ABSTRACT
Objectives: Several populations are at greater risk of severe COVID-19 due to inadequate responses to COVID-19 vaccines. Many of these individuals, and their caregivers, continue practicing varying degrees of social isolation to avoid SARS-CoV-2 infection. Following the end of lockdowns, the behaviors and impacts of continued isolation on the quality-of-life of high-risk populations remain poorly understood. This study describes the main avoidance and protective behaviors and ongoing impacts experienced by adults and caregivers of adults at high-risk of severe COVID-19. Method(s): Four virtual focus groups (April-July 2022) were conducted with individuals at high-risk of severe COVID-19, or caregivers, recruited via a convenience sample from patient panels. A discussion guide of open-ended questions was prepared based on COVID-19 guidance documents and a literature review. For qualitative analyses, an inductive approach was used for behaviors, deductive for impacts. A pre-defined codebook was updated throughout as needed. Salient concepts were defined as those mentioned by >=30% of participants or in every focus-group session. Result(s): Fourteen participants were interviewed (12 patients, 2 caregivers). Participants highlighted continued behaviors greatly impacting their quality-of-life. Avoidance behaviors (staying home, avoiding bystanders, avoiding shopping facilities and gatherings, using delivery services, family protection [43%-64%]) and protective behaviors (masking [79%], vaccination [57%]) were reported. Negative impacts included family relationship impacts (71%), collapse of social relationships (57%), difficulties accessing healthcare (43%), anxiety, fear, loneliness and depression (36%-50%), and impacts on employment/finances (36%). Positive impacts included the advent of telehealth (57%) and recognizing family importance (36%). Lack of trust in authority (57%) and hoarding of medications (36%) were negative general impacts of the COVID-19 pandemic highlighted. Concepts reported by patients and caregivers were similar. Conclusion(s): Individuals at high-risk of severe COVID-19 and their caregivers maintained avoidance and protective behaviors similar to those reported during lockdowns. This study highlights the continued burden experienced by high-risk populations.Copyright © 2023
ABSTRACT
Objectives: COVID-19 vaccine boosters are available in many countries. Public health policymakers face difficult choices over which booster brand to recommend, given limited budgets and the need to maximize health gains. Here, we provide a conceptual model to identify the best booster strategies for age-identified subpopulations under different conditions. Method(s): A constrained optimization model with an objective function to minimize bed-days was developed that varied population proportion receiving different booster options by age, to identify the best booster strategy that minimized bed-days with a constraint of maximum healthcare expenditure of US$2.10/person. It included a 3-month decision-tree model to calculate bed-days, with the following health states: healthy/asymptomatic;mild (not hospitalized);moderate (general ward);severe (intensive care unit [ICU], no mechanical ventilation);critical (requiring mechanical ventilation);and death. Medical resource utilization (MRU) costs and hospital bed-days were calculated for each health state. The base country was Brazil. Three booster options, B1 (US$1), B2 (US$2), and no-booster (NB, US>source ) were considered. Based on real-world effectiveness estimates, B1 and B2 were assumed to be 55% and 75% effective against mild/moderate COVID-19, respectively. Both reduced severe/critical COVID-19 by 90%. The target population was adults eligible for boosters, stratified by age. Result(s): The best booster strategy identified recommended 100% coverage of those eligible, with B1 for population <70 years and B2 for population >=70 years. Compared with NB, bed-days were reduced by 75%, hospitalizations by 68%, and ICU admissions by 90% leading to a 60% reduction in total costs (81% reduction in MRU costs). Within individual age-groups, costs were reduced by 57%-66% based on the age-specific disease risk. Conclusion(s): A constrained optimization model identifies the best age-specific booster allocation strategy to minimize hospital bed-days across different age groups without exceeding a predefined budget. Decision-makers could use this method to achieve the best possible health outcomes when healthcare resources are limited. Copyright © 2022