2022 insights on hospital bed distribution in Saudi Arabia: evaluating needs to achieve global standards.

BACKGROUND: Equitable geographical distribution of health resources, such as hospital beds, is fundamental in ensuring public accessibility to healthcare services. This study examines the distribution of hospital beds across Saudi Arabia's 20 health regions. METHODS: A secondary data analysis was conducted using the 2022 Saudi Ministry of Health Statistical Yearbook. The study focused on calculating the hospital beds-per-1,000-people ratio across Saudi Arabia's 20 health regions. The analysis involved comparing regional bed distributions using the Gini index and Lorenz curve to assess the distribution of hospital beds. RESULTS: The national average beds-per-1,000-people ratio was 2.43, serving a population of approximately 32.2 million. The calculated mean Gini index for bed distribution was 0.15, which indicates a relatively equitable distribution. Further analysis revealed some regional disparities, with health regions like Makkah and Jeddah displaying critically low bed-to-population ratios. In contrast, others like Al-Jouf and the Northern region reported higher ratios. The study also identified the need for an additional 17,062 beds to meet international standards of 2.9 beds per 1,000 people. CONCLUSIONS: The findings revealed a national average beds-per-1,000-people ratio of 2.43, with some regional disparities. The study highlights the critical need for targeted healthcare planning and policy interventions to address the uneven distribution of hospital beds across Saudi Arabia. TRIAL REGISTRATION: Not applicable.

[The Public Health Services at the Metropolitan Region (MR) of Chile have nine acute psychiatric beds per 100,000 over 18 years old inhabitants, insufficient for current need]. / Los Servicios de Salud Públicos de la Región Metropolitana (RM) cuentan con 9 camas psiquiátricas de corta estadía por 100.000 habitantes mayores de 18 años, insuficiente para la necesidad actual.

BACKGROUND: The Public Health Services at the Metropolitan Region (MR) of Chile have nine acute psychiatric beds per 100,000 inhabitants, under international recommendations. AIM: The present study will evaluate the resolution capacity of the main MR Psychiatric Emergency Room (PER), which may help assess the impact of the availability of acute beds in the MR. MATERIAL AND METHODS: A retrospective observational study of electronic patient records for all adult patients attending PER of the Psychiatric Institute "Dr. José Horwitz B." between 2017 and 2020 was analyzed. Crude and adjusted Incidence Rate Ratios were obtained for the indication of hospitalization, admissions, and those rejected due to lack of acute psychiatric beds. RESULTS: 90,464 attendances were evaluated on 41,541 patients, and hospitalization was indicated for 12.5% of them. Admissions were carried out in 59.5%, and 35.9% did not occur due to a lack of acute psychiatric beds. When comparing the adjusted Incidence Rates, only a higher hospitalization rate was observed for users from regions (IRR = 1,267; 95% CI: 1,11-1,44; p-value < 0.001) and during the first half of 2020 (IRR = 1.49; CI95%: 1.35-1.65; p-value < 0.001). CONCLUSIONS: The demand for psychiatric hospitalizations and the low availability of acute psychiatric beds in the MR probably have unsuspected consequences. The solution requires multilevel planning among all the actors involved.

Identifying barriers to emergency department-initiated buprenorphine: A spatial analysis of treatment facility access in Michigan.

