Self-reported symptoms in healthy young adults to predict potential COVID-19 disease

Published:January 05, 2021DOI:https://doi.org/10.1016/j.cmi.2020.12.028

      Abstract

      Objective

      To assess the utility of self-reported symptoms in identifying positive COVID-19 cases among predominantly healthy young adults, in a military setting.

      Methods

      A questionnaire regarding COVID-19 symptoms and exposure history was administered to all individuals contacting the Israeli Defense Forces “Corona call-center”, prior to polymerase chain reaction (PCR) testing. Surveyed symptoms included cough, fever, sore throat, rhinorrhea, loss of taste or smell, chest pain and gastrointestinal symptoms. Factors were compared between positive and negative cases based on confirmatory test results, and positive likelihood ratios (LR) were calculated. Results were stratified by sex, BMI, previous medical history and dates of questioning and a multivariable analysis for association with positive test was conducted.

      Results

      Of 24,362 respondents, 59.1% were men with a median age of 20.5 years (IQR 19.6-22.4). Significant positive LRs were associated with loss of taste or smell (LR 3.38, 95%CI 3.01-3.79), suspected exposure (LR 1.33, 95%CI 1.28-1.39) and fever (LR 1.26, 95%CI 1.17-1.36). Those factors were also associated with positive PCR result in a multivariable analysis (OR 3.51 (3.04-4.06), OR 1.86 (1.65-2.09) and OR 1.34 (1.19-1.51), respectively). Reports of loss of taste or smell increased gradually over time and were significantly more frequent at the late period of the study (63/5,231 (1.21%), 156/7,941 (1.96%), and 1,505/11,190 (13.45%), p<0.001).

      Conclusion

      Loss of taste or smell, report of a suspicious exposure and fever (>37.5ºC) were consistently associated with positive LRs for a positive SARS-CoV-2 PCR test result, in a population of predominantly young and healthy adults.

      Introduction

      Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first reported in China in December 2019 [
      • Lu H.
      • Stratton C.W.
      • Tang Y.W.
      Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle.
      ] and by March 11th 2020 declared as a pandemic [
      • Organisation W.H.
      WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020.
      ]. To date, the reported disease burden exceeds 71million people, with a death toll of approximately 1,600,000 worldwide [

      WHO. WHO Coronavirus Disease Dashboard. Who 2020.

      ].
      Insufficient viral RNA testing capacity [
      • Esbin M.N.
      • Whitney O.N.
      • Chong S.
      • Maurer A.
      • Darzacq X.
      • Tjian R.
      Overcoming the bottleneck to widespread testing: A rapid review of nucleic acid testing approaches for COVID-19 detection.
      ,
      • Péré H.
      • Péré H.
      • Péré H.
      • Podglajen I.
      • Podglajen I.
      • Wack M.
      • et al.
      Nasal swab sampling for SARS-CoV-2: A convenient alternative in times of nasopharyngeal swab shortage.
      ,

      Beetz C, Skrahina V, Förster TM, Gaber H, Paul JJ, Curado F, et al. Rapid Large-Scale COVID-19 Testing During Shortages. Diagnostics (Basel, Switzerland) 2020;10. https://doi.org/10.3390/diagnostics10070464.

      ], which results in propagation of the virus by undiagnosed infected individuals [

      Han C, Duan C, Zhang S, Spiegel B, Shi H, Wang W, et al. Digestive Symptoms in COVID-19 Patients With Mild Disease Severity: Clinical Presentation, Stool Viral RNA Testing, and Outcomes 2020. https://doi.org/10.14309/ajg.0000000000000664.

