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1.
PLoS Comput Biol ; 17(12): e1009697, 2021 12.
Article in English | MEDLINE | ID: covidwho-1571974

ABSTRACT

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.


Subject(s)
Algorithms , COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , SARS-CoV-2 , Vaccination/methods , Age Factors , COVID-19/epidemiology , COVID-19/immunology , COVID-19 Vaccines/supply & distribution , Computational Biology , Computer Simulation , Health Care Rationing/methods , Health Care Rationing/statistics & numerical data , Humans , Mass Vaccination/methods , Mass Vaccination/statistics & numerical data , Netherlands/epidemiology , Pandemics/prevention & control , Pandemics/statistics & numerical data , SARS-CoV-2/immunology , Vaccination/statistics & numerical data
2.
Int J Equity Health ; 20(1): 183, 2021 08 14.
Article in English | MEDLINE | ID: covidwho-1496177

ABSTRACT

BACKGROUND: The determinants of access to immunizers are still poorly understood, leading to questions about which criteria were considered in this distribution. Given the above, the present study aimed to analyze the determinants of access to the SARS-CoV-2 vaccine by different countries. METHODS: The study covered 189 countries using data from different public databases, and collected until February 19, 2021. We used eight explanatory variables: gross domestic product (GDP), extreme poverty, human development index (HDI), life expectancy, median age, coronavirus disease 2019 (COVID-19) cases, COVID-19 tests, and COVID-19 deaths. The endogenous variables were total vaccine doses, vaccine doses per thousand, and days of vaccination. The structural equation modeling (SEM) technique was applied to establish the causal relationship between the country's COVID-19 impact, socioeconomic variables, and vaccine access. To support SEM, we used confirmatory factor analysis, t-test, and Pearson's correlation. RESULTS: We collected the sample on February 19, and to date, 80 countries (42.1%) had already received a batch of immunizers against COVID-19. The countries with first access to the vaccine (e.g., number of days elapsed since they took the first dose) were the United Kingdom (68), China (68), Russia (66), and Israel (62). The countries receiving the highest doses were the United States, China, India, and Israel. The countries with extreme poverty had lower access to vaccines and the richer countries gained priority access. Countries most affected by COVID (deaths and cases) also received immunizers earlier and in greater volumes. Unfortunately, similar to other vaccines, indicators, such as income, poverty, and human development, influence vaccines' access. Thus affecting the population of vulnerable and less protected countries. Therefore, global initiatives for the equitable distribution of COVID need to be discussed and encouraged. CONCLUSIONS: Determinants of vaccine distribution consider the impact of the disease in the country and are also affected by favorable socioeconomic indicators. The COVID-19 vaccines need to be accessible to all affected countries, regardless of their social hands.


Subject(s)
COVID-19 Vaccines , Global Health , Health Care Rationing , Health Services Accessibility , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines/supply & distribution , Health Care Rationing/methods , Health Services Accessibility/statistics & numerical data , Humans , Socioeconomic Factors
4.
Nat Commun ; 12(1): 4673, 2021 08 03.
Article in English | MEDLINE | ID: covidwho-1340997

ABSTRACT

Dynamically adapting the allocation of COVID-19 vaccines to the evolving epidemiological situation could be key to reduce COVID-19 burden. Here we developed a data-driven mechanistic model of SARS-CoV-2 transmission to explore optimal vaccine prioritization strategies in China. We found that a time-varying vaccination program (i.e., allocating vaccines to different target groups as the epidemic evolves) can be highly beneficial as it is capable of simultaneously achieving different objectives (e.g., minimizing the number of deaths and of infections). Our findings suggest that boosting the vaccination capacity up to 2.5 million first doses per day (0.17% rollout speed) or higher could greatly reduce COVID-19 burden, should a new wave start to unfold in China with reproduction number ≤1.5. The highest priority categories are consistent under a broad range of assumptions. Finally, a high vaccination capacity in the early phase of the vaccination campaign is key to achieve large gains of strategic prioritizations.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Health Care Rationing/methods , Mass Vaccination/methods , Basic Reproduction Number , COVID-19/epidemiology , COVID-19/transmission , China/epidemiology , Health Priorities , Humans , Incidence , Models, Theoretical , SARS-CoV-2/immunology , Vaccination Coverage
5.
J Clin Epidemiol ; 139: 255-263, 2021 11.
Article in English | MEDLINE | ID: covidwho-1322194

