Prioritization Of Resources Research Articles

Plasma virome and the risk of blood-borne infection in persons with substance use disorder

There is an urgent need for innovative methods to reduce transmission of bloodborne pathogens like HIV and HCV among people who inject drugs (PWID). We investigate if PWID who acquire non-pathogenic bloodborne viruses like anelloviruses and pegiviruses might be at greater risk of acquiring a bloodborne pathogen. PWID who later acquire HCV accumulate more non-pathogenic viruses in plasma than matched controls who do not acquire HCV infection. Additionally, phylogenetic analysis of those non-pathogenic virus sequences reveals drug use networks. Here we find first in Baltimore and confirm in San Francisco that the accumulation of non-pathogenic viruses in PWID is a harbinger for subsequent acquisition of pathogenic viruses, knowledge that may guide the prioritization of the public health resources to combat HIV and HCV.

Red blood cell distribution width as a prognostic biomarker for viral infections: prospects and challenges.

Viral diseases remain a significant global health threat, and therefore prioritization of limited healthcare resources is required to effectively manage dangerous viral disease outbreaks. In a pandemic of a newly emerged virus that is yet to be well understood, a noninvasive host-derived prognostic biomarker is invaluable for risk prediction. Red blood cell distribution width (RDW), an index of red blood cell size disorder (anisocytosis), is a potential predictive biomarker for severity of many diseases. In view of the need to prioritize resources during response to outbreaks, this review highlights the prospects and challenges of RDW as a prognostic biomarker for viral infections, with a focus on hepatitis and COVID-19, and provides an outlook to improve the prognostic performance of RDW for risk prediction in viral diseases.

A quantifiable framework for ‘Covid-19 exposure’ to support the Vaccine prioritization and resource allocation for resource-constraint societies

As the pandemic COVID-19 has severely affected the entire world, it became the utmost necessity of careful prioritization and utilization of limited medical resources, especially for the resource-constraint societies. This requirement along with the asymptomatic nature of COVID-19 in a significant proportion of the population, has prompted the need for a risk assessment tool-kit that can provide a rational behind prioritization and allocation of limited available medical resources. The purpose of this study is to compose a scientific-rationale-based ‘COVID-19-Self-Assessment-Test’ (C19SAT) framework, which helps develop a basis for measuring the risk of COVID-19 exposure with self-assessment. The study comprises of two major parts- (1) identification of key components of the C19SAT framework by studying various guidelines (WHO & CDC) and relevant literature, and (2) quantification of those components by assigning weight through the analysis of survey results and opinions from medical physicians and experts. A C19SAT framework is developed to measure the risk of covid-19 exposure, comprising a set of identified components (12 parameters and their corresponding attributes) and their corresponding weights. As the scientific rationale behind such assessment framework has not been reported to date in case of available mobile applications and web-based toolkit, the present study brings the novelty and reports the construction of such a framework. The study opens up untouched research and discussion on the composition of the COVID-19 exposure-measurement framework, which can help in vaccine prioritization and resource allocation. Further studies can be carried out on refining the framework for better understanding and effectiveness towards urban health governance.

Effects of age and gender on the relationship between alcohol use disorder and somatic diseases: a national register study in Norway

ObjectiveThis study aimed to examine how age and gender moderate the associations between alcohol use disorders (AUD) and several somatic diseases.Design and settingWe performed a retrospective, register-based cohort study with...

Community and Provider Perspectives on Molecular HIV Surveillance and Cluster Detection and Response for HIV Prevention: Qualitative Findings From King County, Washington.

Responding quickly to HIV outbreaks is one of four pillars of the U.S. Ending the HIV Epidemic (EHE) initiative. Inclusion of cluster detection and response in the fourth pillar of EHE has led to public discussion concerning bioethical implications of cluster detection and response and molecular HIV surveillance (MHS) among public health authorities, researchers, and community members. This study reports on findings from a qualitative analysis of interviews with community members and providers regarding their knowledge and perspectives of MHS. We identified five key themes: (a) context matters, (b) making sense of MHS, (c) messaging, equity, and resource prioritization, (d) operationalizing confidentiality, and (e) stigma, vulnerability, and power. Inclusion of community perspectives in generating innovative approaches that address bioethical concerns related to the use of MHS data is integral to ensure that widely accessible information about the use of these data is available to a diversity of community members and providers.

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

BackgroundMultimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity.MethodsThe performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis).ResultsThe adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal).ConclusionsThe addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.

The Utility of a Rapid Review Evaluation Process to a National HTA Agency.

