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Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening

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Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020. Results: We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems. Conclusion: Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.
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Brief Communications
Measure what matters: Counts of hospitalized patients
are a better metric for health system capacity planning for
a reopening
Sehj Kashyap ,
1,
* Saurabh Gombar,
1,3,
* Steve Yadlowsky,
2
Alison Callahan,
1
Jason Fries,
1
Benjamin A. Pinsky,
3
and Nigam H. Shah
1
1
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA,
2
Deptartment of Electrical
Engineering, Stanford University, Stanford, California, USA, and
3
Department of Pathology and Medicine, Stanford University
School of Medicine, Stanford, California, USA
*These authors contributed equally.
Corresponding Author: Saurabh Gombar, Stanford Center for Biomedical Informatics Research, Medical School Office
Building, Room x235, 1265 Welch Road, Stanford, CA 94305-5479, USA; sgombar@stanford.edu
Received 17 April 2020; Revised 21 April 2020; Editorial Decision 22 April 2020; Accepted 25 April 2020
ABSTRACT
Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity
requirements using readily available inputs. We examined whether testing and hospitalization data could help
quantify the anticipated burden on the health system given shelter-in-place (SIP) order.
Materials and Methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facili-
ties between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed posi-
tive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over
the 40 days between March 2nd and April 11th 2020.
Results: We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued
to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-
19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confir-
mations and slowing new hospitalizations, both of which affects the demand on health systems.
Conclusion: Without using local hospitalization rates and the age distribution of positive patients, current mod-
els are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using
these data to quantify effects of SIP and aid reopening planning.
Key words; COVID-19, surge models, capacity planning, electronic health records
INTRODUCTION
In order to prepare for coronavirus disease 2019 (COVID-19), health
system leaders and policymakers need to forecast future healthcare
needs. A number of forecasting models have been developed and widely
shared to help healthcare facilities and governments predict upcoming
patient surges and plan accordingly.
1,2
These models take in a myriad of
inputs, including population demographics, currently admitted patients,
case doubling times, and the rate at which positive cases turn into hospi-
talizations, among others.
1,35
While there are enough models, guidance,
and data to provide accurate inputs to these models remain lacking.
6
8
Currently, 95% of Americans have been asked to stay at home.
Schools, businesses, and community gathering activities have been
closed or curtailed, and plans of reopening hinge on demonstrated sus-
tained reduction in cases, availability of testing, and enough health sys-
V
CThe Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.
All rights reserved. For permissions, please email: journals.permissions@oup.com
1126
Journal of the American Medical Informatics Association, 27(7), 2020, 1026–1131
doi: 10.1093/jamia/ocaa076
Advance Access Publication Date: 17 June 2020
Brief Communications
Downloaded from https://academic.oup.com/jamia/article/27/7/1026/5858301 by Stanford University Medical Center user on 06 September 2020
tem capacity to treat patients requiring hospitalization without resorting
to crisis standards of care.
9,10
Theneedtoaccuratelyplanhealthsystem
capacity is 1 of the 6 indicators cited to guide the state of California’s de-
cision making around reopening the state’s economy.
11
An important
first step is taking stock of the currently available hospitalization and
testing data and how it has evolved as the pandemic progresses.
Stanford Health Care (SHC), a large academic healthcare system,
serves patients in the San Francisco Bay Area. The major hospitals that
comprise SHC are located in Santa Clara County, which began mandated
shelter in place (SIP) on midnight March 16, 2020, 26 days before we
conducted the analysis presented here. SHC developed an in house Sars-
CoV-2 and had extensive testing capacity starting on March 4, and after-
ward, tested anyone who had influenza-like infection symptoms, anyone
who had known COVID-19–positive contact, and healthcare workers
with a known exposure, or those who were referred by physician discre-
tion. As of April 11, our laboratory had tested nearly 15,800 cases, and
tracked hospitalization data of the test-confirmed COVID-19 cases.
Given our dual access to testing and hospitalization data, we exam-
ined whether we could reliably quantify the effects of state-mandated
SIP using testing and hospitalization rates. This allowed us to quantify
the divergence between the rates of positive case confirmations and
hospitalizations. In addition, we identified a shift in the age distribution
of new COVID-19–positive cases over the duration of the study.