STUDY OBJECTIVES: Emergency department (ED)-initiated buprenorphine/naloxone has been shown to improve treatment retention and reduce illicit opioid use; however, its potential may be limited by a lack of accessible community-based facilities. This study compared one state's geographic distribution of EDs to outpatient treatment facilities that provide buprenorphine treatment and identified ED and geographic factors associated with treatment access. METHODS: Treatment facility data were obtained from the SAMHSA 2018 National Directory of Drug and Alcohol Abuse Treatment Facilities, and ED data were obtained from the Michigan College of Emergency Physician's 2018 ED directory. Geospatial analysis compared EDs to buprenorphine treatment facilities using 5-, 10-, and 20-mile network buffers. RESULTS: Among 131 non-exclusively pediatric EDs in Michigan, 57 (43.5%) had a buprenorphine treatment facility within 5 miles, and 66 (50.4%) had a facility within 10 miles. EDs within 10 miles of a Medicaid-accepting, outpatient buprenorphine treatment facility had higher average numbers of beds (41 vs. 15; p < 0.0001) and annual patient volumes (58,616 vs. 17,484; p < 0.0001) compared to those without. Among Michigan counties with EDs, those with at least one buprenorphine facility had larger average populations (286,957 vs. 44,757; p = 0.005) and higher annual rates of opioid overdose deaths (mean 18.3 vs. 13.0 per 100,000; p = 0.02) but were similar in terms of opioid-related hospitalizations and socioeconomic distress. CONCLUSION: Only half of Michigan EDs are within 10 miles of a buprenorphine treatment facility. Given these limitations, expanding access to ED-initiated buprenorphine in states similar to Michigan may require developing alternative models of care.

Association Between Caseload Surge and COVID-19 Survival in 558 U.S. Hospitals, March to August 2020.

BACKGROUND: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. OBJECTIVE: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. DESIGN: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). SETTING: 558 U.S. hospitals in the Premier Healthcare Database. PARTICIPANTS: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. MEASUREMENTS: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. RESULTS: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. LIMITATION: Residual confounding. CONCLUSION: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.

COVID-19 and Italy: what next?

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3-4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.

Expansion of transcatheter aortic valve replacement in the United States.

BACKGROUND: Patterns of diffusion of TAVR in the United States (U.S.) and its relation to racial disparities in TAVR utilization remain unknown. METHODS: We identified TAVR hospitals in the continental U.S. from 2012-2017 using Medicare database and mapped them to Hospital Referral Regions (HRR). We calculated driving distance from each residential ZIP code to the nearest TAVR hospital and calculated the proportion of the U.S. population, in general and by race, that lived <100 miles driving distance from the nearest TAVR center. Using a discrete time hazard logistic regression model, we examined the association of hospital and HRR variables with the opening of a TAVR program. RESULTS: The number of TAVR hospitals increased from 230 in 2012 to 540 in 2017. The proportion of the U.S. population living <100 miles from nearest TAVR hospital increased from 89.3% in 2012 to 94.5% in 2017. Geographic access improved for all racial and ethnic subgroups: Whites (84.1%-93.6%), Blacks (90.0%- 97.4%), and Hispanics (84.9%-93.7%). Within a HRR, the odds of opening a new TAVR program were higher among teaching hospitals (OR 1.48, 95% CI 1.16-1.88) and hospital bed size (OR 1.44, 95% CI 1.37-1.52). Market-level factors associated with new TAVR programs were proportion of Black (per 1%, OR 0.78, 95% CI 0.69-0.89) and Hispanic (per 1%, OR 0.82, 95% CI 0.75-0.90) residents, the proportion of hospitals within the HRR that already had a TAVR program (per 10%, OR 1.07, 95% CI 1.03-1.11), P <.01 for all. CONCLUSION: The expansion of TAVR programs in the U.S. has been accompanied by an increase in geographic coverage for all racial subgroups. Further study is needed to determine reasons for TAVR underutilization in Blacks and Hispanics.

The Impact of Halting Elective Admissions in Anticipation of a Demand Surge Due to the Coronavirus Pandemic (COVID-19).