      ,
      • Gandhi M.
      • Yokoe D.S.
      • Havlir D.V.
      Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19.
      ] cripples the ability of health authorities to control viral spread [
      • Xie J.
      • Tong Z.
      • Guan X.
      • Du B.
      • Qiu H.
      • Slutsky A.S.
      Critical care crisis and some recommendations during the COVID-19 epidemic in China.
      ,
      • Grasselli G.
      • Pesenti A.
      • Cecconi M.
      Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy.
      ]. Thus, prioritizing testing and efficiently isolating positive cases is a vital aim. To this end, several studies examined the utility of self-reported symptoms in predicting COVID-19 and found a correlation between constitutional and respiratory symptoms with a positive test [
      • Shoer S.
      • Karady T.
      • Keshet A.
      • Shilo S.
      • Rossman H.
      • Gavrieli A.
      • et al.
      Who should we test for COVID-19? A triage model built from national symptom surveys.
      ,
      • Tostmann A.
      • Bradley J.
      • Bousema T.
      • Yiek W.-K.
      • Holwerda M.
      • Bleeker-Rovers C.
      • et al.
      Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.
      ,
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ]. However, low response rates in young healthy adults [
      • Tostmann A.
      • Bradley J.
      • Bousema T.
      • Yiek W.-K.
      • Holwerda M.
      • Bleeker-Rovers C.
      • et al.
      Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.
      ,
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ] and their exclusion from studies [
      • Shoer S.
      • Karady T.
      • Keshet A.
      • Shilo S.
      • Rossman H.
      • Gavrieli A.
      • et al.
      Who should we test for COVID-19? A triage model built from national symptom surveys.
      ], results in a dearth of data guiding testing in this population. As the proportion of young infected adults increases [
      • Salvatore P.P.
      • Sula E.
      • Coyle J.P.
      • PhD
      • Caruso E.
      • Smith A.R.
      • et al.
      Recent Increase in COVID-19 Cases Reported Among Adults Aged 18–22 Years — United States, May 31–September 5, 2020.
      ,

      Simchoni M, Klopstock I, Furer A. COVID-19 among Young Adults. vol. 17. 2020.

      ,
      Report on the epidemiological features of coronavirus disease 2019 (covid-19) outbreak in the republic of korea from january 19 to march 2, 2020.
      ], and their potential in propagating disease spread grows [
      • Gandhi M.
      • Yokoe D.S.
      • Havlir D.V.
      Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19.
      ,

      Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy n.d. https://doi.org/10.1038/s41591-020-0883-7.

      ,
      • Gao Z.
      • Xu Y.
      • Sun C.
      • Wang X.
      • Guo Y.
      • Qiu S.
      • et al.
      A systematic review of asymptomatic infections with COVID-19.
      ], there is an impending need to better characterize the symptom distribution suggestive of infection in this group.
      The Israel Defense Forces (IDF) is comprised mostly of young healthy individuals who have undergone extensive medical screening [
      • Furer A.
      • Afek A.
      • Sommer A.
      • Keinan-Boker L.
      • Derazne E.
      • Levi Z.
      • et al.
      Adolescent obesity and midlife cancer risk: a population-based cohort study of 2·3 million adolescents in Israel.
      ]. Therefore, in this study we used a retrospective cohort design to evaluate the association between symptoms in young adults and a positive test result.

      Methods

       Study population

      The special IDF COVID-19 center (ICC) handled documentation of all individuals with suspicious symptoms, those quarantined, and all confirmed COVID-19 cases. This included collection of data obtained via contact tracing and an inquiries 'hotline'. ICC referred soldiers for swab tests based on report of symptoms and exposure history. We included all individuals who were deemed eligible for COVID-19 testing by the ICC, including those voluntarily calling to report symptoms or a suspected exposure, or those actively addressed following an epidemiological investigation. Eligibility for confirmatory testing was directed by the surgeon general and approved by public health officers on a case-by-case basis. Testing was approved for those with: 1) presence of at least two acute respiratory symptoms (cough, shortness of breath, sore throat, fever > 38C); or 2) one symptom combined with a suspicious exposure; or 3) loss of taste or smell as a sole symptom.

       Primary outcome

      The primary outcome measure was the positive likelihood ratio (LR) of symptoms suggesting a positive real-time reverse transcriptase–polymerase chain reaction (rRT-PCR) result.

       Data collection

      Answers to questions asked by ICC prior to swab testing were drawn from a digital database where each individual has a unique identification number. Symptoms surveyed included: cough, fever (>37.5°C), sore throat, rhinorrhea, loss of taste or smell, chest pain and gastrointestinal symptoms (GI, i.e. abdominal pain, vomiting, or diarrhea). Suspected exposure was defined as close contact with a confirmed COVID-19 patient or recent (<14 days) international travel (questionnaire example pg.2, supplement). Data were collected between March 26th and August 2nd, 2020. A group of 6,643 individuals had COVID-19 tests without answering the questionnaires thus symptoms and exposure history data was unavailable and they were excluded. PCR test results were merged using the identification number from a digital laboratory database and classified as either positive or negative. For positive cases, only the first positive test was included, and data were collected only from the questionnaire obtained prior to this test. The following covariates were abstracted from the medical registry: BMI, baseline health and comorbidities, as detailed in supplement extended methods.