MATERIALS AND METHODS
A total of 16,103 severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) reverse-transcription polymerase chain reaction tests
were performed on 15,807 patients at Stanford facilities between March
2 and April 11, 2020. Of these, 8309 tests were performed on 7929
patients in facilities in which the patient would have been admitted to
our hospital if necessary. We analyzed the fraction of tested patients that
were confirmed positive for COVID-19, the fraction of those needing
hospitalization, and the fraction requiring intensive care unit (ICU) ad-
mission over the 40 days between March 2 and April 11, 2020.
Data were obtained from a combination of 2 sources: a daily
refreshed snapshot of the health system’s Enterprise Data Warehouse
(EDW) and a twice-daily refreshed extract of all reverse-transcription
polymerase chain reaction laboratory tests for SARS-CoV-2 at Stan-
ford.AccesstoaviewoftheEDWwassetuplastyearaspartofStan-
ford Medicine’s Program for Artificial Intelligence in Healthcare.
12
The
EDW view consisted of 33 tables, containing demographics, diagnoses,
procedures, labs, and orders. These tables were refreshed nightly with
updated 1-year historical and current hospitalization information of
patients admitted in the census of the hospital. The access to the labo-
ratory testing was provided as part of Stanford’s data science response
to COVID-19.
13
The laboratory testing data consisted of details about
the specimen collected, the result, the procedure of specimen collection,
identifiers to link with EDW tables, and symptoms documented on ad-
mission. The project to track SARS-CoV-2 test positivity and hospitali-
zation trends was initiated as a quality assurance project aimed at
enabling hospital capacity planning, and institutional review board ap-
proval (protocol # 55544) was obtained prior to this submission sum-
marizing the learning from this effort.
RESULTS
A total of 3.77%of COVID-19–positive patients required
an ICU admission
As shown in Figure 1, of these 7929 tested patients, 451 (5.68%)
tested positive for COVID-19, and of these 451 cases, 59 (13.08%)
were hospitalized following their test. Among the 59 hospitalized
cases, 17 (28.8% of hospitalized and 3.77% of all positive cases) re-
quired ICU care. Our observed case hospitalization rate is more in
line with the 12% reported nationally than the 25.5% reported in
New York City (as of April 7).
14,15
The higher case hospitalization
rate in New York City may be due to the fact that low testing capac-
ity to case numbers might have led to severe cases being prioritized
for testing. Our ICU rate among confirmed cases similarly matches
American national reports of 2.9%, compared with the 5% reported
in China or 12% in Lombardy (Italy).
16,17
The hospitalization rate slowed within 10 days of SIP,
even as confirmed cases continue to rise
Between March 2nd, 2020 and April 11th, 2020, we have continued
to see new COVID-19 cases, hospitalizations, and ICU admissions;
however, their rates have slowed (Figure 2). The doubling time for
each metric increased from under 5 days on March 16 to over 25
days as of April 11. The slowdown in hospital admissions began
within 10 days of SIP and was more dramatic than the slowdown of
confirmed cases. Forecasting models should incorporate the diver-
gence between the rate of new COVID-19 cases vs new hospitaliza-
tions to better forecast near-term demand on the health system.
Given that the test results and admissions data are available in
nearly real time, health systems–based monitoring of admission
rates and the doubling time of hospitalized patient counts can pro-
vide accurate data for both public health planning and epidemiologi-
cal modeling.
3
Recently detected COVID-19 cases were younger than
were those detected prior to social distancing measures
As shown in Figure 3, between weeks 11 and 14 of the epidemic,
there was no significant shift in the age distribution of patients
tested. However, the average age of COVID-19–positive patients de-
creased (P¼.0004) from 55.6 (95% confidence interval [CI], 53.0-
58.3) years of age prior to social distancing (March 16, week 11) to
49.8 (95% CI 47.9—51.7) years of age after 2 weeks of social dis-
tancing (March 30, week 14).
Characteristics of COVID-19 cases hospitalized later
were the same as those hospitalized earlier
Compared with before social distancing, the mean length of stay of
hospitalized cases and rate of ICU admission was not significantly dif-
ferent (1.73 [95% CI, -2.16 to 5.62] days shorter and 4.1% absolute
increase [95% CI, -30% to 38%], respectively) than 2 weeks after so-
cial distancing (difference in means P¼.378 and .810, respectively)
(Table 1). Because the length of stay was right-censored for patients
still in the hospital, these estimates were corrected for censoring.