ABSTRACT

OBJECTIVE: In pandemics like COVID-19, the need for medical resources quickly outpaces available supply. policymakers need strategies to inform decisions about allocating scarce resources. STUDY DESIGN AND SETTING: We updated a systematic review on evidence-based approaches and searched databases through May 2020 for evaluation of strategies for policymakers. RESULTS: The 201 identified studies evaluated reducing demand for healthcare, optimizing existing resources, augmenting resources, and adopting crisis standards of care. Most research exists to reduce demand (n = 149); 39 higher quality studies reported benefits of contact tracing, school closures, travel restrictions, and mass vaccination. Of 28 strategies to augment resources, 6 higher quality studies reported effectiveness of establishing temporary facilities, use of volunteers, and decision support software. Of 23 strategies to optimize existing resources, 12 higher quality studies reported successful scope of work expansions and building on existing interagency agreements. Of 15 COVID-19 studies, 5 higher quality studies reported on combinations of policies and benefits of community-wide mask policies. CONCLUSION: Despite the volume, the evidence base is limited; few strategies were empirically tested in robust study designs. The review provides a comprehensive overview of the effects of strategies to allocate resources and provides critical appraisal to identify the best available evidence.


Subject(s)
Administrative Personnel , Health Care Rationing/methods , Pandemics , COVID-19/epidemiology , Humans
6.
MMWR Morb Mortal Wkly Rep ; 70(28): 991-996, 2021 Jul 16.
Article in English | MEDLINE | ID: covidwho-1311471

ABSTRACT

COVID-19 has disproportionately affected non-Hispanic Black or African American (Black) and Hispanic persons in the United States (1,2). In North Carolina during January-September 2020, deaths from COVID-19 were 1.6 times higher among Black persons than among non-Hispanic White persons (3), and the rate of COVID-19 cases among Hispanic persons was 2.3 times higher than that among non-Hispanic persons (4). During December 14, 2020-April 6, 2021, the North Carolina Department of Health and Human Services (NCDHHS) monitored the proportion of Black and Hispanic persons* aged ≥16 years who received COVID-19 vaccinations, relative to the population proportions of these groups. On January 14, 2021, NCDHHS implemented a multipronged strategy to prioritize COVID-19 vaccinations among Black and Hispanic persons. This included mapping communities with larger population proportions of persons aged ≥65 years among these groups, increasing vaccine allocations to providers serving these communities, setting expectations that the share of vaccines administered to Black and Hispanic persons matched or exceeded population proportions, and facilitating community partnerships. From December 14, 2020-January 3, 2021 to March 29-April 6, 2021, the proportion of vaccines administered to Black persons increased from 9.2% to 18.7%, and the proportion administered to Hispanic persons increased from 3.9% to 9.9%, approaching the population proportion aged ≥16 years of these groups (22.3% and 8.0%, respectively). Vaccinating communities most affected by COVID-19 is a national priority (5). Public health officials could use U.S. Census tract-level mapping to guide vaccine allocation, promote shared accountability for equitable distribution of COVID-19 vaccines with vaccine providers through data sharing, and facilitate community partnerships to support vaccine access and promote equity in vaccine uptake.


Subject(s)
COVID-19 Vaccines/administration & dosage , /statistics & numerical data , Adolescent , Adult , Aged , COVID-19/epidemiology , COVID-19/ethnology , COVID-19/prevention & control , Health Care Rationing/methods , Health Status Disparities , Humans , Middle Aged , North Carolina/epidemiology , Vaccination Coverage/statistics & numerical data , Young Adult
8.
Nat Med ; 27(7): 1298-1307, 2021 07.
Article in English | MEDLINE | ID: covidwho-1233717

ABSTRACT

Many vaccine rationing guidelines urge planners to recognize, and ideally reduce, inequities. In the United States, allocation frameworks are determined by each of the Centers for Disease Control and Prevention's 64 jurisdictions (50 states, the District of Columbia, five cities and eight territories). In this study, we analyzed vaccine allocation plans published by 8 November 2020, tracking updates through to 30 March 2021. We evaluated whether jurisdictions adopted proposals to reduce inequity using disadvantage indices and related place-based measures. By 30 March 2021, 14 jurisdictions had prioritized specific zip codes in combination with metrics such as COVID-19 incidence, and 37 jurisdictions (including 34 states) had adopted disadvantage indices, compared to 19 jurisdictions in November 2020. Uptake of indices doubled from 7 to 14 among the jurisdictions with the largest shares of disadvantaged communities. Five applications were distinguished: (1) prioritizing disadvantaged groups through increased shares of vaccines or vaccination appointments; (2) defining priority groups or areas; (3) tailoring outreach and communication; (4) planning the location of dispensing sites; and (5) monitoring receipt. To ensure that equity features centrally in allocation plans, policymakers at the federal, state and local levels should universalize the uptake of disadvantage indices and related place-based measures.


