Impact of lockdown on bed occupancy rate in a referral hospital during the COVID-19 pandemic in northeast Brazil

Lino, Danielli Oliveira da Costa; Barreto, Renato; Souza, Francisco Daniel De; Lima, Carlos José Mota Mota De; Silva, Geraldo Bezerra da

doi:10.1016/j.bjid.2020.08.002

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Healthcare utilization has repeatedly been observed to decrease during pandemics, due to such factors as mobility restrictions, social distancing measures, and fears of contracting the virus within health facilities, as patients and healthcare providers defer or forego routine healthcare, especially elective and preventive visits [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] . The magnitude of the impact of the pandemic have varied markedly according to the type of healthcare service, location, and type of facility [11 , 12 , [14] , [15] , [16] , [21] , [22] , [23] , [24] , [25] , effects that have exacerbated existing inequities in the health system. In a recent example, analyses using routine health information system data from Guinea, Liberia and Sierra Leone showed substantial reductions in the delivery of maternal, child and reproductive health services, disruptions in HIV and tuberculosis testing, and large-scale collapse of vaccine and malaria case management programs during the 2014–2015 Ebola virus disease outbreak [9 , 17 , 26 , 27] .…”

Section: Introductionmentioning

confidence: 99%

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016–2020

Xiao

Dai

Wagenaar

et al. 2021

The Lancet Regional Health - Western Pacific

107

102

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Background The aim of this study is to quantify the effects of the SARS-CoV-2 pandemic on health services utilization in China using over four years of routine health information system data. Methods We conducted a retrospective observational cohort study of health services utilization from health facilities at all levels in all provinces of mainland China. We analyzed monthly all-cause health facility visits and inpatient volume in health facilities before and during the SARS-CoV-2 outbreak using nationwide routine health information system data from January 2016 to June 2020. We used interrupted time series analyses and segmented negative binomial regression to examine changes in healthcare utilization attributable to the pandemic. Stratified analyses by facility type and by provincial Human Development Index (HDI) – an area-level measure of socioeconomic status – were conducted to assess potential heterogeneity in effects. Findings In the months before the SARS-CoV-2 outbreak, a positive secular trend in patterns of healthcare utilization was observed. After the SARS-CoV-2 outbreak, we noted statistically significant decreases in all indicators, with all indicators achieving their nadir in February 2020. The magnitude of decline in February ranged from 63% (95% CI 61–65%; p <0•0001) in all-cause visits at hospitals in regions with high HDI and 71% (95% CI 70–72%; p <0•0001) in all-cause visits at primary care clinics to 33% (95% CI 24–42%; p <0•0001) in inpatient volume and 10% (95% CI 3–17%; p = 0•0076) in all-cause visits at township health centers (THC) in regions with low HDI. The reduction in health facility visits was greater than that in the number of outpatients discharged (51% versus 48%; p <0•0079). The reductions in both health facility visits and inpatient volume were greater in hospitals than in primary health care facilities ( p <0•0001) and greater in developed regions than in underdeveloped regions ( p <0•0001). Following the nadir of health services utilization in February 2020, all indicators showed statistically significant increases. However, even by June 2020, nearly all indicators except outpatient and inpatient volume in regions with low HDI and inpatient volume in private hospitals had not achieved their pre-SARS-COV-2 forecasted levels. In total, we estimated cumulative losses of 1020.5 (95% CI 951.2- 1089.4; P <0.0001) million or 23.9% (95% CI 22.5–25.2%; P <0.0001) health facility visits, and 28.9 (95% CI 26.1–31.6; P <0.0001) million or 21.6% (95% CI 19.7–23.4%; P <0.0001) inpatients as of June 2020. Interpretation Inpatient and outpatient health services utilization in China declined sig...

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Section: Introductionmentioning

confidence: 99%

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016–2020

Xiao

Dai

Wagenaar

et al. 2021

The Lancet Regional Health - Western Pacific

107

102

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“…This paper focuses on modeling the hospital capacities in terms of ICU and non-ICU beds during the pandemic while distinguishing between patients with and without COVID-19, since most publications focus on bed management in other contexts [ 28 , 29 , 30 , 31 , 32 , 33 ] (except for Condes and Arribas [ 34 ] and Fanelli et al [ 35 ]). We have not found a work in the literature that gives information on this topic with the details and large number of hospital and inpatients that this article uses, highlighting the fact that the hospitals involved in this study were in the epicenter of the first wave of the COVID-19 pandemic at the European level.…”

Section: Discussionmentioning

confidence: 99%

Flexibility and Bed Margins of the Community of Madrid’s Hospitals during the First Wave of the SARS-CoV-2 Pandemic

Sánchez‐Úbeda

Martín

Torrego-Ellacuría

et al. 2021

IJERPH

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Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM’s health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: “bed margin” (available beds minus occupied beds, expressed as a percentage) and “flexibility” (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions:This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital’s management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.

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“…The number of hospitalized cases has been widely adopted as a metric with which to estimate the required resources for health facilities and to define lockdown restriction levels [3]. Furthermore, machine learning (ML) tools have been used to predict the number of new as well as hospitalized cases a few weeks ahead, helping local authorities to make informed decisions [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

Miranda¹,

Cardoso²,

Pahar³

et al. 2021

Preprint

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Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

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Impact of lockdown on bed occupancy rate in a referral hospital during the COVID-19 pandemic in northeast Brazil

Cited by 13 publications

References 6 publications

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016–2020

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016–2020

Flexibility and Bed Margins of the Community of Madrid’s Hospitals during the First Wave of the SARS-CoV-2 Pandemic

Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

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