<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.109554.2</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Determinants of mortality risk among Indonesian patients with COVID-19</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 2; peer review: awaiting peer review]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Efendi</surname>
                        <given-names>Ferry</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-7988-9196</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Haryanto</surname>
                        <given-names>Joni</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Has</surname>
                        <given-names>Eka Mishbahatul Mar'ah</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-1786-285X</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Makhfudli</surname>
                        <given-names>Makhfudli</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Indarwati</surname>
                        <given-names>Retno</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-5596-2360</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Kuswanto</surname>
                        <given-names>Heri</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Wahyuhadi</surname>
                        <given-names>Joni</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Farabi</surname>
                        <given-names>Makhyan Jibril Al</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a4">4</xref>
                    <xref ref-type="aff" rid="a5">5</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Ho</surname>
                        <given-names>Ken Hok Man</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-4934-2450</uri>
                    <xref ref-type="aff" rid="a6">6</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Faculty of Nursing, Universitas Airlangga, Surabaya, East Java, 60115, Indonesia</aff>
                <aff id="a2">
                    <label>2</label>Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, East Java, 60111, Indonesia</aff>
                <aff id="a3">
                    <label>3</label>Department of Neurosurgery, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60132, Indonesia</aff>
                <aff id="a4">
                    <label>4</label>Dr. Soetomo General Hospital, Surabaya, East Java, 60286, Indonesia</aff>
                <aff id="a5">
                    <label>5</label>Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, 60132, Indonesia</aff>
                <aff id="a6">
                    <label>6</label>The Nethersole School of Nursing, Chinese University of Hong Kong, Hong Kong, Hong Kong</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:ferry-e@fkp.unair.ac.id">ferry-e@fkp.unair.ac.id</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>14</day>
                <month>4</month>
                <year>2023</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2022</year>
            </pub-date>
            <volume>11</volume>
            <elocation-id>814</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>12</day>
                    <month>4</month>
                    <year>2023</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2023 Efendi F et al.</copyright-statement>
                <copyright-year>2023</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/11-814/pdf"/>
            <abstract>
                <p>
                    <bold>Background:</bold> The mortality risk of the novel coronavirus disease 19 (COVID-19) remains a global concern. Particularly in Indonesia, patients aged 18&#x2013;59 years have a high risk of death due to COVID-19. In this study, we analysed the determinants of mortality risk among patients with confirmed COVID-19.</p>
                <p>
                    <bold>Methods:</bold> A secondary analysis of data from the Indonesian COVID-19 dashboard, tracking COVID-19 cases from April 2020 to May 2021 was performed. Data of 22,314 patients with COVID-19 aged 18&#x2013;59 years were analysed using descriptive analysis, chi-square test, and binary logistic regression to obtain adjusted odds ratios (AORs) with 95% confidence intervals (CIs).</p>
                <p>
                    <bold>Results:</bold> Factors associated with confirmed mortality risk among patients aged 18&#x2013;59 years with COVID-19 included increasing age (AOR = 1.08; 95% CI = 1.07&#x2013;1.09), hospitalised and on ventilator support (AOR = 130.75; 95% CI = 69.03&#x2013;247.63), having severe disease (AOR = 15.24; 95% CI = 11.51&#x2013;20.17), and travelling history (AOR = 1.36; 95% CI = 1.13&#x2013;1.63).</p>
                <p>
                    <bold>Conclusions:</bold> These findings confirmed that the increasing risk of death due to COVID-19 was associated with increasing age, ventilator support during hospitalisation, developing severe disease, and having a travelling history. This suggests that curative strategies should be strengthened with a focus on improving clinical governance by prioritising patients with the above variables.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>COVID-19</kwd>