       Viral RNA test

      Viral RNA presence was examined using rRT-PCR based on SARS-CoV-2 Centers for Disease Control and Prevention (CDC) protocol [

      CDC. CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel- For Emergency Use Only. 2020.

      ]; using viral markers for each of the three viral genes (RdRP, N gene and E gene). Cycle threshold values were reported, as well as an internal control marker. A threshold of 40 cycles and below was considered positive. Results in which only N gene was present within the range of 36-40 cycles, were defined as marginally-positive. In those cases, a certified physician and a public-health officer interpreted the result according to the clinical context, and the individual was re-tested if deemed necessary. Additional details on testing methods are available elsewhere [
      • Moore N.M.
      • Li H.
      • Schejbal D.
      • Lindsley J.
      • Hayden M.K.
      Comparison of two commercial molecular tests and a laboratory-developed modification of the CDC 2019-ncov reverse transcriptase PCR assay for the detection of sars-cov-2.
      ]. All swab materials were processed in the central IDF lab, throughout the entire study.

       Statistical analysis

      Prevalence of each reported symptom and exposure was compared between positive and negative rRT-PCR groups. Continuous variables were presented as medians and interquartile ranges, and categorical variables were presented as percentages and counts. Positive LR, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and confidence intervals (CI) for test statistics were calculated using the methods of Simel [
      • Simel D.L.
      • Samsa G.P.
      • Matchar D.B.
      Likelihood ratios with confidence: sample size estimation for diagnostic test studies.
      ] and Wilson [
      • Wilson E.B.
      Probable Inference, the Law of Succession, and Statistical Inference.
      ]. Results were stratified by sex, BMI category, previous medical history and dates of questioning. Univariate and multivariable logistic regression models were performed to assess the relationship between a positive PCR test result (dependent variable) and clinical (asthma, chronic sinusitis, hypertension and BMI), anamnestic (report of symptoms and exposure) and demographic factors (sex and age - analyzed as continuous variable and as categorical ≤20; 21-22; ≥23) as independent variables. Six statistically significant independent variables were included in multivariable analysis and selected using forward stepwise method (p-in 0.05; p-out 0.10). No collinearity was found among independent variables included in multivariable analysis, maximum variance inflation factor = 1.039. Odds ratio (OR), 95% CIs and p-values were presented.
      To assess trends in symptoms distribution over time, we selected three consequent periods to represent different phases of the pandemic: the initial COVID-19 outbreak, mitigation of disease spread and the increased disease burden following the reopening of communities, as shown in Figure 2b. For each symptom, ANOVA was used to compare the proportion of individuals reporting its presence between periods. Since no homogeneity of variances was found, multiple comparisons were done with post hoc Dunnett T3 test.
      Figure 2
      Figure 2(a) Comparison of the report rate of surveyed factors along time. Presented here are factors that were associated with significant positive likelihood ratios to COVID-19 disease (see text). In general, overall report rates of suspected exposure and fever were parallel to the extent of disease spread in the country, i.e. decreasing between the first and second period, and raising drastically in the third period. All of these changes were statistically significant (all p≤0.038). The exception is reports of loss of smell or taste which increased gradually and significantly along time (all p<0.001), (see for elaborated results of statistical analysis). (b) Description of the time sections selected for comparison. This figure details the three consequent periods selected for comparison with the number of overall study participants in each. The background represents COVID-19 disease burden in Israel, as measured by daily-confirmed cases between February 29th and August 2nd, according to the reports of the World Health Organization (WHO). Green-period 1: March 26th-April 30th, represents the initial COVID-19 outbreak. Yellow-period 2: May 1st-June 30th, represents mitigation of disease spread. Red-period 3: July 1st -August 2nd, represents the increased disease burden following the reopening of communities. The second period is about twice as long, due to relatively small number of cases throughout May and we therefore summed those with the cases identified in the consequent month.
      P-values <0.05 and CI exclusive of the null were considered statistically significant. Statistical analyses were conducted using Microsoft Excel (version 2013) and IBM SPSS Statistics for Windows (version 25.0).
      The institutional review board of the IDF approved this study (protocol number 2082-2020), and waived the requirement for written informed consent based on preserving participants' anonymity.

