real time characterization of risks of death associated with the middle east respiratory CORD-Papers-2022-06-02 (Version 1)

Title: Real-time characterization of risks of death associated with the Middle East respiratory syndrome (MERS) in the Republic of Korea 2015
Abstract: BACKGROUND: An outbreak of the Middle East respiratory syndrome (MERS) comprising 185 cases linked to healthcare facilities occurred in the Republic of Korea from May to July 2015. Owing to the nosocomial nature of the outbreak it is particularly important to gain a better understanding of the epidemiological determinants characterizing the risk of MERS death in order to predict the heterogeneous risk of death in medical settings. METHODS: We have devised a novel statistical model that identifies the risk of MERS death during the outbreak in real time. While accounting for the time delay from illness onset to death risk factors for death were identified using a linear predictor tied to a logit model. We employ this approach to (1) quantify the risks of death and (2) characterize the temporal evolution of the case fatality ratio (CFR) as case ascertainment greatly improved during the course of the outbreak. RESULTS: Senior persons aged 60 years or over were found to be 9.3 times (95 % confidence interval (CI) 5.316.9) more likely to die compared to younger MERS cases. Patients under treatment were at a 7.8-fold (95 % CI 4.016.7) significantly higher risk of death compared to other MERS cases. The CFR among patients aged 60 years or older under treatment was estimated at 48.2 % (95 % CI 35.261.3) as of July 31 2015 while the CFR among other cases was estimated to lie below 15 %. From June 6 2015 onwards the CFR declined 0.3-fold (95 % CI 0.11.1) compared to the earlier epidemic period which may perhaps reflect enhanced case ascertainment following major contact tracing efforts. CONCLUSIONS: The risk of MERS death was significantly associated with older age as well as treatment for underlying diseases after explicitly adjusting for the delay between illness onset and death. Because MERS outbreaks are greatly amplified in the healthcare setting enhanced infection control practices in medical facilities should strive to shield risk groups from MERS exposure.
Published: 2015-09-30
Journal: BMC Med
DOI: 10.1186/s12916-015-0468-3
DOI_URL: http://doi.org/10.1186/s12916-015-0468-3
Author Name: Mizumoto Kenji
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mizumoto_kenji
Author Name: Endo Akira
Author link: https://covid19-data.nist.gov/pid/rest/local/author/endo_akira
Author Name: Chowell Gerardo
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chowell_gerardo
Author Name: Miyamatsu Yuichiro
Author link: https://covid19-data.nist.gov/pid/rest/local/author/miyamatsu_yuichiro
Author Name: Saitoh Masaya
Author link: https://covid19-data.nist.gov/pid/rest/local/author/saitoh_masaya
Author Name: Nishiura Hiroshi
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nishiura_hiroshi
sha: 4bdb53b23deb0bc56d0aa7b26551ab76b1fd082e
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 26420593
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/26420593
pmcid: PMC4588253
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588253
url: https://doi.org/10.1186/s12916-015-0468-3 https://www.ncbi.nlm.nih.gov/pubmed/26420593/
has_full_text: TRUE
Keywords Extracted from Text Content: patients Patients coronaviruses 401.9 f(τ joint β i Korea [15] [16] [17] S(τ Ebola virus H1N1 Patients α i coronavirus ⋯ persons contacts B line patients inpatients
Extracted Text Content in Record: First 5000 Characters:Background: An outbreak of the Middle East respiratory syndrome (MERS), comprising 185 cases linked to healthcare facilities, occurred in the Republic of Korea from May to July 2015. Owing to the nosocomial nature of the outbreak, it is particularly important to gain a better understanding of the epidemiological determinants characterizing the risk of MERS death in order to predict the heterogeneous risk of death in medical settings. We have devised a novel statistical model that identifies the risk of MERS death during the outbreak in real time. While accounting for the time delay from illness onset to death, risk factors for death were identified using a linear predictor tied to a logit model. We employ this approach to (1) quantify the risks of death and (2) characterize the temporal evolution of the case fatality ratio (CFR) as case ascertainment greatly improved during the course of the outbreak. Results: Senior persons aged 60 years or over were found to be 9.3 times (95 % confidence interval (CI), 5.3-16.9) more likely to die compared to younger MERS cases. Patients under treatment were at a 7.8-fold (95 % CI, 4.0-16.7) significantly higher risk of death compared to other MERS cases. The CFR among patients