Background and ObjectiveIn Ireland, similar to other jurisdictions, health technology assessment (HTA) is used to inform the health payer’s drug reimbursement decisions. These HTAs are conducted by the National Centre for Pharmacoeconomics (NCPE). In 2009, the NCPE introduced the Rapid Review process to identify drugs that do not require further assessment in the form of the previously established full HTA process.MethodsA retrospective analysis of all Rapid Reviews submitted to the NCPE from 2010 to 2019, inclusive, was conducted. Rapid Review recommendation was recorded (i.e. full HTA required or not required). For those submitted from 2012 to 2019, additional data relating to the drug, economic and clinical evidence-related factors were collected. Multivariable logistic regression methods were used to model the relationship between these factors and the likelihood of requiring a full HTA. An exploratory analysis estimated the additional NCPE appraisal time that would have been required to evaluate all drugs, had the Rapid Review process not been established.ResultsOf the 446 Rapid Reviews submitted, approximately half (49.6%) were deemed to require a full HTA. Drugs for cancer indications, drugs designated first-in-class status, and high-cost drugs were positively and significantly associated with the likelihood of requiring a full HTA. No significant association was found for drugs for orphan indications when factors relating to cost and clinical evidence were included in the model. Without the Rapid Review process, an estimated additional 15,631 NCPE appraisal days would have been required to evaluate all drugs submitted over the 10-year period.ConclusionsThis is the first study to use data uniquely available to the NCPE to evaluate factors associated with the requirement for a full HTA following a Rapid Review. The process has reduced the NCPE appraisal time required to evaluate all submissions over the study period. The NCPE’s Rapid Review process allows for appropriate resource prioritisation within a national HTA agency.

Benchmarking strategic alignment of business and IT strategies: opportunities, risks, challenges and solutions.

Business and IT strategy alignment is a complex dynamic process in which organizations are in a position to enable extensive IT capabilities to achieve their business objectives. This interdependence is amplified by the COVID-19 crisis, which makes the integration of IT and business strategies more important than ever. This paper mainly aims to contribute to the understanding of strategic alignment from a practical perspective, as well as to demonstrate the applicability and robustness of the Strategic Alignment Model (SAM). Moreover, potential opportunities and risks associated with the strategic alignment of business and IT strategies are analysed. Findings are discussed after a qualitative analysis of 31 participants (semi-structured survey and interviews). Results indicated several difficulties affecting the strategic alignment implementation transcend the business and IT strategies like communication, corporate culture, governance, resource prioritization, and effective leadership. The study contends that there is a need to align and harmonize different agendas and interests within an organization and improve the understanding of the value of Strategic Alignment.

Comparing 911 and Emergency Hotline Calls for Domestic Violence in Seven Cities: What Happened When People Started Staying Home Due to COVID-19?

Research Summary: We examine changes in help-seeking for domestic violence (DV) in seven U.S. cities during the COVID-19 pandemic. Using Bayesian structural time-series modeling with daily data to construct a synthetic counterfactual, we test whether calls to police and/or emergency hotlines varied in 2020 as people stayed home due to COVID-19. Across this sample, we estimate there were approximately 1,030 more calls to police and 1,671 more calls to emergency hotlines than would have occurred absent the pandemic. Policy Implications: Inter-agency data sharing and analysis holds great promise for better understanding localized trends in DV in real time. Research-practitioner partnerships can help DV coordinated community response teams (CCRTs) develop accessible and sustainable dashboards to visualize data and advance community transparency. As calls for drastic changes in policing are realized, prioritization of finite resources will become critical. Data-driven decision-making by CCRTs provides an opportunity to work within resource constraints without compromising the safety of DV victims.

Effect of Coronavirus Disease 2019 on Transplantation and Nephrology in India: A Nationwide Report From India

BackgroundThe effect of coronavirus disease 2019 (COVID-19) on a developing nation is sparsely reported and, more importantly, the discrepancies in public and private sectors are underexplored.MethodsWe retrospectively investigated the data on the effect of COVID-19 on renal transplantation between 2019 and 2020 in a nationwide analysis from 8 public and 10 private sector hospitals of India.ResultsOn comparing the yearly data, the number of living-related transplants and deceased donor transplants declined by 48% (2610 vs 1370) and 49% (194 vs 99), respectively. The outpatient numbers and in-center admissions decreased by 40.4% (616,741 vs 367,962) and 30.8 % (73,190 vs 49,918). respectively. There was no increase in the number of renal or graft biopsies in the COVID-19 era. The number of waitlisted patients on hemodialysis was higher in public (304,898 vs 338,343) when compared with private (163,096 vs 150,292) in the last 2 years. Similarly, the number of waitlisted patients on peritoneal dialysis (4655 vs 3526) was higher in the public sector compared with private sector (932 vs 745). The decline in living transplants during the pandemic was higher in public sectors (58%) compared with the private (49%). However, the decline in deceased donation was higher in private (57.9%) relative to public (50.6%).ConclusionsCOVID-19 has adversely affected the transplantation activities across the Indian transplantation centers, with a disproportionately higher impact on waitlisted patients in public sector programs. A sound prioritization of health care resources is mandated to safeguard the most deprived and high-risk waitlisted patients during the pandemic.