      Results

      Overall, 31,155 individuals were identified between March 26th and August 2nd. We excluded those with no recorded result, or results processed in an external lab (n=150) and individuals with missing questionnaire items (n=6,643, 21.4%) (Fig. 1, see Table S1 for comparison with the included cohort). Ultimately, 24,362 respondents were included in the analysis. Over half (59.1%) were men, with a median age of 20.5 (IQR, 19.6-22.4) years of whom 65% (n=15,534) had unimpaired health at baseline. Of all PCR tests, 5.5% (n=1,338) were positive, 5.7% in men and 5.2% in women. During the first, second and third periods of the study, a total of 5,231, 7,941, and 11,190 tests were conducted with a 2.25% (n=118), 2.29% (n=182) and 9.27% (n=1,038) positivity rate, respectively. Characteristics of the study population are shown in table 1.
      Figure 1
      Figure 1Study population flow chart. Numbers represent respondents. A total of 31,155 individuals were identified between March 26th and August 2nd. Those with no recorded PCR test result, or tests processed in an external lab (n=150) were excluded. Questionnaires were not performed among 6,643 (21.4%) individuals thus their exposure and symptoms data were missing, resulting in their exclusion from the analysis, which ultimately included 24,362 respondents with a PCR test result and questionnaire data.
      Table 1Baseline characteristics of study population
      Overall(n=24,362)Positive cases(n,% of overall)(n=1,338, 5.5)Negative cases(n, % of overall)(n=23,024)p-value
      Age (years); Median, (IQR)20.5 (19.6-22.4)21 (20-23)21 (20-23)p=0.426
      Male sex (%)14,398 (59.1)817 (61.1)13,577 (59.0)p=0.075
      Unimpaired health
      a Definition and method for determination of unimpaired health is detailed in supplementary material.
      (%)
      15,534 (63.7)869 (64.9)14,487 (62.9)p=0.135
      Asthma
      b History of childhood asthma, with no use of inhaler for 3 or more years, and normal pulmonary function tests, do not affect medical fitness.
      1,786 (7.3)(6.7)901,696 (7.4)p=0.383
      Allergic rhinitis1,756 (7.2)(6.2) 83(7.3) 1,673p=0.144
      Chronic sinusitis(0.05) 11(0.07) 1(0.04) 10p=0.463
      Hypertension(0.22) 55(0.07) 1(0.23) 54p=0.371
      BMI
       BMI<18.52,477 (10.2)(10.5) 140(10.15) 2,337p=0.713
       BMI 18.5-24.915,356 (63.0)(63.6)851(62.9) 14,505p=0.657
       BMI 25-29.93,438 (14.1)(12.9) 172(14.2) 3,266p=0.174
       BMI≥301,309 (5.4)(4.8) 64(5.4) 1,245p=0.325
      a Definition and method for determination of unimpaired health is detailed in supplementary material.
      b History of childhood asthma, with no use of inhaler for 3 or more years, and normal pulmonary function tests, do not affect medical fitness.