aged 60 years or older under treatment was estimated at 48.2 % (95 % CI, 35.2-61.3) as of July 31, 2015, while the CFR among other cases was estimated to lie below 15 %. From June 6, 2015, onwards, the CFR declined 0.3-fold (95 % CI, 0.1-1.1) compared to the earlier epidemic period, which may perhaps reflect enhanced case ascertainment following major contact tracing efforts. Conclusions: The risk of MERS death was significantly associated with older age as well as treatment for underlying diseases after explicitly adjusting for the delay between illness onset and death. Because MERS outbreaks are greatly amplified in the healthcare setting, enhanced infection control practices in medical facilities should strive to shield risk groups from MERS exposure. When a novel infectious disease emerges, it is vital to assess the potential global public health impact, i.e. the potential of a pathogen to cause a devastating pandemic [1] . The public health impact is mainly characterized by the transmission potential and virulence, the latter of which is usually measured by the case fatality ratio (CFR), namely the proportion of deaths among cases [2] . Due to the importance of generating accurate estimates of the CFR in real time as an outbreak progresses and public health interventions are implemented, a number of epidemiological methods have been proposed, including those that account for the time delay from illness onset to death [3] [4] [5] and those that correct for ascertainment bias through the combination of several different observations by employing a Bayesian evidence synthesis method [6] . Other methods have developed a slightly different CFR concept, e.g. the so-called "infection fatality risk", the ratio of excess mortality to the serologically determined infected fraction of the population [7] . The CFR is an important epidemiological quantity to estimate in real time during outbreaks, and the recent outbreak of the Middle East respiratory syndrome (MERS) in the Republic of Korea in 2015 has not been an exception [8] . Besides the MERS outbreak in the Republic of Korea, earlier studies of MERS in the Middle East have consistently indicated that the CFR for MERS has been about 40 % [9] and could be as low as 20 % for estimates that only include secondary cases [10] . Despite the availability of useful methods to estimate the CFR for MERS, it is crucial to identify risk factors associated with MERS death, as the risk of death may vary significantly with age, occupation, and underlying comorbidities [11] . During the epidemic of severe acute respiratory syndrome (SARS), a real-time analysis found that confirmed or probable cases aged 60 years or older were at greater risk of death than younger cases [12] . Moreover, infections with SARS-associated coronavirus with comorbidities were found to be 1.7 times more likely to die than those without comorbidities [13] . Due to similarities in clinical characteristics between MERS and SARS patients as both coronaviruses are closely related [9] , we can expect that the risk of MERS death can also be similarly characterized by a set of underlying epidemiological features of the cases. While the methods for identifying the risk of death in real time during the SARS epidemic have relied on survival analysis techniques, including non-parametric Kaplan-Meier-like methods [12, 14] , there is a need to develop a simple yet tractable method which is inspired on the adjustment of censoring for CFR [4, 5] with particular application to small outbreak sizes such as the MERS outbreak in the Republic of Korea. Such a modelling framework should adjust for case ascertainment, which is widely recognized as a key issue that arises during emerging disease epidemics, includin
Keywords Extracted from PMC Text: 401.9 \exp \left({a}_0+{\displaystyle \sum_{k=1}^N{a}_k{x}_{k B inpatients \end{document}Laαβtm=∏i∈AStm−αi;pi∏i∈Bpifβi−αi \end{document}lnpi1−pi patients line joint \boldsymbol{\upbeta i}} L\left(\mathbf{a};\boldsymbol{\upalpha \end{document}pi a0+∑k=1Nakxk i}}\right)}{1+ a1 H1N1 contacts {t}_m\right)=\prod_{i\in A}S\left({t}_m-{\alpha}_i;{p}_i\right)\prod_{i\in B}\left[{p}_if\left({\beta}_i-{\alpha}_i\right)\right asvi\documentclass[12pt]{minimal byii\documentclass[12pt]{minimal f(x)}dx i1+expa0+∑k=1Nakxk coronaviruses coronavirus k-th t0ε f(τ " fort0≤t \end{document}Sτp=1−p∫0τfxdx i.e.v\documentclass[12pt]{minimal Korea [15–17 p\right)=1-p{\displaystyle Patients \kern2.75em \mathrm{f}\mathrm{o}\mathrm{r}\ {t}_0\le ⋯, aN asi\documentclass[12pt]{minimal persons δexpa0+∑k=1Nakxk Ebola virus