Subject(s)
COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , Health Care Rationing/methods , Health Policy , Socioeconomic Factors , COVID-19/epidemiology , Guidelines as Topic , Health Equity , Humans , Incidence , SARS-CoV-2 , United States/epidemiology
11.
J Racial Ethn Health Disparities ; 8(4): 799-802, 2021 08.
Article in English | MEDLINE | ID: covidwho-1216285

ABSTRACT

Strikingly ignoring the critical impact of systemic racism in vulnerabilities to the deadly coronavirus, phase one of the vaccine rollout is not reaching the Black population that has suffered the most from COVID. An urgent need exists for a race-conscious approach that ensures equitable opportunities to both access and receive the vaccines.


Subject(s)
COVID-19 Vaccines/supply & distribution , COVID-19/ethnology , Health Care Rationing/methods , Racism/prevention & control , African Americans/statistics & numerical data , COVID-19/prevention & control , Health Status Disparities , Humans , United States/epidemiology
13.
Geriatr Nurs ; 42(4): 787-791, 2021.
Article in English | MEDLINE | ID: covidwho-1213243

ABSTRACT

The COVID 19 pandemic has led to an increase in the number of patients in need of ventilation. Limitations in the number of respirators may cause an ethical problem for the medical and nursing staff in deciding who should be connected to the available respirators.  We conducted a cross-sectional survey among a convenience sample of 278 healthcare professionals at one medical center. They were asked to rank their preference in respirator allocation to three COVID-19 patients, one 80 years old with no cognitive illness, one 50 years old with Alzheimer's disease (AD), and one 80 years old with AD. Most respondents (75%) chose the 80-year-old AD patient as last preference, but were evenly divided on how to rank the other two patients. Medical staff have difficulty deciding whether age or cognitive status should be the deciding factor ventilator allocation. Determination of a set policy would help professionals with these decisions.


Subject(s)
Alzheimer Disease/complications , COVID-19/therapy , Health Care Rationing/methods , Personnel, Hospital/psychology , Ventilators, Mechanical , Aged, 80 and over , COVID-19/epidemiology , Choice Behavior , Cross-Sectional Studies , Humans , Pandemics , SARS-CoV-2
14.
BMJ Open Qual ; 10(2)2021 04.
Article in English | MEDLINE | ID: covidwho-1183363

ABSTRACT

During the first wave of the coronavirus pandemic, the UK government took the decision to centralise the procurement, allocation and distribution of mission-critical intensive care unit (ICU) medical equipment. Establishing new supply chains in the context of global shortages presented significant challenges. This report describes the development of an innovative platform developed rapidly and voluntarily by clinical engineers, to mobilise the UK's shared medical equipment inventory, in order to match ICU capacity to dynamically evolving clinical demand. The 'Coronavirus ICU Medical Equipment Distribution' platform was developed to optimise ICU equipment allocation, distribution, collection, redeployment and traceability across the National Health Service. Although feedback on the platform has largely been very positive, the platform was built for a scenario that did not fully materialise in the UK and this affected the implementation approach. As such, it was not used to its full potential. Nonetheless, the platform and the insights derived and disseminated in its development have been extremely valuable. It provides a prototype for not only optimising system capacity in future pandemic scenarios but also a means for maximally exploiting the large amount of new equipment in the UK health system, as a result of the coronavirus pandemic. This early stage innovation has demonstrated that a system-wide pooled information resource can benefit the operations of individual organisations. It has also generated numerous lessons to be borne in mind in innovation projects.


