ABSTRACT
Purpose: The COVID-19 pandemic has forced countries to consider how to reach vulnerable communities with extended outreach services to improve vaccination uptake. The authors created an optimization model to align with decision-makers' objective to maximize immunization coverage within constrained budgets and deploy resources considering empirical data and endogenous demand. Design/methodology/approach: A mixed integer program (MIP) determines the location of outreach sites and the resource deployment across health centers and outreach sites. The authors validated the model and evaluated the approach in consultation with UNICEF using a case study from The Gambia. Findings: Results in The Gambia showed that by opening new outreach sites and optimizing resource allocation and scheduling, the Ministry of Health could increase immunization coverage from 91.0 to 97.1% under the same budget. Case study solutions informed managerial insights to drive gains in vaccine coverage even without the application of sophisticated tools. Originality/value: The research extended resource constrained LMIC vaccine distribution modeling literature in two ways: first, endogenous calculation of demand as a function of distance to health facility location enabled the effective design of the vaccine network around convenience to the community and second, the model's resource bundle concept more accurately and flexibly represented complex requirements and costs for specific resources, which facilitated buy-in from stakeholders responsible for managing health budgets. The paper also demonstrated how to leverage empirical research and spatial analysis of publicly available demographic and geographic data to effectively represent important contextual factors. © 2022, Emerald Publishing Limited.
ABSTRACT
Background and Purpose: We sought to investigate the impact of COVID-19 pandemic on number of acute stroke patients admitted to Japanese primary stroke centers (PSCs). Methods: The Japan Stroke Society and the MHLW registry of mechanical thrombectomy for acute ischemic stroke conducted a national annual survey of hospitalization volumes for acute ischemic stroke, intracranial cerebral hemorrhage, and subarachnoid hemorrhage in PSCs. Number of acute stroke patients was defined as sum of three stroke subtypes admitted within 7 days after the onset. Monthly acute stroke volumes were compared between 2019 and 2020, among COVID-19 waves, and regional infectious rates. Results: The stroke volume data was completed in 530 PSCs. The annual acute stroke volume was declined 2.5% from 179,893 in 2019 to 174,385 in 2020. Number of acute stoke patients was declined during COVID-19 expanding periods (1 wave, Mar-May;2 wave Jul-Aug;3 wave NovDec), whereas it was increased in the other months. The mean decline rate of stroke volumes from 2019 to 2020 was greater in 125 PSCs located in prefectures with high estimated SARS-CoV 2 infected rate (more than 2,300 per million people) than in 405 PSCs of the other regions (-4.6±15.4% vs -0.1±20.0%, P=0.008), especially during COVID-19 expanding periods (-8.2±17.9% vs -3.1±21.3%, P=0.009). Conclusions: Acute stroke volumes were declined in 2020 from 2019 in Japanese PSCs, especially during COVID-19 expanding periods and in highly infected regions. The overwhelmed health care system and infection control practices may have associated with decline of number of acute stroke patients during COVID-19 pandemic.
ABSTRACT
Background: The aim of this study was to investigate the effect of the coronavirus disease (COVID-19) pandemic restrictions on the change in muscle mass in older patients with type 2 diabetes (T2D), who were not infected with COVID-19. Methods: In this retrospective cohort study, data were obtained from outpatients who underwent bioelectrical impedance analysis at least twice before April 2020 and at least once thereafter. Skeletal muscle mass index (SMI, kg/m<sup>2</sup>) was calculated as appendicular muscle mass (kg) divided by height squared (m<sup>2</sup>). Change in SMI (kg/m<sup>2</sup>/year) was calculated as (follow-up SMI-baseline SMI/follow-up period). The differences between the changes in SMI before and after the start of the COVID-19 pandemic were evaluated using paired t test.