BACKGROUND: In anticipation of a demand surge for hospital beds attributed to the coronavirus pandemic (COVID-19) many US states have mandated that hospitals postpone elective admissions. OBJECTIVES: To estimate excess demand for hospital beds due to COVID-19, the net financial impact of eliminating elective admissions in order to meet demand, and to explore the scenario when demand remains below capacity. RESEARCH DESIGN: An economic simulation to estimate the net financial impact of halting elective admissions, combining epidemiological reports, the US Census, American Hospital Association Annual Survey, and the National Inpatient Sample. Deterministic sensitivity analyses explored the results while varying assumptions for demand and capacity. SUBJECTS: Inputs regarding disease prevalence and inpatient utilization were representative of the US population. Our base case relied on a hospital admission rate reported by the Center for Disease Control and Prevention of 137.6 per 100,000, with the highest rates in people aged 65 years and older (378.8 per 100,000) and 50-64 years (207.4 per 100,000). On average, elective admissions accounted for 20% of total hospital admissions, and the average rate of unoccupied beds across hospitals was 30%. MEASURES: Net financial impact of halting elective admissions. RESULTS: On average, hospitals COVID-19 demand for hospital bed-days fell well short of hospital capacity, resulting in a substantial financial loss. The net financial impact of a 90-day COVID surge on a hospital was only favorable under a narrow circumstance when capacity was filled by a high proportion of COVID-19 cases among hospitals with low rates of elective admissions. CONCLUSIONS: Hospitals that restricted elective care took on a substantial financial risk, potentially threatening viability. A sustainable public policy should therefore consider support to hospitals that responsibly served their communities through the crisis.

Critical Care Requirements Under Uncontrolled Transmission of SARS-CoV-2.

Objectives. To estimate the critical care bed capacity that would be required to admit all critical COVID-19 cases in a setting of unchecked SARS-CoV-2 transmission, both with and without elderly-specific protection measures.Methods. Using electronic health records of all 2432 COVID-19 patients hospitalized in a large hospital in Madrid, Spain, between February 28 and April 23, 2020, we estimated the number of critical care beds needed to admit all critical care patients. To mimic a hypothetical intervention that halves SARS-CoV-2 infections among the elderly, we randomly excluded 50% of patients aged 65 years and older.Results. Critical care requirements peaked at 49 beds per 100 000 on April 1-2 weeks after the start of a national lockdown. After randomly excluding 50% of elderly patients, the estimated peak was 39 beds per 100 000.Conclusions. Under unchecked SARS-CoV-2 transmission, peak critical care requirements in Madrid were at least fivefold higher than prepandemic capacity. Under a hypothetical intervention that halves infections among the elderly, critical care peak requirements would have exceeded the prepandemic capacity of most high-income countries.Public Health Implications. Pandemic control strategies that rely exclusively on protecting the elderly are likely to overwhelm health care systems.

A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic.

BACKGROUND: As the COVID-19 pandemic continues, there is a question of whether hospitals have adequate resources to manage patients. We aim to investigate global hospital bed (HB), acute care bed (ACB), and intensive care unit (ICU) bed capacity and determine any correlation between these hospital resources and COVID-19 mortality. METHOD: Cross-sectional study utilizing data from the World Health Organization (WHO) and other official organizations regarding global HB, ACB, ICU bed capacity, and confirmed COVID-19 cases/mortality. Descriptive statistics and linear regression were performed. RESULTS: A total of 183 countries were included with a mean of 307.1 HBs, 413.9 ACBs, and 8.73 ICU beds/100,000 population. High-income regions had the highest mean number of ICU beds (12.79) and HBs (402.32) per 100,000 population whereas upper middle-income regions had the highest mean number of ACBs (424.75) per 100,000. A weakly positive significant association was discovered between the number of ICU beds/100,000 population and COVID-19 mortality. No significant associations exist between the number of HBs or ACBs per 100,000 population and COVID-19 mortality. CONCLUSIONS: Global COVID-19 mortality rates are likely affected by multiple factors, including hospital resources, personnel, and bed capacity. Higher income regions of the world have greater ICU, acute care, and hospital bed capacities. Mandatory reporting of ICU, acute care, and hospital bed capacity/occupancy and information relating to coronavirus should be implemented. Adopting a tiered critical care approach and targeting the expansion of space, staff, and supplies may serve to maximize the quality of care during resurgences and future disasters.

Influenza-associated in-hospital mortality during the 2017/2018 influenza season: a retrospective multicentre cohort study in central Germany.