Subject(s)
COVID-19 , Critical Care/organization & administration , Health Care Rationing/methods , Hospital Distribution Systems/organization & administration , Intensive Care Units/organization & administration , Humans , SARS-CoV-2 , State Medicine , United Kingdom/epidemiology
16.
JAMA Netw Open ; 4(3): e211974, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1148765

ABSTRACT

Importance: Breast cancer screening, surveillance, and diagnostic imaging services were profoundly limited during the initial phase of the coronavirus disease 2019 (COVID-19) pandemic. Objective: To develop a risk-based strategy for triaging mammograms during periods of decreased capacity. Design, Setting, and Participants: This population-based cohort study used data collected prospectively from mammography examinations performed in 2014 to 2019 at 92 radiology facilities in the Breast Cancer Surveillance Consortium. Participants included individuals undergoing mammography. Data were analyzed from August 10 to November 3, 2020. Exposures: Clinical indication for screening, breast symptoms, personal history of breast cancer, age, time since last mammogram/screening interval, family history of breast cancer, breast density, and history of high-risk breast lesion. Main Outcomes and Measures: Combinations of clinical indication, clinical history, and breast cancer risk factors that subdivided mammograms into risk groups according to their cancer detection rate were identified using classification and regression trees. Results: The cohort included 898 415 individuals contributing 1 878 924 mammograms (mean [SD] age at mammogram, 58.6 [11.2] years) interpreted by 448 radiologists, with 1 722 820 mammograms in individuals without a personal history of breast cancer and 156 104 mammograms in individuals with a history of breast cancer. Most individuals were aged 50 to 69 years at imaging (1 113 174 mammograms [59.2%]), and 204 305 (11.2%) were Black, 206 087 (11.3%) were Asian or Pacific Islander, 126 677 (7.0%) were Hispanic or Latina, and 40 021 (2.2%) were another race/ethnicity or mixed race/ethnicity. Cancer detection rates varied widely based on clinical indication, breast symptoms, personal history of breast cancer, and age. The 12% of mammograms with very high (89.6 [95% CI, 82.3-97.5] to 122.3 [95% CI, 108.1-138.0] cancers detected per 1000 mammograms) or high (36.1 [95% CI, 33.1-39.3] to 47.5 [95% CI, 42.4-53.3] cancers detected per 1000 mammograms) cancer detection rates accounted for 55% of all detected cancers and included mammograms to evaluate an abnormal mammogram or breast lump in individuals of all ages regardless of breast cancer history, to evaluate breast symptoms other than lump in individuals with a breast cancer history or without a history but aged 60 years or older, and for short-interval follow-up in individuals aged 60 years or older without a breast cancer history. The 44.2% of mammograms with very low cancer detection rates accounted for 13.1% of detected cancers and included annual screening mammograms in individuals aged 50 to 69 years (3.8 [95% CI, 3.5-4.1] cancers detected per 1000 mammograms) and all screening mammograms in individuals younger than 50 years regardless of screening interval (2.8 [95% CI, 2.6-3.1] cancers detected per 1000 mammograms). Conclusions and Relevance: In this population-based cohort study, clinical indication and individual risk factors were associated with cancer detection and may be useful for prioritizing mammography in times and settings of decreased capacity.


Subject(s)
Breast Neoplasms/diagnosis , COVID-19 , Health Care Rationing/methods , Mammography , Mass Screening/methods , Pandemics , Triage/methods , Aged , Breast/diagnostic imaging , Breast/pathology , COVID-19/prevention & control , Cohort Studies , Early Detection of Cancer , Female , Humans , Medical History Taking , Middle Aged , Physical Examination , Radiology , Risk Factors , SARS-CoV-2
17.
Patient ; 14(3): 331-338, 2021 05.
Article in English | MEDLINE | ID: covidwho-1144419

ABSTRACT

INTRODUCTION: One of the challenges faced by hospitals during the coronavirus disease 2019 (COVID-19) pandemic is resource shortages in intensive care units (ICUs). In times of scarcity, patient prioritization based on non-medical considerations might be necessary. OBJECTIVE: The aim of this study was to pilot test a survey to elicit public opinions on the relative importance of non-medical considerations in priority setting when admitting patients to the ICU in times of crisis. METHODS: A discrete-choice experiment was used to collect social preferences for priority setting when admitting patients to the ICU during the COVID-19 pandemic. The six attributes were patient age, profession, guardianship, risk-conscious behavior on a societal level, health-conscious behavior, and expected ICU length of stay. The data were analyzed using a mixed multinomial logit model. Interactions between the age and profession of the respondents and the age and profession of the patient profiles were considered. RESULTS: The mean (± standard deviation) age of respondents was 35.9 ± 14.5 years. In all, 70% of respondents indicated that medical and/or non-medical considerations should play a role in prioritizing patients for the ICU, whereas 15% agreed with a "first come, first served" strategy and the remaining 15% had no opinion. Respondents deemed risk-conscious behavior on a societal level to be the most important non-medical factor that should be used to prioritize patients in phase three of the framework, garnering an attribute importance (AI) of 31.2%, followed by patient age (AI 16.3%) and health-conscious behavior (AI 16.0%). ICU length of stay had the lowest impact on patient prioritization for ICU admittance (AI 10.9%). Younger and older respondents attached more importance to age than respondents in the middle age group and indicated a stronger preference to prioritize patients in their own age group (p = 0.042). CONCLUSION: The results of our study demonstrate the relative importance members of the public attach to responsible societal behavior during the COVID-19 pandemic. In the next phase of the study, we will elicit the perspectives of a representative sample of the Dutch population. Changes to the task design and attribute operationalization could improve the external validity of the study findings, and optimization of the experimental design will improve the internal validity of the study.