       Reported symptoms

      Cough was the most commonly reported symptom (13,675/24,362, 56.1%), followed by report of suspected exposure (12,211/24,362, 50.1%) and fever (6,896/24,362, 28.3%). The item with highest sensitivity (65.5% (63-68.1%)) was suspected exposure, followed by cough (55.5% (52.9-58.2%)) and fever (35.1% (32.6-37.7%)). Highest specificity was seen with GI symptoms (98.6% (98.5-98.8%)), followed by chest pain (98.2% (98.1-98.4%)) and loss of taste or smell (93.7% (93.4-94.1%)). Loss of taste or smell was also found to have the highest PPV (16.4% (14.8-17.9%)). Suspected exposure was found to have the highest NPV (96.2% (95.9-96.5%)) followed by loss of taste or smell (95.3% (95.2-95.5%)) and fever (95% (94.8-95.2%)). The overall distribution of reports on surveyed factors, sensitivity, specificity, PPV and NPV, are summarized in Table 2.
      Table 2Reasons for contacting the IDF COVID-19 call center and sensitivity, specificity, positive and negative predictive values for each (n=24,362).
      Prevalence (%)Sensitivity (95%CI)Specificity (95%CI)Positive predictive value (95%CI)Negative predictive value (95%CI)
      Suspected exposure12,211 (50.1)65.6%(63-68.1%)50.8% (50.1-51.4%)7.2%(6.9-7.5%)96.2% (95.9-96.5%)
      Cough13,675 (56.1)55.5%(52.9-58.2%)43.8%(43.2-44.5%)5.4%(5.2-5.7%)94.4% (94.1-94.7%)
      Fever >37.5°c6,896 (28.3)35.1%(32.6-37.7%)72.1%(71.5-72.7%)6.8%(6.3-7.3%)95.0% (94.8-95.2%)
      Sore throat5,559 (22.8)21.2%(19.1-23.4%)77.1%(76.5-77.6%)5.1%(4.6-5.6%)94.4% (94.2-94.5%)
      Rhinorrhea2,298 (9.4)7.9%(6.5-9.4%)90.5%(90.1-90.9%)4.6%(3.8-5.5%)94.4% (94.3-94/5%)
      Loss of taste or smell1,723 (7.1)21.2%(19.1-23.4%)93.7%(93.4-94.1%)16.4%(14.8-17.9%)95.3% (95.2-95.5%)
      Chest pain430 (1.8)1.8%(1.2-2.7%)98.2%(98.1-98.4%)5.6%(3.8-8.2%)94.5% (94.46-94.54)
      GI symptoms339 (1.4)1.6%(1-23.9%)98.6%(98.5-98.8%)6.2%(4.1-9.3%)94.5% (94.47-94.55%)
      Suspected exposure in the survey was defined as a close contact with a confirmed COVID-19 patient or a recent (<14 day) international travel;IDF- Israel Defense Forces. GI- gastrointestinal symptoms i.e. abdominal pain, vomiting, diarrhea.
      Comparing the distribution of reports over time (Fig. 2a, Tables S2-S3) demonstrated that suspected exposure decreased between first and second periods (2,303/5,231 (44.0%), 2,411/7,941 (30.4%) respectively, p<0.001), and was significantly higher at the third period (7,498/11,190 (67.0%), p<0.001). The same trend was seen with reports of cough (2,747/5,321 (52.5%), 3,591/7,941 (45.2%), 7,337/11,190 (65.6%), p<0.001) and fever (1,370/5,321 (26.2%), 1,927/7,941 (24.3%), 3,598/11,190 (32.2%), p=0.038, p<0.001). For loss of taste or smell, a significant gradual increase in reporting during the three study periods occurred (63/5,321 (1.2%), 156/7,941 (1.96%), 1,505/11,190 (13.5%), all p <0.001). For context, disease burden in Israel during this timeline decreased between the first and second periods and increased drastically in the third period (Fig. 2b).

       Likelihood Ratio

      Of symptoms surveyed, loss of taste or smell had highest positive LR (LR 3.38, 95%CI 3.01-3.79). Suspected exposure (LR 1.33, 95%CI 1.28-1.39) and fever (LR 1.26, 95%CI 1.17-1.36) were also found to have significant positive LR (Fig. 3). Stratification by sex, baseline health status, particular comorbidities and BMI did not principally affect the results (Table S4-S9, Fig. S1).
      Figure 3
      Figure 3Positive likelihood ratios for surveyed factors (n=24,362). Factors are shown on a logarithmic scale with 95% confidence interval (CI), those positively associated with COVID-19 disease are report of suspected exposure (see text for definition), fever >37.5ºC, and loss of smell or taste. LR-likelihood ratio. Error bars represent 95%CI.
      Loss of taste or smell (LR 11.25, 95%CI 6.28-20.13) and suspected exposure (LR 1.44, 95%CI 1.25-1.70) were significantly associated with a positive test during the first period. During the second and third periods, the positive LR for loss of taste or smell decreased substantially yet remained significant (LR 2.62, 95%CI 2.36-5.05, LR 2.05, 95%CI 1.83-2.31, respectively) (Table 3). Fever was also found to have significant positive LR (LR 1.4, 95%CI 1.2-1.3) during the third period while exposure shown significant positive LR in both earlier periods but not in the third, accompanied by lower specificity in the latest period (Table S2).
      Table 3Positive likelihood ratios (LR) for symptoms and exposure in three consequent periods (see Fig. 2b legend for details).
      LR Period 1

