Forecasting local hospital bed demand for COVID-19 using on-request simulations.

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System.

Based upon 30-years of research by the author, a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 people is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem, a new approach to bed modeling has been developed that plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with a higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence of overutilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, and surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

Six-month post-intensive care outcomes during high and low bed occupancy due to the COVID-19 pandemic: A multicenter prospective cohort study.

INTRODUCTION: The COVID-19 pandemic can be seen as a natural experiment to test how bed occupancy affects post-intensive care unit (ICU) patient's functional outcomes. To compare by bed occupancy the frequency of mental, physical, and cognitive impairments in patients admitted to ICU during the COVID-19 pandemic. METHODS: Prospective cohort of adults mechanically ventilated >48 hours in 19 ICUs from seven Chilean public and private hospitals. Ninety percent of nationwide beds occupied was the cut-off for low versus high bed occupancy. At ICU discharge, 3- and 6-month follow-up, we assessed disability using the World Health Organization Disability Assessment Schedule 2.0. Quality of life, mental, physical, and cognitive outcomes were also evaluated following the core outcome set for acute respiratory failure. RESULTS: We enrolled 252 participants, 103 (41%) during low and 149 (59%) during high bed occupancy. Patients treated during high occupancy were younger (P50 [P25-P75]: 55 [44-63] vs 61 [51-71]; p<0.001), more likely to be admitted due to COVID-19 (126 [85%] vs 65 [63%]; p<0.001), and have higher education qualification (94 [63%] vs 48 [47%]; p = 0.03). No differences were found in the frequency of at least one mental, physical or cognitive impairment by bed occupancy at ICU discharge (low vs high: 93% vs 91%; p = 0.6), 3-month (74% vs 63%; p = 0.2) and 6-month (57% vs 57%; p = 0.9) follow-up. CONCLUSIONS: There were no differences in post-ICU outcomes between high and low bed occupancy. Most patients (>90%) had at least one mental, physical or cognitive impairment at ICU discharge, which remained high at 6-month follow-up (57%). CLINICAL TRIAL REGISTRATION: NCT04979897 (clinicaltrials.gov).

Assessing the impact of COVID-19 on outpatient and inpatient revenues: A comparative analysis of large and small hospitals in Taiwan.

BACKGROUND: The COVID-19 pandemic has had profound effects on healthcare systems worldwide, not only by straining medical resources but also by significantly impacting hospital revenues. These economic repercussions have varied across different hospital departments and facility sizes. This study posits that outpatient (OPD) revenues experienced greater reductions than inpatient (IPD) revenues and that the financial impact was more profound in larger hospitals than in smaller hospitals. METHODS: We collected data on patient case numbers and associated revenues for 468 hospitals from the Taiwan government-run National Health Insurance Administration website. We then employed Microsoft Excel to construct scatter plots using the trigonometric function (=DEGREES (Atan (growth rate))) for each hospital. Our analysis scrutinized 4 areas: the case numbers and the revenues (represented by medical fees) submitted to the Taiwan government-run National Health Insurance Administration in both March and April of 2019 and 2020 for OPD and IPD departments. The validity of our hypotheses was established through correlation coefficients (CCs) and chi-square tests. Moreover, to visualize and substantiate the hypothesis under study, we utilized the Kano diagram. A higher CC indicates consistent counts and revenues between 2019 and 2020. RESULTS: Our findings indicated a higher impact on OPDs, with CCs of 0.79 and 0.83, than on IPDs, which had CCs of 0.40 and 0.18. Across all hospital types, there was a consistent impact on OPDs (P = .14 and 0.46). However, a significant variance was observed in the impact on IPDs (P < .001), demonstrating that larger hospitals faced greater revenue losses than smaller facilities, especially in their inpatient departments. CONCLUSION: The two hypotheses confirmed that the COVID-19 pandemic impacted outpatient departments more than inpatient departments. Larger hospitals in Taiwan faced greater financial challenges, especially in inpatient sectors, underscoring the pandemic's varied economic effects. The COVID-19 pandemic disproportionately affected outpatient departments and larger hospitals in Taiwan. Policymakers must prioritize support for these areas to ensure healthcare resilience in future epidemics. The research approach used in this study can be utilized as a model for similar research in other countries affected by COVID-19.

Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Difference in determinants of ICU admission and death among COVID-19 hospitalized patients in two epidemic waves in Portugal: possible impact of healthcare burden and hospital bed occupancy on clinical management and outcomes, March-December 2020.