Subject(s)
COVID-19/epidemiology , Health Care Rationing/methods , Intensive Care Units/statistics & numerical data , Public Opinion , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Choice Behavior , Delivery of Health Care , Female , Humans , Length of Stay , Male , Middle Aged , Netherlands/epidemiology , Pandemics , Patient Admission , Pilot Projects , SARS-CoV-2 , Triage/methods , Young Adult
18.
BMC Med ; 18(1): 404, 2020 12 18.
Article in English | MEDLINE | ID: covidwho-979471

ABSTRACT

BACKGROUND: The COVID-19 pandemic has placed sustained demand on health systems globally, and the capacity to provide critical care has been overwhelmed in some jurisdictions. It is unknown which triage criteria for allocation of resources perform best to inform health system decision-making. We sought to summarize and describe existing triage tools and ethical frameworks to aid healthcare decision-making during infectious disease outbreaks. METHODS: We conducted a rapid review of triage criteria and ethical frameworks for the allocation of critical care resources during epidemics and pandemics. We searched Medline, EMBASE, and SCOPUS from inception to November 3, 2020. Full-text screening and data abstraction were conducted independently and in duplicate by three reviewers. Articles were included if they were primary research, an adult critical care setting, and the framework described was related to an infectious disease outbreak. We summarized each triage tool and ethical guidelines or framework including their elements and operating characteristics using descriptive statistics. We assessed the quality of each article with applicable checklists tailored to each study design. RESULTS: From 11,539 unique citations, 697 full-text articles were reviewed and 83 articles were included. Fifty-nine described critical care triage protocols and 25 described ethical frameworks. Of these, four articles described both a protocol and ethical framework. Sixty articles described 52 unique triage criteria (29 algorithm-based, 23 point-based). Few algorithmic- or point-based triage protocols were good predictors of mortality with AUCs ranging from 0.51 (PMEWS) to 0.85 (admitting SOFA > 11). Most published triage protocols included the substantive values of duty to provide care, equity, stewardship and trust, and the procedural value of reason. CONCLUSIONS: This review summarizes available triage protocols and ethical guidelines to provide decision-makers with data to help select and tailor triage tools. Given the uncertainty about how the COVID-19 pandemic will progress and any future pandemics, jurisdictions should prepare by selecting and adapting a triage tool that works best for their circumstances.


Subject(s)
COVID-19 , Critical Care , Health Care Rationing/ethics , Health Care Rationing/methods , Triage/methods , Disease Outbreaks , Humans , SARS-CoV-2 , Triage/ethics
19.
JAMA Netw Open ; 4(3): e214149, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1141277