The aim of this retrospective cohort study at eight hospitals in Germany was to specify influenza-associated in-hospital mortality during the 2017/2018 flu season, which was the strongest in Germany in the past 30 years. A total of 1560 patients were included in the study. Overall, in-hospital mortality was 6.7% (n = 103), in patients treated in the intensive care unit (n = 161) mortality was 22.4%. The proportion of deceased patients per hospital was between 0% and 7.0%. Influenza was the immediate cause of death in 82.8% (n = 82) of the decedents.

Hospital bed supply and inequality as determinants of maternal mortality in China between 2004 and 2016.

BACKGROUND: Driven by the government's firm commitment to promoting maternal health, maternal mortality ratio (MMR) in China has achieved a remarkable reduction over the past 25 years. Paralleled with the decline of MMR has been the expansion of hospital bed supply as well as substantial reduction in hospital bed distribution inequalities, which were thought to be significant contributors to the reduction in MMR. However, evidences on the impact of hospital bed supply as well as how its distribution inequality has affected MMR remains scarce in China. Addressing this uncertainty is essential to understand whether efforts made on the expansion of healthcare resource supply as well as on improving its distribution inequality from a geographical perspective has the potential to produce measurable population health improvements. METHODS: Panel data of 31 provinces in China between 2004 and 2016 were extracted from the national statistical data, including China Statistical Yearbooks, China Health Statistical Yearbooks and other national publications. We firstly described the changes in hospital bed density as well as its distribution inequality from a geographical perspective. Then, a linear mixed model was employed to evaluate the impact of hospital bed supply as well as its distribution inequality on MMR at the provincial level. RESULTS: The MMR decreased substantially from 48.3 to 19.9 deaths per 100,000 live births between 2004 and 2016. The average hospital bed density increased from 2.28 per 1000 population in 2004 to 4.54 per 1000 population in 2016, with the average Gini coefficient reducing from 0.32 to 0.25. As indicated by the adjusted mixed-effects regressions, hospital bed density had a negative association with MMR (ß = - 0.112, 95% CI: - 0.210--0.013) while every 0.1-unit reduction of Gini coefficient suggested 14.50% decline in MMR on average (ß = 1.354, 95% CI: 0.123-2.584). Based on the mediation analysis, the association between hospital bed density or Gini coefficient with MMR was found to be significantly mediated by facility birth rate, especially during the period from 2004 to 2009. CONCLUSIONS: This study provided empirical evidences on China's impressive success in the aspect of reducing MMR which could be attributed to the expansion of hospital beds as well as the improvement in its distribution inequality from a geographical perspective. Such findings were expected to provide evidence-based implications for long-term policy-making procedures in order to achieve rational healthcare resource allocations as well as promoting the equity and accessibility to obtaining health care from a holistic perspective. Constant efforts should be made on improving the equity in healthcare resource allocations in order to achieve the penetration of universal healthcare coverage.

Burden of pneumococcal pneumonia requiring ICU admission in France: 1-year prognosis, resources use, and costs.

BACKGROUND: Community-acquired pneumonia (CAP), especially pneumococcal CAP (P-CAP), is associated with a heavy burden of illness as evidenced by high rates of intensive care unit (ICU) admission, mortality, and costs. Although well-defined acutely, determinants influencing long-term burden are less known. This study assessed determinants of 28-day and 1-year mortality and costs among P-CAP patients admitted in ICUs. METHODS: Data regarding all hospital and ICU stays in France in 2014 were extracted from the French healthcare administrative database. All patients admitted in the ICU with a pneumonia diagnosis were included, except those hospitalized for pneumonia within the previous 3 months. The pneumococcal etiology and comorbidities were captured. All hospital stays were included in the cost analysis. Comorbidities and other factors effect on the 28-day and 1-year mortality were assessed using a Cox regression model. Factors associated with increased costs were identified using log-linear regression models. RESULTS: Among 182,858 patients hospitalized for CAP in France for 1 year, 10,587 (5.8%) had a P-CAP, among whom 1665 (15.7%) required ICU admission. The in-hospital mortality reached 22.8% at day 28 and 32.3% at 1 year. The mortality risk increased with age > 54 years, malignancies (hazard ratio (HR) 1.54, 95% CI [1.23-1.94], p = 0.0002), liver diseases (HR 2.08, 95% CI [1.61-2.69], p < 0.0001), and the illness severity at ICU admission. Compared with non-ICU-admitted patients, ICU survivors remained at higher risk of 1-year mortality. Within the following year, 38.2% (516/1350) of the 28-day survivors required at least another hospital stay, mostly for respiratory diseases. The mean cost of the initial stay was €19,008 for all patients and €11,637 for subsequent hospital stays within 1 year. One-year costs were influenced by age (lower in patients > 75 years old, p = 0.008), chronic cardiac (+ 11% [0.02-0.19], p = 0.019), and respiratory diseases (+ 11% [0.03-0.18], p = 0.006). CONCLUSIONS: P-CAP in ICU-admitted patients was associated with a heavy burden of mortality and costs at one year. Older age was associated with both early and 1-year increased mortality. Malignant and chronic liver diseases were associated with increased mortality, whereas chronic cardiac failure and chronic respiratory disease with increased costs. TRIAL REGISTRATION: N/A (study on existing database).

Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response.

Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.

Organizational aspects of pediatric anesthesia and surgery between two waves of Covid-19.

BACKGROUND: The initial wave of the Covid-19 pandemic has hit Italy, and Lombardy in particular, with violence, forcing to reshape all hospitals' activities; this happened even in pediatric hospitals, although the young population seemed initially spared from the disease. "Vittore Buzzi" Children's Hospital, which is a pediatric/maternal hospital located in Milan (Lombardy Region), had to stop elective procedures-with the exception of urgent/emergent ones-between February and May 2020 to leave space and resources to adults' care. We describe the challenges of reshaping the hospital's identity and structure, and restarting pediatric surgery and anesthesia, from May on, in the most hit area of the world, with the purpose to avoid and contain infections. Both patients and caregivers admitted to hospital have been tested for Sars-CoV-2 in every case. METHODS: Observational cohort study via review of clinical charts of patients undergoing surgery between 16th May and 30th September 2020, together with SARS-CoV -2 RT-PCR testing outcomes, and comparison to same period surgeries in 2019. RESULTS: An increase of approximately 70% in pediatric surgeries (OR 1.68 [1.33-2.13], P < .001) and a higher increase in the number of surgeries were reported (OR 1.75 (1.43-2.15), P < .001). Considering only urgent procedures, a significant difference in the distribution of the type of surgery was observed (Chi-squared P-value < .001). Sars-CoV-2-positive patients have been 0.8% of total number; 14% of these was discovered through caregiver's positivity. CONCLUSION: We describe our pathway for safe pediatric surgery and anesthesia and the importance of testing both patient and caregiver.

Impact of the COVID-19 Pandemic on Surgical Oncology in Europe: Results of a European Survey.

BACKGROUND: The first COVID-19 pandemic wave hit most of the health-care systems worldwide. The present survey aimed to provide a European overview on the COVID-19 impact on surgical oncology. METHODS: This anonymous online survey was accessible from April 24 to May 11, 2020, for surgeons (n = 298) who were contacted by the surgical society European Digestive Surgery. The survey was completed by 88 surgeons (29.2%) from 69 different departments. The responses per department were evaluated. RESULTS: Of the departments, 88.4% (n = 61/69) reported a lower volume of patients in the outpatient clinic; 69.1% (n = 47/68) and 75.0% (n = 51/68) reported a reduction in hospital bed and the operating room capacity, respectively. As a result, the participants reported an average reduction of 29.3% for all types of oncological resections surveyed in this questionnaire. The strongest reduction was observed for oncological resections of hepato-pancreatico-biliary (HPB) cancers. Of the interviewed surgeons, 68.7% (n = 46/67) agreed that survival outcomes will be negatively impacted by the pandemic. CONCLUSION: The first COVID-19 pandemic wave had a significant impact on surgical oncology in Europe. The surveyed surgeons expect an increase in the number of unresectable cancers as well as poorer survival outcomes due to cancellations of follow-ups and postponements of surgeries.