Aim: Identify factors associated with COVID-19 intensive care unit (ICU) admission and death among hospitalized cases in Portugal, and variations from the first to the second wave in Portugal, March-December 2020. Introduction: Determinants of ICU admission and death for COVID-19 need further understanding and may change over time. We used hospital discharge data (ICD-10 diagnosis-related groups) to identify factors associated with COVID-19 outcomes in two epidemic periods with different hospital burdens to inform policy and practice. Methods: We conducted a retrospective cohort study including all hospitalized cases of laboratory-confirmed COVID-19 in the Portuguese NHS hospitals, discharged from March to December 2020. We calculated sex, age, comorbidities, attack rates by period, and calculated adjusted relative risks (aRR) for the outcomes of admission to ICU and death, using Poisson regressions. We tested effect modification between two distinct pandemic periods (March-September/October-December) with lower and higher hospital burden, in other determinants. Results: Of 18,105 COVID-19 hospitalized cases, 10.22% were admitted to the ICU and 20.28% died in hospital before discharge. Being aged 60-69 years (when compared with those aged 0-49) was the strongest independent risk factor for ICU admission (aRR 1.91, 95%CI 1.62-2.26). Unlike ICU admission, risk of death increased continuously with age and in the presence of specific comorbidities. Overall, the probability of ICU admission was reduced in the second period but the risk of death did not change. Risk factors for ICU admission and death differed by epidemic period. Testing interactions, in the period with high hospital burden, those aged 80-89, women, and those with specific comorbidities had a significantly lower aRR for ICU admission. Risk of death increased in the second period for those with dementia and diabetes. Discussion and conclusions: The probability of ICU admission was reduced in the second period. Different patient profiles were identified for ICU and deaths among COVID-19-hospitalized patients in different pandemic periods with lower and higher hospital burden, possibly implying changes in clinical practice, priority setting, or clinical presentation that should be further investigated and discussed considering impacts of higher burden on services in health outcomes, to inform preparedness, healthcare workforce planning, and pandemic prevention measures.

RSV Burden and Its Impact on Pediatric Inpatient Bed Occupancy in Belgium: An Analysis of National Hospital Claims Data.

BACKGROUND: Respiratory syncytial virus (RSV) infections represent a substantial burden on pediatric services during winter. While the morbidity and financial burden of RSV are well studied, less is known about the organizational impact on hospital services (ie, impact on bed capacity and overcrowding and variation across hospitals). METHODS: Retrospective analysis of the population-wide Belgian Hospital Discharge Data Set for the years 2017 and 2018 (including all hospital sites with pediatric inpatient services), covering all RSV-associated (RSV-related International Classification of Diseases, 10th Version, Clinical Modification diagnoses) inpatient hospitalization by children under 5 years old as well as all-cause acute hospitalizations in pediatric wards. RESULTS: RSV hospitalizations amount to 68.3 hospitalizations per 1000 children less than 1 year and 5.0 per 1000 children 1-4 years of age and are responsible for 20%-40% of occupied beds during the peak period (November-December). The mean bed occupancy rate over the entire year (2018) varies across hospitals from 22.8% to 85.1% and from 30.4% to 95.1% during the peak period. Small-scale pediatric services (<25 beds) are more vulnerable to the volatility of occupancy rates. Forty-six hospital sites have daily occupancy rates above 100% (median of 9 days). Only in 1 of 23 geographically defined hospital networks these high occupancy rates are on the same calendar days. CONCLUSIONS: Pediatric services tend to be over-dimensioned to deal with peak activity mainly attributable to RSV. RSV immunization can substantially reduce pediatric capacity requirements. Enhanced collaboration in regional networks is an alternative strategy to deal with peaks and reduce capacity needs.

Gerenciamento de Leitos: do Planejamento à Implementação em um Serviço Hospitalar / Hospital bed management: from planning to implementation in a hospital service

Objetivo: A falta de leitos hospitalares no Brasil é queixa comum entre usuários do Sistema Único de Saúde. Objetivo: Relatar a experiência da construção de um Serviço de Gerenciamento de leitos e apresentar a atuação do enfermeiro como gestor, em prol da visibilidade e fortalecimento da classe de enfermagem. Método: Relato de experiência da implementação da gestão de leitos de um hospital público estadual de médio porte, em um município do interior do estado de São Paulo. Resultado: A partir da implantação houve mudanças no perfil dos indicadores dos setores assistencias, com a utilização dos leitos aproveitados em sua capacidade máxima. Observou-se a diminuição da fila de espera para internação em consequência do acesso oportuno e ordenado à vaga. Conclusão: Pode-se inferir que o gerenciamento de leitos é efetivo e eficiente na gestão hospitalar com resultados operacionais e financeiros satisfatórios e um fator preponderante para a segurança e satisfação dos clientes.(AU)