                <kwd>health risk</kwd>
                <kwd>middle-aged adult patients</kwd>
                <kwd>mortality risk</kwd>
                <kwd>young patients</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1">
                    <funding-source>Universitas Airlangga (Top Tier Mandate Research Grant)</funding-source>
                    <award-id>768/UN3.15/PT/2021</award-id>
                </award-group>
                <funding-statement>This study was supported by Universitas Airlangga, Surabaya, Indonesia through &#x201c;Hibah Riset Mandat Top Tier&#x201d; (Top Tier Mandate Research Grant) grant number [768/UN3.15/PT/2021]. </funding-statement>
                <funding-statement>
                    <italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic>
                </funding-statement>
            </funding-group>
        </article-meta>
        <notes>
            <sec sec-type="version-changes">
                <label>Revised</label>
                <title>Amendments from Version 1</title>
                <p>1. We clarified the written method and results based on the reviewer's suggestion. 2. The method is improved by clarifying the method/statistical analysis used to evaluate each independent variable. 3. The results include a description of the univariate statistical results. 4. The method now includes an explanation of the multivariate statistical method used. 5. Each statistical analysis' appropriate p-value is expressed to four decimal places, not just a p-value threshold for significance.</p>
            </sec>
        </notes>
    </front>
    <body>
        <sec id="sec1" sec-type="intro">
            <title>Introduction</title>
            <p>The novel coronavirus disease 2019 (COVID-19) was declared a global pandemic by the 
                <ext-link ext-link-type="uri" xlink:href="https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020">World Health Organization (WHO)</ext-link>. The virus that causes COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in December 2019 in the Wuhan province of China.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup> The virus has since spread rapidly 
                <ext-link ext-link-type="uri" xlink:href="https://cdn.who.int/media/docs/default-source/searo/indonesia/covid19/external-situation-report-78_27-october-2021.pdf?sfvrsn=b6ddee6a_4">around the world</ext-link>, affecting more than 235 million people by October 2021, and causing approximately 4.8 million deaths, with the highest number of positive confirmed cases in America reaching 90 million. The first case of COVID-19 in Indonesia was detected on 2 March 2020.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>
                </sup> COVID-19 cases in Indonesia had reached 
                <ext-link ext-link-type="uri" xlink:href="https://cdn.who.int/media/docs/default-source/searo/indonesia/covid19/external-situation-report-78_27-october-2021.pdf?sfvrsn=b6ddee6a_4">approximately 4.2 million</ext-link>, with 143,299 deaths by 27 October 2021. During this period, East Java became the fourth province with the highest number of confirmed cases, having a total of 396,001 positive patients. Among them, 
                <ext-link ext-link-type="uri" xlink:href="https://covid19.go.id/">29,464 people died</ext-link>, 365,340 recovered, and 1,197 were in the process of being treated or undergoing self-isolation. Moreover, the case fatality rate from COVID-19 in East Java is known to be the highest in Indonesia, 
                <ext-link ext-link-type="uri" xlink:href="http://infocovid19.jatimprov.go.id/">having reached 7.44%</ext-link>.</p>
            <p>The national trend of COVID-19 cases in Indonesia shows that adults aged 31&#x2013;45 years are more prone to infection, followed by young adults aged 19&#x2013;30 years; however, those &#x2265; 46 years of age show the highest number of deaths and a low recovery rate.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>
                </sup> These patterns are similar to current trends in both high-income and developing countries, which report as many as 5% more deaths in patients between 20&#x2013;39 years old, and up to 23% in those between 40&#x2013;59 years old.
                <sup>
                    <xref ref-type="bibr" rid="ref3">3</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>
                </sup> Supporting these findings, several studies in 2021 revealed that the mortality of COVID-19 increases with age, which is considered to be associated with deteriorating health status or pre-existing comorbidities.
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup>
            </p>
            <p>In addition to age-related factors, mortality from COVID-19 has also been correlated with other factors, such as gender, comorbidities, and clinical condition.