DISCUSSION
While most epidemic simulation models use new case rates, no cur-
rently published model takes into account the shifts in demographics
of positive patients, which is a major determinant of future hospital
admission rates. If most new cases are younger, the corresponding
need for hospitalizations will also be lower. Over the course of 5
weeks, we did not find a significant change in the age distribution of
patients presenting with influenza-like illness (ILI) and who tested
for COVID-19; however, we did find a significant shift toward
younger patients in those testing positive for COVID-19. The sim-
plest explanation for the shift toward younger patients testing posi-
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tive could be relaxing testing criteria. For example, guidelines at our
institution went from requiring ILI symptoms and a known COVID-
19 exposure to expressing symptoms consistent with ILI, to medical
doctor discretion. However, this does not explain why there is no
change in the demographics of patients getting tested for COVID-
19. A more plausible explanation is that because of the SIP order,
the at-risk elderly population is protected and hence less likely to
contract COVID-19. This interpretation is supported by the fact
that despite seeing a larger number of younger COVID-19–positive
patients, those that need to be admitted have similar ages, rate of
ICU admission, and length of stay as before the SIP order; now, we
see fewer absolute numbers of such cases.
Our analysis spanning 26 days after SIP clearly demonstrates
that the rate of confirmed cases, hospitalizations, and ICU admis-
sions for COVID-19 has flattened. The decrease in the rate of new
hospitalizations began within 10 days after SIP was initiated and
continues today. Despite the decrease in hospitalizations, we con-
tinue to see new patients presenting with ILI and younger patients
testing positive for COVID-19, indicating ongoing community
spread but perhaps in a lower-risk population.
This analysis demonstrates how, compared with new case counts,
new hospitalizations is a better metric both for detecting the effect of SIP
and for estimating the anticipated burden on the health system.
10,11,18
Our findings also suggest that existing surge planning efforts should fre-
quently recompute hospitalization doubling time because the change
can be swift, as seen in our data.
19
Models that do not use local hospital-
ization rates as well as the age distribution of the positive patients are
likely to overestimate the resource burden of COVID-19.
Investments made in setting up data feeds prior to and immedi-
ately at the outset of this crisis were critical to accessing such data
quickly. Additionally, a unification of our information technology
organizations across the School of Medicine and the Healthcare Sys-
Figure 1. Test result as well as hospitalization outcomes of patients tested for severe acute respiratory syndrome coronavirus 2. Each box represents 10 patients.
ICU: intensive care unit.
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Figure 2. Slowed growth in total cases detected, hospitalized, and admitted to the intensive care unit (ICU) is seen after the shelter-in-place order (first dashed
line). The prolongation of the doubling time of hospitalizations (yellow) happens faster and earlier than cases detected (gray). The divergence between the rate of
cases detected and slower rate of hospitalizations is seen within 10 days of the shelter-in-place order. Doubling times calculated over a 7-day sliding window
show that by March 28 (second dashed line), cases were doubling every 9 days, but hospitalizations were doubling every 13 days.
Figure 3. Change in age distribution of patients with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test (brown) compared with those
tested for SARS-CoV-2 (gray) for 4 weeks after sheltering in place order.
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tem, which was completed in September 2019, proved to be im-
mensely valuable enabling the setup of data access (such as to the
laboratory testing data) in a timely manner.
CONCLUSION
Given the relative ease of obtaining testing and admission data at a
health system, these metrics can not only help quantify the effects of
state-mandated SIP, but also enable better planning of health system
capacity to aid any actions required to return to precrisis operations.
20
For any reopening scenario, accurate projections of near-term health
system capacity requirements are essential.
21
Therefore, we must start
using these easily available, and useful, inputs right away.
FUNDING
This work was supported by National Library of Medicine grant
R01LM011369-07 (SK, AC, JF, and NHS), the Stanford Health CEO Innova-
tion Fund, and the Debra and Mark Leslie endowment for AI in Healthcare.