Objective: The lack of hospital beds in Brazil is a common complaint among users of the Unified Health System. Objective: To report the experience of the construction of a Bed Management Service and to present the nurse's role as manager, for the visibility and strengthening of the nursing class. Method: Experience report of the implementation of bed management in a public hospital of medium size, in a city in the interior of the state of São Paulo. Result: From the implementation there were changes in the profile of the indicators of the care sectors, with the use of beds used to their maximum capacity. A reduction in the waiting list for hospitalization was observed as a result of the timely and orderly access to vacancies. Conclusion: It can be inferred that the management of beds is effective and efficient in hospital management with satisfactory operational and financial results and a preponderant factor for the customers' safety and satisfaction.(AU)

Objetivo: La falta de camas hospitalarias en Brasil es una queja común entre los usuarios del Sistema Único de Salud. Objetivo: Relatar la experiencia de la construcción de un Servicio de Gestión de camas y presentar la actuación de la enfermera como gestora, para la visibilidad y fortalecimiento de la clase de enfermería. Método: Relato de experiência da implementação da gestão de lechos de um hospital público estadual de médio porte, em um município do interior do estado de São Paulo. Resultado: A partir da implementação houve mudanças no perfil dos indicadores dos setores assistência, com o uso de camas utilizadas ao seu máximo de capacidade. Observou-se a diminuição da fila de espera para internação em consequência do acesso oportuno e ordenado à vaga. Conclusão: É possível inferir que a gestão de camas é eficaz e eficiente na gestão hospitalar com resultados operacionais e financeiros satisfatórios e um factor preponderante para a segurança e satisfação dos clientes.(AU)

Reflexões acerca do uso da metodologia lean em serviços hospitalares / Reflections on the use of lean metodhodoly in hospital services

Introdução: A metodologia Lean é contemporânea e vem sendo utilizada em ambientes hospitalares, principalmente em serviços de urgências e emergências. Objetivo: Refletir acerca da metodologia Lean na perspectiva de suas ferramentas e estratégias, desafios, limitações e potencialidades, para a atenção hospitalar. Método: Estudo reflexivo fundamentado em base teórica e científica acerca da metodologia Lean na atenção hospitalar. Resultados: São discutidas considerações sobre a utilização da metodologia Lean com enfoque na implementação, monitoramento, potencialidades e limitações das ferramentas/estratégias utilizadas. Conclusão: Conclui-se que a implementação da metodologia Lean contribui para obtenção de melhores resultados nos principais indicadores da gestão de leitos, diminuição da superlotação nos serviços e do tempo de permanência nos leitos hospitalares, fortalece a atuação da regulação de leitos que, por sua vez, contribuem para uma melhoria da qualidade da assistência e satisfação dos usuários

Introduction: The Lean methodology is contemporary and has been used in hospital environments, especially in emergency services. Objective: To reflect on the Lean methodology from the perspective of its tools and strategies, challenges, limitations and potentialities for hospital care. Method: Reflective study based on theoretical and scientific basis about the Lean methodology in hospital care. Results: Considerations are discussed about the use of the Lean methodology focusing on the implementation, monitoring, potentialities and limitations of the tools/strategies used. Conclusion: It is concluded that the implementation of the Lean methodology contributes to obtain better results in the main indicators of bed management, reduction of overcrowding in services and length of stay in hospital beds, the regulation of beds, which in turn contribute to an improvement in the quality of care and user satisfaction

Introducción: La metodología Lean es contemporánea y ha sido utilizada en ambientes hospitalarios, principalmente en servicios de urgencias y emergencias. Objetivo: Reflexionar sobre la metodología Lean desde la perspectiva de sus herramientas y estrategias, desafíos, limitaciones y potencialidades para la atención hospitalaria. Método: Estudio reflexivo basado en bases teóricas y científicas sobre la metodología Lean en la atención hospitalaria. Resultados: Se discuten consideraciones sobre el uso de la metodología Lean, con foco en la implementación, seguimiento, potencialidades y limitaciones de las herramientas/estrategias utilizadas. Conclusión: Se concluye que la implementación de la metodología Lean contribuye a obtener mejores resultados en los principales indicadores de gestión de camas, reduciendo el hacinamiento en los servicios y el tiempo de estancia en camas hospitalarias, fortaleciendo el desempeño de la regulación de camas, que a su vez contribuyen a una mejora en la calidad de la atención y la satisfacción del usuario

Gestión de camas hospitalarias durantela pandemia en Navarra con el apoyo demétodos matemáticos de predicción / Management of hospital beds during the pandemic in Navarra with the support of mathematical prediction methods