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> Many studies have focused on analysing the risk factors for mortality in hospitalised patients,
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> and these were mainly associated with clinical treatment, existing chronic disease(s), type of treatment unit, and complications. However, very few studies have focused on determining the factors associated with mortality from COVID-19 in non-hospitalised patients.</p>
            <p>Therefore, the aim of this study was to analyse the predictors of mortality among hospitalised and non-hospitalised patients with COVID-19 in East Java, Indonesia.</p>
        </sec>
        <sec id="sec2" sec-type="methods">
            <title>Methods</title>
            <sec id="sec3">
                <title>Ethical considerations</title>
                <p>The datasets used in this study were analysed after all individual-level identification variables were removed. In addition, it was not possible to identify the residences of any of the subjects; therefore, ethical approval was not required for this study.</p>
            </sec>
            <sec id="sec4">
                <title>Data source</title>
                <p>Secondary data analysis from the Indonesian COVID-19 dashboard tracked COVID-19 cases from April 2020 to May 2021 (the peak case period) was performed. Data of all patients with COVID-19 aged 18&#x2013;59 years were analysed and sorted according to the determinants of mortality risks.</p>
                <p>
                    <italic toggle="yes">Sample size and sampling</italic>
                </p>
                <p>The COVID-19 Task Force of East Java has established a 
                    <ext-link ext-link-type="uri" xlink:href="https://infocovid19.jatimprov.go.id/">database</ext-link> to compile all data of COVID-19 related cases reported by health facilities since the inception of the pandemic. It is mandatory for health facilities to report COVID-19 related cases through this platform. Reported information included sociodemographic data, particularly age, gender, occupation, case category, address, treatment status, treatment location, date of tracking, treatment outcome, symptoms, diagnosis, comorbidity, travelling history, severity, and history of exposure to a suspected or confirmed case. Because the data relied on the self-reported mechanism from primary health care centres and hospitals, the accuracy and the completeness of data submitted to the platform varies across facilities.</p>
                <p>In the present study, purposive sampling of respondents aged 18&#x2013;59 years was applied. The initial extracted data included 87,162 respondents; however, only respondents with complete data, including sex, treatment status, category of illness, travelling history, and exposure to confirmed cases, were included in the data analysis. After eliminating cases that did not meet the above-mentioned criteria, a sample size of 22,314 was included in this study.</p>
                <p>
                    <italic toggle="yes">Variables and measurements</italic>
                </p>
                <p>The dependent variables in this study were either dying (death) or living (alive). Several patient-related variables, including age, sex (male and female), treatment status, category of illness, and travelling history were considered as independent variables. All of these variables were chosen as the variables related to mortality of patients with COVID-19. Treatment status was categorised into three categories: non-hospitalised, hospitalised, and hospitalised on a ventilator. The severity of COVID-19, according to the 
                    <ext-link ext-link-type="uri" xlink:href="https://covid19.go.id/storage/app/media/Regulasi/2021/Agustus/kmk-no-hk0107-menkes-5671-2021-ttg-manajemen-klinis-tata-laksana-covid-19-di-fasilitas-pelayanan-kesehatan-signed-1.pdf">Diagnosis and Treatment Protocol for COVID-19 (seventh edition)</ext-link>, is briefly divided into three categories: 1) mild (mild clinical symptoms and undetected signs of pneumonia on CT images); 2) moderate (fever and respiratory symptoms with signs of pneumonia on CT images); 3) severe (any of the following symptoms: (a) respiratory distress and requiring invasive ventilation, (b) pulse oxygen saturation &#x02c2; 93%, or (c) partial pressure of oxygen (PaO
                    <sub>2</sub>)/oxygen concentration &#x02c2; 300 mmHg).</p>
                <p>Travelling history was measured as yes or no, which determined whether a patient had travelled to other cities, and whether the areas were high-risk or not. The classification of areas was granted by the COVID-19 Task Force which was calculated based on the daily confirmed case rate. Exposure to confirmed patients was categorised as either yes or no.</p>
            </sec>
            <sec id="sec5">
                <title>Statistical analysis</title>
                <p>Statistical software of 
                    <ext-link ext-link-type="uri" xlink:href="https://www.ibm.com/uk-en/products/spss-statistics">SPSS</ext-link> 26.0 for Windows (SPSS, Inc., Chicago, IL, USA) was used for statistical analyses. To determine the mortality risk factors in patients aged 18-59 years with COVID-19, we performed univariate, bivariate, and multivariate analysis. Univariate analysis was presented in frequencies data, bivariate analysis was done using Chi-square test, and binary logistic regression test was used to multivariate analysis. A p-value of &lt; 0.05 was interpreted as indicating statistical significance. To determine the strength of the relationship between the dependent and independent variables, it was assessed using an adjusted odds ratios (AORs) with 95% confidence intervals (CIs).</p>
            </sec>
        </sec>
        <sec id="sec6" sec-type="results">
            <title>Results</title>
            <p>
                <xref ref-type="table" rid="T1">Table 1</xref> shows the results of a univariate analysis of the sociodemographic characteristics of respondents. According to the findings, the prevalence of death from COVID-19 in patients aged 18-59 years in East Java, Indonesia is 4.8%. (1065). The proportion of male and female mortality differed by 1.1%, with male patients (5.3%) having a higher prospect of succumbing to the disease than the female patients (4.2%). In addition, patients who were hospitalised on a ventilator are more likely to die (52.6%). The severity category showed that the more severe the disease is, the more patients tend to die (63.3%). Based on travel history, patients who have a high history of travelling are more likely to die (6.3%), compared to those who have no traveling history.</p>
            <p>Furthermore, patients with a history of being in contact with a confirmed COVID-19 case are less likely to die (4.7%). Based on the results of bivariate analysis, the chi-square test revealed that four independent variables were significant (p &#x02c2; 0.05) in relation to the mortality risk in patients aged 18&#x2013;59 years caused by COVID-19. Nonetheless, sex, treatment status, severity category, and travel history were significant factors associated with the incidences of COVID-19 deaths. The data are depicted in 