AUTHOR CONTRIBUTIONS
All authors were involved in drafting or critically revising the presented work,
gave final approval of the submitted manuscript, and agree to be accountable
for ensuring the integrity of the work. SG, SK, NHS and SY drafted the manu-
script, which was reviewed and edited by all co-authors. SG, SK, NHS con-
ceived of the analysis design. BAP acquired, helped interpret and analyze lab
testing data. JAF and AC analyzed hospitalized patients’ admission character-
istics and presenting symptom severity. SG, SK and SY conducted statistical
analysis of the data, and SY conducted additional statistical analyses to pro-
duce the censor-adjusted estimates.
ACKNOWLEDGMENTS
We acknowledge support from the Departments of Medicine and Pathology
at Stanford.
CONFLICT OF INTEREST STATEMENT
The authors have no competing interests to declare.
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Table 1. Characteristics of COVID-19–hospitalized cases around the state-mandated SIP ordered on March 16
Period Hospitalized Cases Age (y) Length of stay (d) % Hospitalized Admitted to ICU Transfer to ICU From Admission (d)
Pre-SIP 16 58.1 615.1 8.31 31.2 1.72 61.4
First 2 wk SIP 28 59 618.7 6.91 25 1.19 61.7
>2 wk SIP 15 61.4 619.1 6.58 33.3 0.76 60.65
Values are mean 6SD, unless otherwise indicated. Length of stay is adjusted for censoring using the Kaplan-Meier curve and 14 day restricted mean length of
stay.
COVID-19: coronavirus disease 2019; ICU: intensive care unit; SIP: shelter in place.
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Geographic information systems (GIS) have become essential tools in the public health domain, especially when it comes to monitoring and surveillance of disease. The purpose of this article is to describe and explore the benefits of using GIS to improve public health emergency response during a global pandemic and, in particular, how to effectively optimize the allocation of public health resources in a rural setting using a data‐driven approach that considers the multifactorial demand for new COVID‐19 testing sites. Herein, the authors present their interprofessional project as an example of such efforts to inform applications for practice. The team developed a GIS‐based multicriteria decision analysis model for use by decision‐makers and public health experts in similar future planning and response scenarios. Focus is placed on rural characteristics (e.g., accessibility), vulnerable populations, and daily changing conditions (e.g., COVID‐19 daily case fluctuations) that create additional challenges for public health agencies and policymakers.
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Importance: Preclinical models suggest dysregulation of the renin-angiotensin system (RAS) caused by SARS-CoV-2 infection may increase the relative activity of angiotensin II compared with angiotensin (1-7) and may be an important contributor to COVID-19 pathophysiology. Objective: To evaluate the efficacy and safety of RAS modulation using 2 investigational RAS agents, TXA-127 (synthetic angiotensin [1-7]) and TRV-027 (an angiotensin II type 1 receptor-biased ligand), that are hypothesized to potentiate the action of angiotensin (1-7) and mitigate the action of the angiotensin II. Design, setting, and participants: Two randomized clinical trials including adults hospitalized with acute COVID-19 and new-onset hypoxemia were conducted at 35 sites in the US between July 22, 2021, and April 20, 2022; last follow-up visit: July 26, 2022. Interventions: A 0.5-mg/kg intravenous infusion of TXA-127 once daily for 5 days or placebo. A 12-mg/h continuous intravenous infusion of TRV-027 for 5 days or placebo. Main outcomes and measures: The primary outcome was oxygen-free days, an ordinal outcome that classifies a patient's status at day 28 based on mortality and duration of supplemental oxygen use; an adjusted odds ratio (OR) greater than 1.0 indicated superiority of the RAS agent vs placebo. A key secondary outcome was 28-day all-cause mortality. Safety outcomes included allergic reaction, new kidney replacement therapy, and hypotension. Results: Both trials met prespecified early stopping criteria for a low probability of efficacy. Of 343 patients in the TXA-127 trial (226 [65.9%] aged 31-64 years, 200 [58.3%] men, 225 [65.6%] White, and 274 [79.9%] not Hispanic), 170 received TXA-127 and 173 received placebo. Of 290 patients in the TRV-027 trial (199 [68.6%] aged 31-64 years, 168 [57.9%] men, 195 [67.2%] White, and 225 [77.6%] not Hispanic), 145 received TRV-027 and 145 received placebo. Compared with placebo, both TXA-127 (unadjusted mean difference, -2.3 [95% CrI, -4.8 to 0.2]; adjusted OR, 0.88 [95% CrI, 0.59 to 1.30]) and TRV-027 (unadjusted mean difference, -2.4 [95% CrI, -5.1 to 0.3]; adjusted OR, 0.74 [95% CrI, 0.48 to 1.13]) resulted in no difference in oxygen-free days. In the TXA-127 trial, 28-day all-cause mortality occurred in 22 of 163 patients (13.5%) in the TXA-127 group vs 22 of 166 patients (13.3%) in the placebo group (adjusted OR, 0.83 [95% CrI, 0.41 to 1.66]). In the TRV-027 trial, 28-day all-cause mortality occurred in 29 of 141 patients (20.6%) in the TRV-027 group vs 18 of 140 patients (12.9%) in the placebo group (adjusted OR, 1.52 [95% CrI, 0.75 to 3.08]). The frequency of the safety outcomes was similar with either TXA-127 or TRV-027 vs placebo. Conclusions and relevance: In adults with severe COVID-19, RAS modulation (TXA-127 or TRV-027) did not improve oxygen-free days vs placebo. These results do not support the hypotheses that pharmacological interventions that selectively block the angiotensin II type 1 receptor or increase angiotensin (1-7) improve outcomes for patients with severe COVID-19. Trial registration: ClinicalTrials.gov Identifier: NCT04924660.