      March 26th-April 30th
      LR Period 2

      May 1st-June30th
      LR Period 3

      July 1st-August 2nd
      Suspected exposure1.44 (1.25-1.66)1.85 (1.61-2.11)1.01 (0.97-1.06)
      Cough0.97 (0.81-1.16)0.93 (0.79-1.11)0.88 (0.84-0.93)
      Fever0.84 (0.6-1.18)1.18 (0.94-1.49)1.2 (1.1-1.3)
      Sore throat0.85 (0.47-1.55)1.09 (0.86-1.38)0.79 (0.7-0.89)
      Rhinorrhea0.92 (0.52-1.63)0.83 (0.53-1.32)0.93 (0.75-1.17)
      Loss of taste or smell11.25 (6.28-20.13)2.62 (1.36-5.05)2.05 (1.83-2.31)
      Chest pain1.07 (0.27-4.31)0.71 (0.1-5.12)0.77 (0.5-1.2)
      GI symptoms1.98 (0.27-14.5)0.44 (0.06-3.14)0.93 (0.59-1.49)
      LR-positive likelihood ratio; GI- gastrointestinal symptoms (i.e. abdominal pain, vomiting, or diarrhea).
      In a multivariable logistic regression loss of taste or smell (OR 3.51, 95%CI 3.04-4.06), fever (OR 1.34, 95%CI 1.19-1.51) and history of exposure (OR 1.86. 95%CI 1.65-2.09) were the only factors independently associated with a positive test (Table S10).

      Discussion

      In order to make better use of limited COVID-19 confirmatory tests and identify those at highest risk for infection, in Israel, like in many other countries, certain criteria were used to determine testing eligibility [

      Home Page, Ministry of Health n.d. https://www.health.gov.il/English/Pages/HomePage.aspx (accessed August 21, 2020).

      , ,

      Coronavirus: rolling out community testing for covid-19 in the NHS - The BMJ n.d. https://blogs.bmj.com/bmj/2020/02/17/coronavirus-rolling-out-community-testing-for-covid-19-in-the-nhs/(accessed August 21, 2020).

      ]. In this study, suspicious exposure, loss of taste or smell and fever were associated with a COVID-19 molecular diagnosis in over 24,000 young adults suggesting that self-reported symptoms are an effective way to triage testing in this population.
      Previous reports have been inconsistent with respect to the association between fever and a positive test [
      • Shoer S.
      • Karady T.
      • Keshet A.
      • Shilo S.
      • Rossman H.
      • Gavrieli A.
      • et al.
      Who should we test for COVID-19? A triage model built from national symptom surveys.
      ,
      • Tostmann A.
      • Bradley J.
      • Bousema T.
      • Yiek W.-K.
      • Holwerda M.
      • Bleeker-Rovers C.
      • et al.
      Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.
      ,
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ]. These conflicting data may stem from the inclusion of populations with a variety of age groups, a factor shown to affect clinical manifestation and disease severity [
      • Riccardo F.
      • Ajelli M.
      • Andrianou X.
      • Bella A.
      • Del Manso M.
      • Fabiani M.
      • et al.
      Epidemiological characteristics of COVID-19 cases in Italy and estimates of the reproductive numbers one month into the epidemic.
      ,
      • Wiersinga W.J.
      • Rhodes A.
      • Cheng A.C.
      • Peacock S.J.
      • Prescott H.C.
      Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.
      ]. Varying definitions of fever between studies, and different survey components (where in some body-temperature was not a mandatory question) also contribute to unequivocal findings. In the current study fever >37.5C was significantly associated with COVID-19 diagnosis. Yet, when three consequent periods were compared, fever had significant positive LR only in the late period. This could represent lack of power in earlier periods due to smaller participants number, or true increase in fever as a manifestation of disease. Nonetheless, given the high likelihood observed we suggest prioritizing febrile or sub-febrile individuals for testing especially if they are young and healthy at baseline. This recommendation should be taken with caution as the specificity of fever was 72% in our study and we hypothesize that with upsurge in seasonal cold and flu during wintertime this will decrease.
      Our results suggest exposure was associated with disease only in earlier periods and not in the latest one. When considering the countrywide disease burden, it is evident that by the third period infection was widespread and much more prevalent. Thus, naturally, exposures were more probable and overall reports increased. However, compliance with measures taken to decrease transmission, such as mandated mask use in public places, and stricter enforcement perhaps resulted in many of these contacts being less endangering in the latest period. Based on our findings and the relatively low specificity of 51%, it is reasonable to refine the definition of an exposure to achieve better specificity in these circumstances.
      In general, we found symptom-reporting to be time dependent and mostly parallel to disease propagation in the country [

      WHO. WHO Coronavirus Disease Dashboard. Who 2020.