                <xref ref-type="table" rid="T1">Table 1</xref>.</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>Table 1. </label>
                <caption>
                    <title>Sociodemographic information of adult patients (18&#x2013;59 years old) with COVID-19 (N = 22314).</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="3" valign="top">Variables</th>
                            <th align="left" colspan="4" rowspan="1" valign="top">Confirmed cases</th>
                            <th align="left" colspan="2" rowspan="1" valign="top"/>
                        </tr>
                        <tr>
                            <th align="left" colspan="2" rowspan="1" valign="top">Alive</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">Died</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">
                                <sup/> 
                                <italic toggle="yes">p-value (X
                                    <sup>2</sup>)</italic>
                            </th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">N</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">%</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">N</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">%</th>
                            <th align="left" colspan="2" rowspan="1" valign="top"/>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Sex</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="2" rowspan="1" valign="top">0.0002
                                <xref ref-type="table-fn" rid="tfn1">*</xref> (13.53)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Male</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11507</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">94.7%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">638</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.3%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Female</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9742</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">95.8%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">427</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.2%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Treatment</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="2" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn2">**</xref> (2760.82)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Non-Hospitalised</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8518</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">99.9%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.1%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Hospitalised</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12514</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">93.9%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">812</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.1%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Hospitalised on a ventilator</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">217</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">47.4%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">241</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">52.6%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Severity</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="2" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn2">**</xref> (3359.39)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Mild</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">19044</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">96.8%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">639</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.2%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Medium</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2048</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">93.0%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">155</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.0%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Severe</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">157</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">36.7%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">271</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">63.3%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Travelling history</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td align="left" colspan="2" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn2">**</xref> (119.45)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Yes</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10420</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">93.7%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">705</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.3%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>No</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10829</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">96.8%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">360</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.2%</td>
                            <td colspan="2" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="5" rowspan="1" valign="top">
                                <bold>Exposed to suspect/confirmed person</bold>
                            </td>
                            <td align="left" colspan="2" rowspan="1" valign="top">0.4943 (0.46)</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Yes</bold>
                            </td>
                            <td align="left" colspan="2" rowspan="1" valign="top">15032</td>
                            <td align="left" colspan="2" rowspan="1" valign="top">95.3%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">743</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.7%</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>No</bold>
                            </td>
                            <td align="left" colspan="2" rowspan="1" valign="top">6217</td>
                            <td align="left" colspan="2" rowspan="1" valign="top">95.1%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">322</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.9%</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Total</bold>
                            </td>
                            <td align="left" colspan="2" rowspan="1" valign="top">21249</td>
                            <td align="left" colspan="2" rowspan="1" valign="top">95.2%</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1065</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.8%</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <fn-group content-type="footnotes">
                        <fn id="tfn1">
                            <label>*</label>
                            <p>p &lt; 0.05.</p>
                        </fn>
                        <fn id="tfn2">
                            <label>**</label>