Health care costs of hospitalization of young children for respiratory syncytial virus infections: a population-based matched cohort study.

Background:Respiratory syncytial virus (RSV) infection poses a substantial clinical burden among infants and young children. We sought to determine the health care costs of hospitalizations attributable to RSV in Ontario, Canada, from the health care payer perspective.Methods:For this population-based matched cohort study, we identified children younger than 24 months who were or were not hospitalized with RSV infections in 2006–2016. We performed a cost-of-illness analysis using linked administrative health data, with subjects stratified by gestational age and congenital heart disease, and propensity score–matched on established risk factors. The primary outcome was attributable health care costs per patient, reflecting the difference in direct medical costs between the groups, calculated to 12 months postdischarge in 2020 Canadian dollars.Results:We identified 14 608 RSV-infected children, matched to 72 040 controls. The adjusted attributable cost of hospitalized RSV was $134 931 900 over 10 years, or $9240 per patient (95% confidence interval [CI] $8790–$9690). Health care costs escalated 3 days before hospitalization, and persisted up to 12 months after discharge. Increased costs were associated with major comorbidities, but not extreme premature birth. The highest mean attributable cost per patient was in the presence of hemodynamically significant heart disease ($60 110, 95% CI $26 700–$93 060). Infants born at 36–43 weeks’ gestation constituted the greatest overall cost burden at $117 886 720.Interpretation:Although the greatest direct medical costs per patient hospitalized with RSV infection are among children with cardiac disease, the greatest overall cost burden is from children born at or near term, who are not targeted by current prophylaxis strategies. The substantial attributable health care costs of RSV can inform cost-effectiveness analyses of novel RSV vaccines and prioritization of health care resources.

A Simplified Method for Rapid Estimation of Emergency Water Supply Needs after Earthquakes

Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (Mw) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m2 (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.

Air Pollution and Urban Green Space: Evidence of Environmental Injustice in Adama, Ethiopia

While air pollution data in Ethiopia is limited, existing studies indicate high levels of both ambient and household air pollution; rapid urbanization also threatens the preservation of urban green spaces. In this study, environmental injustice, or the disproportionate burden of environmental exposures on persons of lower socioeconomic status (SES), was explored among women in Ethiopia using a mother and child cohort from the city of Adama. Land-use regression models were previously developed for modeling ambient nitrogen dioxide (NO2) and nitrogen oxides (NOx) throughout Adama, while household air pollution (cooking fuel type) and the presence of green space were assessed through questionnaires and home visits, respectively. The odds of being exposed to these environmental factors were analyzed in association with two SES indicators, education and occupation, using logistic regression. Our results indicate the presence of environmental injustice in Adama, as women with lower SES shouldered a higher burden of air pollution exposure and enjoyed less urban green space than their higher SES counterparts. These findings encourage the prioritization of air quality control and urban planning resources toward policy action within lower SES areas. From a societal perspective, our results also support more upstream interventions, including investment in educational and occupational opportunities. Still, a human rights approach is emphasized, as governments are responsible for protecting the right to a clean environment, especially for those disproportionately exposed. To the best of our knowledge, this is the first study on environmental injustice in Ethiopia, and the first in Sub-Saharan Africa to investigate the inequalities of ambient and household air pollution exposure as well as urban green space access in the same cohort.

Prioritization of renewable energy resources based on sustainable management approach using simultaneous evaluation of criteria and alternatives: A case study on Iran's electricity industry

Nowadays, an increase in energy demand, environmental concerns, and the shortage of fossil fuel resources have made energy management researchers explore alternative resources for clean and renewable energy resources. The simultaneous incorporation of a comprehensive set of technical, economic, environmental, and social attributes in decision-making processes is recommended to improve the efficiency and effectiveness of sustainable decisions on energy resource planning. To tackle this challenge, an efficient method is proposed based on the Simultaneous Evaluation of Criteria and Alternatives (SECA) for the optimal selection of renewable energy resources. In the SECA technique, criteria and alternative evaluations are performed simultaneously. The proposed assessment framework is applied in Iran's electricity industry as a real case study, and a two-round fuzzy-based Delphi technique is also adopted to extract the assessment criteria from the experts' opinions. Moreover, the management insights are inferred from sensitivity analysis. According to the study results, solar, wind, biomass, hydroelectric, hydrogen, geothermal, and marine energy resources are sorted in descending order of priority. Finally, the best composite option of sustainable energies encompassing a combination of solar, wind, and biomass energy resources concludes the study.