Extracted PMC Text Content in Record: First 5000 Characters:When a novel infectious disease emerges, it is vital to assess the potential global public health impact, i.e. the potential of a pathogen to cause a devastating pandemic [1]. The public health impact is mainly characterized by the transmission potential and virulence, the latter of which is usually measured by the case fatality ratio (CFR), namely the proportion of deaths among cases [2]. Due to the importance of generating accurate estimates of the CFR in real time as an outbreak progresses and public health interventions are implemented, a number of epidemiological methods have been proposed, including those that account for the time delay from illness onset to death [3–5] and those that correct for ascertainment bias through the combination of several different observations by employing a Bayesian evidence synthesis method [6]. Other methods have developed a slightly different CFR concept, e.g. the so-called "infection fatality risk", the ratio of excess mortality to the serologically determined infected fraction of the population [7]. The CFR is an important epidemiological quantity to estimate in real time during outbreaks, and the recent outbreak of the Middle East respiratory syndrome (MERS) in the Republic of Korea in 2015 has not been an exception [8]. Besides the MERS outbreak in the Republic of Korea, earlier studies of MERS in the Middle East have consistently indicated that the CFR for MERS has been about 40 % [9] and could be as low as 20 % for estimates that only include secondary cases [10]. Despite the availability of useful methods to estimate the CFR for MERS, it is crucial to identify risk factors associated with MERS death, as the risk of death may vary significantly with age, occupation, and underlying comorbidities [11]. During the epidemic of severe acute respiratory syndrome (SARS), a real-time analysis found that confirmed or probable cases aged 60 years or older were at greater risk of death than younger cases [12]. Moreover, infections with SARS-associated coronavirus with comorbidities were found to be 1.7 times more likely to die than those without comorbidities [13]. Due to similarities in clinical characteristics between MERS and SARS patients as both coronaviruses are closely related [9], we can expect that the risk of MERS death can also be similarly characterized by a set of underlying epidemiological features of the cases. While the methods for identifying the risk of death in real time during the SARS epidemic have relied on survival analysis techniques, including non-parametric Kaplan-Meier-like methods [12, 14], there is a need to develop a simple yet tractable method which is inspired on the adjustment of censoring for CFR [4, 5] with particular application to small outbreak sizes such as the MERS outbreak in the Republic of Korea. Such a modelling framework should adjust for case ascertainment, which is widely recognized as a key issue that arises during emerging disease epidemics, including the H1N1 pandemic in 2009 [2, 7, 8]. During the MERS outbreak in the Republic of Korea, an extensive contact tracing effort was made by public health authorities soon after the first few cases were reported, with all cases being confirmed by laboratory testing irrespective of clinical signs and symptoms. As in any outbreak, it is likely that the ascertainment rate was substantially improved as the outbreak progressed. The present study aims to statistically identify risk factors associated with MERS death in the Republic of Korea using a simple but novel statistical modelling approach. We also use our approach to assess the time dependent variation in the ascertainment of cases. The present study employs published secondary data of the confirmed MERS cases arising from the outbreak in the Republic of Korea [15–17]. As of July 31, 2015, a total of 185 cases have been diagnosed (excluding one case diagnosed and recovered in China) including 36 deceased cases. During the course of this outbreak, a detailed line list of cases has been made publicly available [15]. Data on individual cases include the (1) dates of illness onset, (2) age-group, (3) gender, and (4) background health status either as outpatient or inpatient of a specific healthcare facility. Since the dates of illness onset among those recently reported were not yet available, we relied on the dates of confirmatory diagnosis as an alternative. We believe it is reasonable to approximate the date of illness onset by the date of confirmed diagnosis in this setting, because from the midst of the outbreak, all suspected contacts have been regularly monitored and have been subjected to laboratory testing regardless of clinical signs and symptoms. We limited ourselves to handle the abovementioned covariates (2) to (4) only, not each specific individual comorbidity, because it is not clear if the presence of all common underlying comorbidities were routinely evaluated and consistently documented in the case list; the identifica
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