Durante la pandemia por coronavirus, en Navarra se utilizaron modelos matemáticos depredicción para estimar las camas necesarias, convencionales y de críticos, para atender alos pacientes COVID-19. Las seis ondas pandémicas presentaron distinta incidencia en la población, ocasionandovariabilidad en los ingresos hospitalarios y en la ocupación hospitalaria. La respuesta a laenfermedad de los pacientes no fue constante en cada onda, por lo que, para la predicción decada una, se utilizaron los datos correspondientes de esa onda.El método de predicción constó de dos partes: una describió la entrada de pacientes alhospital y la otra su estancia dentro del mismo. El modelo requirió de la alimentación a tiempo real de los datos actualizados. Los resultados delos modelos de predicción fueron posteriormente volcados al sistema de información corporativotipo Business Intelligence. Esta información fue utilizada para planificar el recurso cama y lasnecesidades de profesionales asociadas a la atención de estos pacientes en el ámbito hospitalario.En la cuarta onda se realizó un análisis para cuantificar el grado de acierto de los modelospredictivos. Los modelos predijeron adecuadamente el pico, la meseta y el cambio detendencia, pero sobreestimaron los recursos necesarios para la atención de los pacientes enla parte descendente de la curva. El principal punto fuerte de la sistemática utilizada para la construcción de modelospredictivos fue proporcionar modelos en tiempo real con datos recogidos con precisión porlos sistemas de información que consiguieron un grado de acierto aceptable permitiendo unautilización inmediata.(AU)

Boletim COVID-19 - 16/05/2023

Boletim semanal COVID-19 no município de São Paulo de 16 de maio de 2023

Boletim Diário - COVID-19 no Município de São Paulo – nº 615

Boletim informativo sobre a situação do novo coronavírus na capital paulista nos hospitais da rede municipal e de campanha, contratualizados e Atenção Básica.

Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands.

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

Managing Interhospital Referrals During a COVID-19 Patient Surge in Japan: Creating Available Beds by Exchanging Patients.

A COVID-19 patient surge in Japan from July to September 2021 caused a mismatch between patient severity and bed types because hospital beds were fully occupied and patient referrals between hospitals stagnated. Japan's predominantly private healthcare system lacks effective mechanisms to coordinate healthcare providers to address the mismatch. To address the surge, in August 2021, Tokyo Saiseikai Central Hospital started a scheme to exchange patients with other hospitals to mitigate the mismatch. In this article, we outline a retrospective observational study using medical records from a tertiary care medical center that treated severe COVID-19 cases. We describe daily patient admissions to our hospital's COVID-19 beds from July to September 2021, and compared the moving average of daily admissions before and after the exchange scheme was introduced. Bed occupancy reached nearly 100% in late July when the patient surge began and continued to exceed 100% in August when the surge peaked. However, the average daily admission did not decrease in August compared with July: the median daily admission (25th to 75th percentile) during each period was 2 (1 to 2.5) in late July and 3 (2 to 4) in August. The number of patients referred in from secondary care hospitals and the number of patients referred out was balanced in August. During the patient surge, the exchange scheme enabled the hospital to maintain and even increase the number of new admissions despite the bed shortage. Coordinating patient referrals in both directions simultaneously, rather than the usual 1-way transfer, can mitigate such mismatches.

The association between bed occupancy rates and hospital quality in the English National Health Service.

We study whether hospitals that exhibit systematically higher bed occupancy rates are associated with lower quality in England over 2010/11-2017/18. We develop an economic conceptual framework to guide our empirical analysis and run regressions to inform possible policy interventions. First, we run a pooled OLS regression to test if high bed occupancy is associated with, and therefore acts as a signal of, lower quality, which could trigger additional regulation. Second, we test whether this association is explained by exogenous demand-supply factors such as potential demand, and unavoidable costs. Third, we include determinants of bed occupancy (beds, length of stay, and volume) that might be associated with quality directly, rather than indirectly through bed occupancy. Last, we use a within-between random-effects specification to decompose these associations into those due to variations in characteristics between hospitals and variations within hospitals. We find that bed occupancy rates are positively associated with overall and surgical mortality, negatively associated with patient-reported health gains, but not associated with other indicators. These results are robust to controlling for demand-supply shifters, beds, and volume. The associations reduce by 12%-25% after controlling for length of stay in most cases and are explained by variations in bed occupancy between hospitals.

Delayed hospital discharges and the trolley crisis.