Assessing Loss to Follow-up After Newborn Hearing Screening in the Neonatal Intensive Care Unit: Sociodemographic Factors That Affect Completion of Initial Audiological Evaluation.

Neonatal intensive care unit (NICU) patients are at high risk for congenital hearing loss. Previous studies have found sociodemographic factors associated with loss to follow-up for newborn hearing screening, but none have specifically studied the NICU population. Our objective is to determine if demographics and socioeconomic status is associated with loss to follow-up in a newborn population with extended NICU stay. A retrospective cohort study was conducted on 443 NICU infants with extended NICU stay utilizing data extracted from infant and maternal medical records at an urban safety-net hospital. Younger maternal age (adjusted odds ratio [OR] 0.95, confidence interval [CI] 0.91 to 0.99), higher gravidity (adjusted OR 1.39, CI 1.12 to 1.72), and former smoking status (adjusted OR 2.57, CI 1.07-6.18) were identified as independent predictors of loss to follow-up for NHS after conducting a multivariable logistic regression. Demographic and socioeconomic variables, such as sex, parity, birth weight, mode of birth, highest level of maternal education, maternal race/ethnicity, zip code metrics, and maternal language were not found to be associated with loss to follow-up. Maternal age, gravidity, and smoking status are risk factors for loss to follow-up for NHS in newborns with extended NICU stay, a group at high risk for hearing loss. Our findings demonstrate that socioeconomic and demographic factors for loss to follow-up in the extended-stay NICU population are distinct from the well-baby population. Further investigation of these patients will allow prioritization of limited resources to subgroups within the extended-stay NICU population at risk for loss to follow-up for newborn hearing screening.

Dynamic updating of post-earthquake damage and functional restoration forecasts of water distribution systems using Bayesian inferencing

A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.

Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas.

Bicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014–2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.

Head and Neck Surgery

Session Description: The COVID-19 pandemic brought many otolaryngology and head and neck practices into sharp focus. Critical resources were diverted to acute care specialties on the front lines. Head and neck cancer care was upended, and surgeons witnessed treatment delays resulting in worse oncologic outcomes and the exacerbation of existing health care disparities. Caring for patients with head and neck cancer under severe acute resource constraint and infection risk of COVID-19 required health care systems and clinicians to develop innovative care delivery strategies to optimize timely, equitable, head and neck cancer care delivery. As a result, several approaches emerged to minimize treatment delays, develop ethical frameworks for prioritization of care and resources, enhance access, and minimize viral transmission;these important innovations are likely to persist into the foreseeable future. Lessons learned during this experience will prepare head and neck surgeons for potential subsequent events that may similarly strain health care resources. The goal of this interactive panel presentation is to understand important lessons learned about cancer care delivery and outcomes during the COVID-19 pandemic in various jurisdictions. These expert panelists will discuss the ethical considerations of surgical prioritization and the clinical practice changes that were implemented. The panelists will provide a framework for ongoing decision making with the goal of minimizing health care disparities and optimizing oncologic outcomes while resources are restricted. Outcome Objectives: (1) Understand the effects of the COVID-19 pandemic on head and neck cancer patients in various health care systems and regions. (2) Recognize the components of attendees' health care systems that may contribute to inequities and demonstrate knowledge of the ethical considerations when caring for cancer patients during a pandemic. (3) Apply the lessons learned to address disparities and care gaps even after the resolution of the COVID-19 pandemic and in possible future disaster situations.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique.

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

COVID-19 risk factors: The curious case of Africa's governance and preparedness

COVID-19 is now established in Africa, and requires appropriate prioritization of resources and customized control measures. Although there have been lower than predicted number of COVID-19 cases (6,839,159 (3.4% of global cases)) and fatalities (172,413 (4.1% of cases in Africa)) compared to global estimates from other regions, as of 3rd August 2021. The World Health Organization (WHO) has warned that poor mitigation strategies could worsen the current situation in African countries. Several aspects have been attributed to the lower COVID-19 magnitude observed in Africa that include: warmer climate, a youthful population, and previous experience in managing infectious diseases. However, the level of COVID-19 risk of exposure and vulnerability to develop complications varies greatly across the continent. At present, most COVID-19 disease trajectories have been predicted using mathematical models focused solely on demographic factors. We compared the global health security and governance indices in forty-one African countries, with a population of more than 2.5 million, and matched these to the magnitude of COVID-19 burden, to establish whether there is correlation. These findings suggests that more accurate comparisons of responses to COVID-19 can only be made within clusters of African countries that share similar governance and preparedness standards. We conclude that proper implementation of the continental framework on disaster preparedness and management is required for management of the COVID-19 pandemic.