anaemia prevalence, with average costs per case averted of US$9, US$35 and US$47, respectively. CONCLUSIONS: Prioritising nutrition investment to the most cost-effective interventions within the country context can maximise the impact of funding. A greater focus on complementing nutrition-specific interventions with nutrition-sensitive ones that address the social determinants of health is critical to reach the SDG targets.

Hepatitis C virus notification rates in Australia are highest in socioeconomically disadvantaged areas.

BACKGROUND: Poor access to health services is a significant barrier to achieving the World Health Organization's hepatitis C virus (HCV) elimination targets. We demonstrate how geospatial analysis can be performed with commonly available data to identify areas with the greatest unmet demand for HCV services. METHODS: We performed an Australia-wide cross-sectional analysis of 2015 HCV notification rates using local government areas (LGAs) as our unit of analysis. A zero-inflated negative binomial regression was used to determine associations between notification rates and socioeconomic/demographic factors, health service and geographic remoteness area (RA) classification variables. Additionally, component scores were extracted from a principal component analysis (PCA) of the healthcare service variables to provide rankings of relative service coverage and unmet demand across Australia. RESULTS: Among LGAs with non-zero notifications, higher rates were associated with areas that had increased socioeconomic disadvantage, more needle and syringe services (incidence rate ratio [IRR] 1.022; 95%CI 1.001, 1.044) and more alcohol and other drug services (IRR 1.019; 1.005, 1.034). The distribution of PCA component scores indicated that per-capita healthcare service coverage was lower in areas outside of major Australian cities. Areas outside of major cities also contained 94% of LGAs in the lowest two socioeconomic quintiles, as well as 35% of HCV notifications despite only representing 29% of the population. CONCLUSIONS: As countries aim for HCV elimination, routinely collected data can be used to identify geographical areas for priority service delivery. In Australia, the unmet demand for HCV treatment services is greatest in socioeconomically disadvantaged and non-metropolitan areas.

Heterogeneity in hepatitis C treatment prescribing and uptake in Australia: a geospatial analysis of a year of unrestricted treatment access.

BACKGROUND AND AIM: Direct-acting antiviral (DAA) treatments became available for all people living with hepatitis C virus (HCV) in Australia in March 2016. We assess variations in treatment rates and prescribing patterns across Australia's 338 Statistical Area 3 (SA3) geographical units. METHODS: Geocoded DAA treatment initiation data were analysed for the period 1 March 2016 to 30 June 2017. Regression models tested associations between the population demographics and healthcare service coverage of geographical areas and (a) their treatment rates; and (b) the proportion of prescriptions written by specialists compared to non-specialists. RESULTS: Across the 320 areas (95%) recording treatments, a median 76 (interquartile range [IQR] 35-207, range 4-3834) per 100,000 were initiated, corresponding to an estimated median 7.9% (IQR 2.9-23.6%, range 0-100%) treatment uptake. Major cities, areas of socioeconomic advantage and areas with lower proportions of the population born overseas had the highest per capita treatment rates. Non-specialists prescribed 46% (20,323/44,382) of treatment initiations. Prescriptions were written by non-specialists only in 163 areas (51%), while in other areas a median 40.0% (IQR 21.8-62.5%) of prescriptions were written by non-specialists. Non-specialist prescribing was higher in regional areas, as well as areas that had greater proportions of Indigenous Australians. CONCLUSIONS: High national-level treatment uptake of 20% in Australia masks underlying health system limitations; more than half of geographical areas may have treated less than 8% of people living with HCV. Areas of socioeconomic disadvantage and areas with a higher proportion of the population born overseas may need targeting with interventions to improve treatment uptake.