We audited use of acute hospital beds in Connolly Hospital over a 3-month period (January-March 2020) which coincided with increased provision of step-down (nursing home) beds. Our results show both ineffective and inefficient baseline uses of these acute beds. Increased step-down beds improve patient care by reducing the trolley count, shortening average length of stay and reducing waiting lists. These data confirm that more step-down beds are a high priority for our Health Service to improve the effectiveness and efficiency of our hospitals i.e. better care at less cost.

Using Geocoding to Identify COVID-19 Outbreaks in Congregate Residential Settings: San Francisco's Outbreak Response in Single-Room Occupancy Hotels.

More than 500 single-room occupancy hotels (SROs), a type of low-cost congregate housing with shared bathrooms and kitchens, are available in San Francisco. SRO residents include essential workers, people with disabilities, and multigenerational immigrant families. In March 2020, with increasing concerns about the potential for rapid transmission of COVID-19 among a population with disproportionate rates of comorbidity, poor access to care, and inability to self-isolate, the San Francisco Department of Public Health formed an SRO outbreak response team to identify and contain COVID-19 clusters in this congregate residential setting. Using address-matching geocoding, the team conducted active surveillance to identify new cases and outbreaks of COVID-19 at SROs. An outbreak was defined as 3 separate households in the SRO with a positive test result for COVID-19. From March 2020 through February 2021, the SRO outbreak response team conducted on-site mass testing of all residents at 52 SROs with outbreaks identified through geocoding. The rate of positive COVID-19 tests was significantly higher at SROs with outbreaks than at SROs without outbreaks (12.7% vs 6.4%; P < .001). From March through May 2020, the rate of COVID-19 cases among SRO residents was higher than among residents of other settings (ie, non-SRO residents), before decreasing and remaining at an equal level to non-SRO residents during later periods of 2020. The annual case fatality rate for SRO residents and non-SRO residents was similar (1.8% vs 1.5%). This approach identified outbreaks in a setting at high risk of COVID-19 and facilitated rapid deployment of resources. The geocoding surveillance approach could be used for other diseases and in any setting for which a list of addresses is available.

A Model to Compare International Hospital Bed Numbers, including a Case Study on the Role of Indigenous People on Acute 'Occupied' Bed Demand in Australian States.

Comparing international or regional hospital bed numbers is not an easy matter, and a pragmatic method has been proposed that plots the number of beds per 1000 deaths versus the log of deaths per 1000 population. This method relies on the fact that 55% of a person's lifetime hospital bed utilization occurs in the last year of life-irrespective of the age at death. This is called the nearness to death effect. The slope and intercept of the logarithmic relationship between the two are highly correlated. This study demonstrates how lines of equivalent bed provision can be constructed based on the value of the intercept. Sweden looks to be the most bed-efficient country due to long-term investment in integrated care. The potential limitations of the method are illustrated using data from English Clinical Commissioning Groups. The main limitation is that maternity, paediatric, and mental health care do not conform to the nearness to death effect, and hence, the method mainly applies to adult acute care, especially medical and critical care bed numbers. It is also suggested that sensible comparison can only be made by comparing levels of occupied beds rather than available beds. Occupied beds measure the expressed bed demand (although often constrained by access to care issues), while available beds measure supply. The issue of bed supply is made complex by the role of hospital size on the average occupancy margin. Smaller hospitals are forced to operate at a lower average occupancy; hence, countries with many smaller hospitals such as Germany and the USA appear to have very high numbers of available beds. The so-called 85% occupancy rule is an "urban myth" and has no fundamental basis whatsoever. The very high number of "hospital" beds in Japan is simply an artefact arising from "nursing home" beds being counted as a "hospital" bed in this country. Finally, the new method is applied to the expressed demand for occupied acute beds in Australian states. Using data specific to acute care, i.e., excluding mental health and maternity, a long-standing deficit of beds was identified in Tasmania, while an unusually high level of occupied beds in the Northern Territory (NT) was revealed. The high level of demand for beds in the NT appears due to an exceptionally large population of indigenous people in this state, who are recognized to have elevated health care needs relative to non-indigenous Australians. In this respect, indigenous Australians use 3.5 times more occupied bed days per 1000 deaths (1509 versus 429 beds per 1000 deaths) and 6 times more occupied bed days per 1000 population (90 versus 15 beds per 1000 population) than their non-indigenous counterparts. The figure of 1509 beds per 1000 deaths (or 4.13 occupied beds per 1000 deaths) for indigenous Australians is indicative of a high level of "acute" nursing care in the last months of life, probably because nursing home care is not readily available due to remoteness. A lack of acute beds in the NT then results in an extremely high average bed occupancy rate with contingent efficiency and delayed access implications.