                            <p>p &lt; 0.001.</p>
                        </fn>
                    </fn-group>
                </table-wrap-foot>
            </table-wrap>
            <p>The determinants of mortality risk among patients aged 18&#x2013;59 years are shown in the multivariate analysis table (
                <xref ref-type="table" rid="T2">Table 2</xref>). According to the data, older patients with COVID-19 were 1.08 times more likely to die than the younger patients with the range aged of 18&#x2013;59 years. Patients with COVID-19 on ventilators were 130.75 times more likely to die than patients receiving other treatment strategies. The disease category indicated that patients with a severe form of the disease were more likely to die than those with a mild or moderate diagnosis (AOR = 15.24, 95% CI = 11.51&#x2013;20.17). Based on travel history, patients with a history of travelling prior to symptom onset had a higher (1.36 times) probability of dying from the disease than those without a travel history.</p>
            <table-wrap id="T2" orientation="portrait" position="float">
                <label>Table 2. </label>
                <caption>
                    <title>Multivariate analysis of adult patients with COVID-19 in East Java Indonesia (N = 22314).</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top"/>
                            <th align="left" colspan="3" rowspan="1" valign="top">Confirmed</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">OR</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Sig.</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">95% CI</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Age</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.08</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn4">**</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.07&#x2013;1.09</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Sex</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Female 
                                    <italic toggle="yes">(Ref)</italic>
                                </bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Male</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.98</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.8066</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.85&#x2013;1.13</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Treatment</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Non-hospitalised 
                                    <italic toggle="yes">(Ref)</italic>
                                </bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Hospitalised</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27.69</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn4">**</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">15.42&#x2013;49.71</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Hospitalised and on a ventilator</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">130.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn4">**</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">69.03&#x2013;247.639</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Category of illness</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Mild 
                                    <italic toggle="yes">(Ref)</italic>
                                </bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Moderate</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.36</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0046
                                <xref ref-type="table-fn" rid="tfn3">*</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.10&#x2013;1.68</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Severe</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">15.24</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0000
                                <xref ref-type="table-fn" rid="tfn4">**</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11.51&#x2013;20.17</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Travelling history</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>No 
                                    <italic toggle="yes">(Ref)</italic>
                                </bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1</td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Yes</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.36</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.0009
                                <xref ref-type="table-fn" rid="tfn3">*</xref>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.13&#x2013;1.63</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>OR, odds ratio; CI, confidence interval.</p>
                    <fn-group content-type="footnotes">
                        <fn id="tfn3">
                            <label>*</label>
                            <p>p &lt; 0.05.</p>
                        </fn>
                        <fn id="tfn4">
                            <label>**</label>
                            <p>p &lt; 0.001.</p>
                        </fn>
                    </fn-group>
                </table-wrap-foot>
            </table-wrap>
        </sec>
        <sec id="sec7" sec-type="discussion">
            <title>Discussion</title>
            <p>The present study findings showed that age is associated with mortality from COVID-19, with the older generation being more vulnerable than the younger population. This finding is supported by studies from various countries, including New York, Mexico, and Pakistan.
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref15">15</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref16">16</xref>
                </sup> In Mexico, the prevalence of COVID-19 survivors and non-survivors was compared and the findings showed that the majority of the non-survivors were 10 years older than those who survived.
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup> The reason for this is that natural immunity and organ function tend to decline gradually with age.
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup> In addition, the social factors of adults, such as work activity, habits, and lifestyle might influence their susceptibility to death.
                <sup>
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup> Thus, it can be postulated that adult patients with COVID-19 are likely to develop more opportunistic infections, and in turn be at higher risk of mortality than the younger population group.</p>
            <p>Patients with COVID-19 who were on ventilator support had a high mortality rate because of reduced pulmonary function. This finding is consistent with that of a study from Pakistan, which reported that 86% of patients that died from COVID-19 required invasive ventilator support.
                <sup>
                    <xref ref-type="bibr" rid="ref16">16</xref>
                </sup> Several countries, states, and provinces reported a high mortality rate in COVID-19 patients on ventilators, including Italy (53.4%), the United Kingdom (67%), New York (88%), and Wuhan (97%).