Article
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Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.
Book
Chống dịch hiệu quả cần dựa vào hiểu biết khoa học. Chúng ta cần huy động được một đội ngũ gồm nhiều nhà khoa học đa ngành, đa lĩnh vực, trong và ngoài nước; không ngừng đổi mới và bổ sung nguồn nhân lực khoa học này để có thêm nhiều góc nhìn mới. Các biện pháp quan trọng còn thiếu cần phải được bổ sung, các biện pháp thực thi chưa hiệu quả cần được củng cố đặc biệt là các chiến lược trọng điểm trong việc phát hiện sớm ca nhiễm bệnh, báo cáo và truy vết nhanh để phá ổ dịch sớm, cách ly sớm, đẩy mạnh độ phủ vắc xin và điều trị sớm. Nhóm chúng tôi là đội ngũ gồm hơn 300 nhà khoa học, bác sĩ và sinh viên yêu nước, hiện đang học tập – công tác tại Việt Nam và nhiều nơi khác trên thế giới. Trưởng nhóm chúng tôi – PGS.TS.BS. Nguyễn Tiến Huy – tốt nghiệp tại Đại học Y Dược TPHCM, lấy bằng Tiến sỹ về các bệnh Truyền nhiễm tại Nhật Bản và hiện đang là Phó Giáo sư tại Đại học Nagasaki, Nhật Bản. Hiện tại, ông có hơn 180 bài báo quốc tế và nằm trong top 2,5% những nhà khoa học uy tín nhất trên toàn thế giới (theo ResearchGate). Ông là người có nền tảng vững vàng về Các bệnh lây nhiễm, cùng kinh nghiệm nhiều năm nghiên cứu khoa học về nhiều lĩnh vực. Ông rất thích hợp để tham gia vào đội ngũ cố vấn chuyên môn cho quá trình phòng, chống dịch tại Việt Nam. Chúng tôi chung tay viết tài liệu này nhằm đề xuất chiến lược ứng phó, đưa ra các biện pháp phù hợp tình thế để giảm thiểu tác động của dịch và tạo điều kiện phát triển kinh tế, xã hội; đồng thời giúp tiết kiệm và sử dụng hợp lý nguồn lực. Tất cả các nội dung đều được viết dựa vào chứng cứ, góp nhặt bài học từ khắp các quốc gia khác trên thế giới. Nhóm đã so sánh các biện pháp kiểm soát dịch của TPHCM với 9 thành phố khác trên thế giới bao gồm Manila (Philippines), Bangkok (Thái Lan), Singapore, Jakarta (Indonesia), New Delhi (Ấn Độ), Tokyo (Nhật Bản), London (Anh), New York (Hoa Kỳ), và Gauteng (Nam Phi). Trong số này, 8 thành phố có nhiều hơn 5.000 ca mỗi ngày trong cơn sóng dịch cuối cùng, đặc biệt là New Delhi (Ấn Độ) có đỉnh dịch với 28.395 ca/ngày và Jakarta (Indonesia) có đỉnh 14.619 ca/ngày. Chúng tôi đồng thời so sánh các chiến lược và biện pháp chống dịch của 9 quốc gia trên, của Trung Quốc và Úc (2 quốc gia theo đuổi zero-COVID), hướng dẫn của WHO, CDC của Hoa Kỳ và Châu Âu. Nhóm cũng đánh giá các nghiên cứu khảo sát trong và ngoài nước bao gồm các nghiên cứu định lượng và định tính, hỏi thăm các nhân viên chống dịch và người dân qua điện thoại và tin nhắn. Nhóm đã tổng quan nhiều nguồn dữ liệu, phân tích các tài liệu khoa học về phòng chống dịch trong và ngoài nước. Từ đó chỉ ra 105 chiến lược và biện pháp, được phân chia vào các nhóm chiến lược khoa học, bao gồm: can thiệp không dùng thuốc, can thiệp dùng vắc xin, và can thiệp dùng thuốc. Nhóm cũng đánh giá sơ bộ hơn 30 ứng dụng điện thoại (app) chống dịch hiện có trong nước để chỉ ra điểm mạnh, điểm yếu và hướng cải thiện. Chúng tôi cũng đề xuất rất nhiều bảng kiểm (checklist), được đúc kết sau quá trình dài tìm hiểu, đánh giá, và chỉnh sửa lại để phù hợp với tình hình Việt Nam. Với tài liệu này, chúng tôi mong muốn góp chút sức nhỏ của mình để giúp Chính phủ ta sớm tái lập kiểm soát dịch bệnh, đi vào trạng thái bình thường mới và phục hồi cuộc sống ấm no cho người dân. Việt Nam quyết thắng đại dịch!
