Healthcare service disruption in 14 Latin American and Caribbean countries during the COVID-19 pandemic: Analysis of household phone surveys, 2020-2021.

Background: The coronavirus 2019 (COVID-19 pandemic) and associated responses have significantly disrupted healthcare. We aimed to estimate the magnitude of and reasons for households reporting healthcare disruption in 14 Latin America and the Caribbean (LAC) region countries from mid-2020 to mid-2021, and its relationship with country contextual factors. Methods: We used COVID-19 high-frequency phone surveys (HFPS) conducted in 14 LAC countries in three rounds in 2020 and one in 2021. We classified the reasons reported for healthcare disruption into four groups: concerns about contracting COVID-19, healthcare supply constraints, financial reasons, and public health measures (PHMs). We used bivariate and multivariate regressions to examine correlates of reported healthcare disruption with the above groups and country context as control variables. Results: On average, 20% of households reported a disruption in May-June 2020 (45% to 10% at country level), dropping to 9% in June-July 2020 (31% to 3%) and July-August 2020 (26% to 3%), and declining to 3% in May-July 2021 (11% to 1%). The most common reason reported for disruption was healthcare supply constraints, followed by concerns about contracting COVID-19, PHM, and financial reasons. In multivariable regression analyses, we found that a higher incidence of new COVID-19 cases (regression coefficient (ß) = 0.018, P < 0.01), stricter PHM (ß = 0.002, P < 0.01), fewer hospital beds per population (ß = -0.011, P < 0.01), and lower out-of-pocket health spending (ß = -0.0008, P < 0.01) were associated with higher levels of disrupted care. A higher care disruption was associated with a lower gross domestic product (GDP) per person (ß = -0.00001, P < 0.01) and lower population density (ß = -0.056, P < 0.01). Conclusions: Healthcare services for households in LAC were substantially disrupted during the COVID-19 pandemic. Findings about supply and financial constraints can inform the recovery of postponed healthcare services, while public health and contextual factors findings can inform future health system resilience efforts in LAC and elsewhere.

Correction to: Optima nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions.

It has been highlighted that the original manuscript [1] contains a typesetting error in the name of Meera Shekar. This had been incorrectly captured as Meera Shekhar in the original article which has since been updated.

Optima Nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions.

BACKGROUND: Child stunting due to chronic malnutrition is a major problem in low- and middle-income countries due, in part, to inadequate nutrition-related practices and insufficient access to services. Limited budgets for nutritional interventions mean that available resources must be targeted in the most cost-effective manner to have the greatest impact. Quantitative tools can help guide budget allocation decisions. METHODS: The Optima approach is an established framework to conduct resource allocation optimization analyses. We applied this approach to develop a new tool, 'Optima Nutrition', for conducting allocative efficiency analyses that address childhood stunting. At the core of the Optima approach is an epidemiological model for assessing the burden of disease; we use an adapted version of the Lives Saved Tool (LiST). Six nutritional interventions have been included in the first release of the tool: antenatal micronutrient supplementation, balanced energy-protein supplementation, exclusive breastfeeding promotion, promotion of improved infant and young child feeding (IYCF) practices, public provision of complementary foods, and vitamin A supplementation. To demonstrate the use of this tool, we applied it to evaluate the optimal allocation of resources in 7 districts in Bangladesh, using both publicly available data (such as through DHS) and data from a complementary costing study. RESULTS: Optima Nutrition can be used to estimate how to target resources to improve nutrition outcomes. Specifically, for the Bangladesh example, despite only limited nutrition-related funding available (an estimated $0.75 per person in need per year), even without any extra resources, better targeting of investments in nutrition programming could increase the cumulative number of children living without stunting by 1.3 million (an extra 5%) by 2030 compared to the current resource allocation. To minimize stunting, priority interventions should include promotion of improved IYCF practices as well as vitamin A supplementation. Once these programs are adequately funded, the public provision of complementary foods should be funded as the next priority. Programmatic efforts should give greatest emphasis to the regions of Dhaka and Chittagong, which have the greatest number of stunted children. CONCLUSIONS: A resource optimization tool can provide important guidance for targeting nutrition investments to achieve greater impact.