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref21">21</xref>
                </sup> Based on these findings, the use of ventilators in the management of COVID-19 should be aligned with patient symptoms since acute respiratory failure in COVID-19 is unique and poorly understood.
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref23">23</xref>
                </sup> This suggests that clinicians must understand the different settings of the ventilator to match disease severity when treating patients with acute respiratory symptoms associated with COVID-19.</p>
            <p>Patients with severe COVID-19 had a higher probability of dying than those in the moderate and mild categories. Patients with severe illness might have been exposed to SARS-CoV-2 longer than those in the other disease categories, resulting in the development of an inflammatory response.
                <sup>
                    <xref ref-type="bibr" rid="ref24">24</xref>
                </sup> The progression of disease in patients with severe infection is important in understanding disease prognosis because these patients tend to experience several symptoms, including acute respiratory distress, coagulopathy, acute respiratory failure, and metabolic acidosis.
                <sup>
                    <xref ref-type="bibr" rid="ref25">25</xref>
                </sup> A study confirmed more patients with severe disease required intensive care and treatment, the lack of which resulted in their death.
                <sup>
                    <xref ref-type="bibr" rid="ref26">26</xref>
                </sup>
            </p>
            <p>Lastly, the majority of deaths in patients aged 18&#x2013;59 years with COVID-19 were associated with having a travelling history, meaning that infection from SARS-CoV-2 occurred during travelling. Study findings from research in Iran is in accordance with this observation, in which Iranian women with a travelling history had 8.8% higher infection rate than those who did not travel, especially those who visited high-risk areas.
                <sup>
                    <xref ref-type="bibr" rid="ref27">27</xref>
                </sup> Other studies concluded that mobility could be a contributing factor in the transmission of COVID-19 among people.
                <sup>
                    <xref ref-type="bibr" rid="ref28">28</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref29">29</xref>
                </sup>
            </p>
        </sec>
        <sec id="sec8">
            <title>Limitations</title>
            <p>Since our study used secondary data, the use of specific built-in variables could have led to focus bias. In addition, some individuals who might have developed symptoms and did not report to treatment centres could not be included in the study.</p>
        </sec>
        <sec id="sec9" sec-type="conclusions">
            <title>Conclusions</title>
            <p>Mortality risk from COVID-19 among patients aged 18&#x2013;59 years has been correlated with various factors, specifically, age, treatment, severity of illness, and travelling history. Reduction in mortality rate might be achieved by enhancing precision treatment and broadening prevention at both clinical and community levels.</p>
        </sec>
        <sec id="sec10">
            <title>Data availability</title>
            <sec id="sec11">
                <title>Underlying data</title>
                <p>The dataset used in this study can be accessed upon request to the East Java Task Force of COVID-19 Program website (
                    <ext-link ext-link-type="uri" xlink:href="https://infocovid19.jatimprov.go.id/">https://infocovid19.jatimprov.go.id/</ext-link>). All of the requests should be directed to the task force through the contact at their website (Call Center 1500 117 or e-mail at 
                    <email xlink:href="mailto:humas.dinkesjatim@gmail.com">humas.dinkesjatim@gmail.com</email>) and are subject to the approval of their team. The name of the dataset is East Java COVID-19 data and access to the data would normally be granted by the task force team for research purposes.</p>
            </sec>
        </sec>
    </body>
    <back>
        <ref-list>
            <title>References</title>
            <ref id="ref1">
                <label>1</label>
                <mixed-citation publication-type="journal">
                    <person-group person-group-type="author">

                        <name name-style="western">
                            <surname>Umakanthan</surname>
                            <given-names>S</given-names>
                        </name>

                        <name name-style="western">
                            <surname>Sahu</surname>
                            <given-names>P</given-names>
                        </name>

                        <name name-style="western">
                            <surname>Ranade</surname>
                            <given-names>AV</given-names>
                        </name>

                        <etal/>
</person-group>:
                    <article-title>Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19).</article-title>
                    <source>

                        <italic toggle="yes">Postgrad Med J.</italic>
</source>
                    <year>2020</year>;<volume>96</volume>:<fpage>753</fpage>&#x2013;<lpage>758</lpage>.
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