ABSTRACT

Importance: Significant concern has been raised that crisis standards of care policies aimed at guiding resource allocation may be biased against people based on race/ethnicity. Objective: To evaluate whether unanticipated disparities by race or ethnicity arise from a single institution's resource allocation policy. Design, Setting, and Participants: This cohort study included adults (aged ≥18 years) who were cared for on a coronavirus disease 2019 (COVID-19) ward or in a monitored unit requiring invasive or noninvasive ventilation or high-flow nasal cannula between May 26 and July 14, 2020, at 2 academic hospitals in Miami, Florida. Exposures: Race (ie, White, Black, Asian, multiracial) and ethnicity (ie, non-Hispanic, Hispanic). Main Outcomes and Measures: The primary outcome was based on a resource allocation priority score (range, 1-8, with 1 indicating highest and 8 indicating lowest priority) that was assigned daily based on both estimated short-term (using Sequential Organ Failure Assessment score) and longer-term (using comorbidities) mortality. There were 2 coprimary outcomes: maximum and minimum score for each patient over all eligible patient-days. Standard summary statistics were used to describe the cohort, and multivariable Poisson regression was used to identify associations of race and ethnicity with each outcome. Results: The cohort consisted of 5613 patient-days of data from 1127 patients (median [interquartile range {IQR}] age, 62.7 [51.7-73.7]; 607 [53.9%] men). Of these, 711 (63.1%) were White patients, 323 (28.7%) were Black patients, 8 (0.7%) were Asian patients, and 31 (2.8%) were multiracial patients; 480 (42.6%) were non-Hispanic patients, and 611 (54.2%) were Hispanic patients. The median (IQR) maximum priority score for the cohort was 3 (1-4); the median (IQR) minimum score was 2 (1-3). After adjustment, there was no association of race with maximum priority score using White patients as the reference group (Black patients: incidence rate ratio [IRR], 1.00; 95% CI, 0.89-1.12; Asian patients: IRR, 0.95; 95% CI. 0.62-1.45; multiracial patients: IRR, 0.93; 95% CI, 0.72-1.19) or of ethnicity using non-Hispanic patients as the reference group (Hispanic patients: IRR, 0.98; 95% CI, 0.88-1.10); similarly, no association was found with minimum score for race, again with White patients as the reference group (Black patients: IRR, 1.01; 95% CI, 0.90-1.14; Asian patients: IRR, 0.96; 95% CI, 0.62-1.49; multiracial patients: IRR, 0.81; 95% CI, 0.61-1.07) or ethnicity, again with non-Hispanic patients as the reference group (Hispanic patients: IRR, 1.00; 95% CI, 0.89-1.13). Conclusions and Relevance: In this cohort study of adult patients admitted to a COVID-19 unit at 2 US hospitals, there was no association of race or ethnicity with the priority score underpinning the resource allocation policy. Despite this finding, any policy to guide altered standards of care during a crisis should be monitored to ensure equitable distribution of resources.


Subject(s)
COVID-19 , Health Care Rationing , Healthcare Disparities/ethnology , Hospitalization/statistics & numerical data , Resource Allocation , Standard of Care/statistics & numerical data , COVID-19/ethnology , COVID-19/therapy , Cohort Studies , Female , Florida/epidemiology , Health Care Rationing/methods , Health Care Rationing/organization & administration , Health Services Needs and Demand , Humans , Male , Middle Aged , Mortality/ethnology , Resource Allocation/methods , Resource Allocation/organization & administration
20.
Patient ; 14(3): 319-330, 2021 05.
Article in English | MEDLINE | ID: covidwho-1117440

ABSTRACT

BACKGROUND AND OBJECTIVE: During the COVID-19 pandemic, resources in intensive care units (ICUs) have the potential to be inadequate to treat all those who might benefit. Therefore, it is paramount to identify the views of the community regarding how to allocate such resources. This study aims to quantify Australian community preferences for ventilation allocation. METHODS: A discrete choice experiment was designed and administrated to an adult Australian online panel. Each survey respondent answered 12 choice sets from a total design of 120. Each choice set placed the respondent in the role of hypothetical decision maker, prioritising care between two patients. Conditional logit, mixed logit regression and latent class analysis were used to analyse the data. Additionally, we asked a series of attitudinal questions about different methods of making such decisions in practice, focusing on who should be responsible. RESULTS: A total of 1050 community members completed the survey and responded to each choice. Dimensions considered most important were age, likely effectiveness, smoking status, whether the person has dependents, whether they are a healthcare worker, and whether they have a disability or not. Estimating marginal rates of substitution between patient characteristics and chance of survival if ventilated yielded values of up to 30 percentage points if the patient was 70 years old relative to being 30. However, respondents typically said they would prefer such decisions to be made by medical professionals. CONCLUSION: This study demonstrated the preferences of the community to allocation of ventilators during the COVID-19 pandemic. The use of such information should be treated with some caution as the underlying reason for such preferences are unclear, and respondents themselves preferred the decision to be made by others.


Subject(s)
COVID-19/epidemiology , Choice Behavior , Health Care Rationing/methods , Ventilators, Mechanical , Adolescent , Adult , Age Factors , Aged , Australia/epidemiology , Female , Humans , Intensive Care Units , Male , Middle Aged , Pandemics , Quality-Adjusted Life Years , SARS-CoV-2 , Smoking/epidemiology , Socioeconomic Factors , Young Adult
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