Factors influencing the COVID-19 daily deaths' peak across European countries.

OBJECTIVES: The purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries. STUDY DESIGN: For 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic. METHODS: Univariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables. RESULTS: The proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak. CONCLUSIONS: Our analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.

Acute Stroke Care Is at Risk in the Era of COVID-19: Experience at a Comprehensive Stroke Center in Barcelona.

BACKGROUND AND PURPOSE: The purpose of the study is to analyze how the coronavirus disease 2019 (COVID-19) pandemic affected acute stroke care in a Comprehensive Stroke Center. METHODS: On February 28, 2020, contingency plans were implemented at Hospital Clinic of Barcelona to contain the COVID-19 pandemic. Among them, the decision to refrain from reallocating the Stroke Team and Stroke Unit to the care of patients with COVID-19. From March 1 to March 31, 2020, we measured the number of emergency calls to the Emergency Medical System in Catalonia (7.5 million inhabitants), and the Stroke Codes dispatched to Hospital Clinic of Barcelona. We recorded all stroke admissions, and the adequacy of acute care measures, including the number of thrombectomies, workflow metrics, angiographic results, and clinical outcomes. Data were compared with March 2019 using parametric or nonparametric methods as appropriate. RESULTS: At Hospital Clinic of Barcelona, 1232 patients with COVID-19 were admitted in March 2020, demanding 60% of the hospital bed capacity. Relative to March 2019, the Emergency Medical System had a 330% mean increment in the number of calls (158 005 versus 679 569), but fewer Stroke Code activations (517 versus 426). Stroke admissions (108 versus 83) and the number of thrombectomies (21 versus 16) declined at Hospital Clinic of Barcelona, particularly after lockdown of the population. Younger age was found in stroke admissions during the pandemic (median [interquartile range] 69 [64-73] versus 75 [73-80] years, P=0.009). In-hospital, there were no differences in workflow metrics, angiographic results, complications, or outcomes at discharge. CONCLUSIONS: The COVID-19 pandemic reduced by a quarter the stroke admissions and thrombectomies performed at a Comprehensive Stroke Center but did not affect the quality of care metrics. During the lockdown, there was an overload of emergency calls but fewer Stroke Code activations, particularly in elderly patients. Hospital contingency plans, patient transport systems, and population-targeted alerts must act concertedly to better protect the chain of stroke care in times of pandemic.

Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia.

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.

Critical Care Bed Capacity in Asian Countries and Regions.

OBJECTIVE: To assess the number of adult critical care beds in Asian countries and regions in relation to population size. DESIGN: Cross-sectional observational study. SETTING: Twenty-three Asian countries and regions, covering 92.1% of the continent's population. PARTICIPANTS: Ten low-income and lower-middle-income economies, five upper-middle-income economies, and eight high-income economies according to the World Bank classification. INTERVENTIONS: Data closest to 2017 on critical care beds, including ICU and intermediate care unit beds, were obtained through multiple means, including government sources, national critical care societies, colleges, or registries, personal contacts, and extrapolation of data. MEASUREMENTS AND MAIN RESULTS: Cumulatively, there were 3.6 critical care beds per 100,000 population. The median number of critical care beds per 100,000 population per country and region was significantly lower in low- and lower-middle-income economies (2.3; interquartile range, 1.4-2.7) than in upper-middle-income economies (4.6; interquartile range, 3.5-15.9) and high-income economies (12.3; interquartile range, 8.1-20.8) (p = 0.001), with a large variation even across countries and regions of the same World Bank income classification. This number was independently predicted by the World Bank income classification on multivariable analysis, and significantly correlated with the number of acute hospital beds per 100,000 population (r = 0.19; p = 0.047), the universal health coverage service coverage index (r = 0.35; p = 0.003), and the Human Development Index (r = 0.40; p = 0.001) on univariable analysis. CONCLUSIONS: Critical care bed capacity varies widely across Asia and is significantly lower in low- and lower-middle-income than in upper-middle-income and high-income countries and regions.