Article
Shelter in place (SIP) orders were instituted by states to alleviate the impact of the COVID-19 pandemic. However, states proceeded to reopen as SIPs were noted to be hurting the economy. We evaluated whether these reopenings affected COVID-19 hospitalizations. We collected public data on US state reopening orders and COVID-19 hospitalizations from March 8 to August 8, 2020. We utilized a doubling time metric to compare increase in hospitalizations in line with reopenings and proceeded to quantify the impact of reopening orders on cumulative hospitalizations. We found that some reopenings increased hospitalizations, and this varied by state. We also discovered that the most negatively impactful reopenings overall tended to be restaurants/bars (-92%) and houses of worship (-63.6%). Without data-backed guidance on reopening states, the healthcare burden from COVID-19 will likely persist. State governments should use data to understand the potential effects of these reopenings to guide future policies.
Article
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Health system resilience has never been more important than with the COVID-19 pandemic. There is need to identify feasible measures of resilience, potential strategies to build resilience and weaknesses of health systems experiencing shocks. The purpose of this systematic review is to examine how the resilience of health systems has been measured across various health system shocks. Following PRISMA guidelines, with double screening at each stage, the review identified 3175 studies of which 68 studies were finally included for analysis. Almost half (46%) were focused on COVID-19, followed by the economic crises, disasters and previous pandemics. Over 80% of studies included quantitative metrics. The most common WHO health system functions studied were resources and service delivery. In relation to the shock cycle, most studies reported metrics related to the management stage (79%) with the fewest addressing recovery and learning (22%). Common metrics related to staff headcount, staff wellbeing, bed number and type, impact on utilisation and quality, public and private health spending, access and coverage, and information systems. Limited progress has been made with developing standardised qualitative metrics particularly around governance. Quantitative metrics need to be analysed in relation to change and the impact of the shock. The review notes problems with measuring preparedness and the fact that few studies have really assessed the legacy or enduring impact of shocks.
Preprint
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Key Points Question: Assuming social distancing measures are maintained, what are the forecasted gaps in available health service resources and number of deaths from the COVID-19 pandemic for each state in the United States? Findings: Using a statistical model, we predict excess demand will be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds at the peak of COVID-19. Peak ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674) ventilators. Peak demand will be in the second week of April. We estimate 81,114 (95% UI 38,242 to 162,106) deaths in the United States from COVID-19 over the next 4 months. Meaning: Even with social distancing measures enacted and sustained, the peak demand for hospital services due to the COVID-19 pandemic is likely going to exceed capacity substantially. Alongside the implementation and enforcement of social distancing measures, there is an urgent need to develop and implement plans to reduce non-COVID-19 demand for and temporarily increase capacity of health facilities. Abstract Importance: This study presents the first set of estimates of predicted health service utilization and deaths due to COVID-19 by day for the next 4 months for each state in the US. Objective: To determine the extent and timing of deaths and excess demand for hospital services due to COVID-19 in the US. Design, Setting, and Participants: This study used data on confirmed COVID-19 deaths by day from WHO websites and local and national governments; data on hospital capacity and utilization for US states; and observed COVID-19 utilization data from select locations to develop a statistical model forecasting deaths and hospital utilization against capacity by state for the US over the next 4 months. Exposure(s): COVID-19. Main outcome(s) and measure(s): Deaths, bed and ICU occupancy, and ventilator use. Results: Compared to licensed capacity and average annual occupancy rates, excess demand from COVID-19 at the peak of the pandemic in the second week of April is predicted to be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds. At the peak of the pandemic, ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674). The date of peak excess demand by state varies from the second week of April through May. We estimate that there will a total of 81,114 (95% UI 38,242 to 162,106) deaths from COVID-19 over the next 4 months in the US. Deaths from COVID-19 are estimated to drop below 10 deaths per day between May 31 and June 6. Conclusions and Relevance: In addition to a large number of deaths from COVID-19, the epidemic in the US will place a load well beyond the current capacity of hospitals to manage, especially for ICU care. These estimates can help inform the development and implementation of strategies to mitigate this gap, including reducing non-COVID-19 demand for services and temporarily increasing system capacity. These are urgently needed given that peak volumes are estimated to be only three weeks away. The estimated excess demand on hospital systems is predicated on the enactment of social distancing measures in all states that have not done so already within the next week and maintenance of these measures throughout the epidemic, emphasizing the importance of implementing, enforcing, and maintaining these measures to mitigate hospital system overload and prevent deaths.
Article
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Globally, approximately 170,000 confirmed cases of coronavirus disease 2019 (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) have been reported, including an estimated 7,000 deaths in approximately 150 countries (1). On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a pandemic (2). Data from China have indicated that older adults, particularly those with serious underlying health conditions, are at higher risk for severe COVID-19-associated illness and death than are younger persons (3). Although the majority of reported COVID-19 cases in China were mild (81%), approximately 80% of deaths occurred among adults aged ≥60 years; only one (0.1%) death occurred in a person aged ≤19 years (3). In this report, COVID-19 cases in the United States that occurred during February 12-March 16, 2020 and severity of disease (hospitalization, admission to intensive care unit [ICU], and death) were analyzed by age group. As of March 16, a total of 4,226 COVID-19 cases in the United States had been reported to CDC, with multiple cases reported among older adults living in long-term care facilities (4). Overall, 31% of cases, 45% of hospitalizations, 53% of ICU admissions, and 80% of deaths associated with COVID-19 were among adults aged ≥65 years with the highest percentage of severe outcomes among persons aged ≥85 years. In contrast, no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups.
A model to forecast regional demand for COVID-19 related hospital beds
  • J O Ferstad
  • A J Gu
  • R Y Lee
Ferstad JO, Gu AJ, Lee RY, et al. A model to forecast regional demand for COVID-19 related hospital beds. medRxiv 10.1101/2020.03.26 .20044842; 2020. Accessed April 2020.
COVID-19 hospital impact model for epidemics (CHIME)
  • I Srivastava
Srivastava I. COVID-19 hospital impact model for epidemics (CHIME). https://oto.med.upenn.edu/2020/03/31/covid-19-hospital-impact-modelfor-epidemics-chime/ Accessed April 2020.
Report 13: estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries
  • S Flaxman
  • S Mishra
  • A Gandy
Flaxman S, Mishra S, Gandy A, et al. Report 13: estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. https://spiral.imperial.ac.uk:8443/handle/10044/1/77731 Accessed April 2020.
Estimation of SARS-CoV-2 Infection Prevalence
  • S Yadlowsky
  • N Shah
  • J Steinhardt
Yadlowsky S, Shah N, Steinhardt J. Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County. medRxiv 10.1101/2020.03.24. 20043067; 2020.
There are enough models
  • N Shah
Shah N. There are enough models, we need accurate inputs! https://medium.com/@nigam/there-are-enough-models-we-need-accurate-inputs-5a20aef22f01 Accessed April 2020.