      ]. This trend was notable among positive cases but also within the entire cohort, implying a possible behavioral component. The phenomenon of healthy concerned individuals seeking medical attention was demonstrated previously in the setting of different catastrophic events and natural disasters [
      • Lacy T.J.
      • Benedek D.M.
      Terrorism and weapons of mass destruction: managing the behavioral reaction in primary care. (Review Article).
      ], and our findings suggest similar behavior during the COVID-19 pandemic. Notably, reporting of loss of taste or smell, increased over time, resulting in a decline in the magnitude of the positive LR. Among possible explanations is the increased media coverage and public recognition of this complaint as suggestive of COVID-19. Previously an opposite phenomenon was reported, where with mounting media reports of loss of smell the association between this complaint and a positive test increased [
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ]. The increase in loss of taste or smell in our cohort could have been attributed to an increase in prevalence of a different illness manifesting with these symptoms, but it is highly unlikely that this could explain a nine-fold increase within a month. Nonetheless, loss of taste or smell remain significantly and consistently associated with a positive likelihood for COVID-19 disease, and fair PPV, despite the possible reporting bias. As this is a specific complaint [
      • Salmon Ceron D.
      • Bartier S.
      • Hautefort C.
      • Nguyen Y.
      • Nevoux J.
      • Hamel A.L.
      • et al.
      Self-reported loss of smell without nasal obstruction to identify COVID-19. The multicenter Coranosmia cohort study.
      ] reaching as high as 94% in our cohort, unlike other flu-like symptoms, we suggest that young individuals presenting with loss of taste or smell should be prioritized for testing. In contrast to previous reports [
      • Shoer S.
      • Karady T.
      • Keshet A.
      • Shilo S.
      • Rossman H.
      • Gavrieli A.
      • et al.
      Who should we test for COVID-19? A triage model built from national symptom surveys.
      ,
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ] we did not find respiratory symptoms to be associated with a positive test.
      Our study includes a unique cohort comprised mainly of young healthy adults, a subgroup that has been overlooked in previous reports [
      • Shoer S.
      • Karady T.
      • Keshet A.
      • Shilo S.
      • Rossman H.
      • Gavrieli A.
      • et al.
      Who should we test for COVID-19? A triage model built from national symptom surveys.
      ,
      • Tostmann A.
      • Bradley J.
      • Bousema T.
      • Yiek W.-K.
      • Holwerda M.
      • Bleeker-Rovers C.
      • et al.
      Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.
      ,
      • Menni C.
      • Valdes A.M.
      • Freidin M.B.
      • Sudre C.H.
      • Nguyen L.H.
      • Drew D.A.
      • et al.
      Real-time tracking of self-reported symptoms to predict potential COVID-19.
      ] but is the most common disease transmitting vector [
      • Gandhi M.
      • Yokoe D.S.
      • Havlir D.V.
      Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19.
      ,

      Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy n.d. https://doi.org/10.1038/s41591-020-0883-7.

      ,
      • Gao Z.
      • Xu Y.
      • Sun C.
      • Wang X.
      • Guo Y.
      • Qiu S.
      • et al.
      A systematic review of asymptomatic infections with COVID-19.
      ]. Given the mandatory duty to serve in the IDF, the included population is highly representative of young Israeli adults and can be generalized to young adults worldwide. Additional strengths are the systemic data collection and measurement throughout the study period and the consistency of lab exams performed in the same protocol, facility and by the same staff. In addition, the free access to health services in the military is of importance in light of evidence linking socioeconomic and racial disparities to COVID-19 morbidity and mortality [
      • Wiersinga W.J.
      • Rhodes A.
      • Cheng A.C.
      • Peacock S.J.
      • Prescott H.C.
      Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.
      ]. A major shortcoming of this study is the use of self-reported symptoms which is subject to response bias, especially when presence of certain symptoms serves as the gatekeeper for PCR approval. In addition, risk for selection bias exists secondary to testing policy, which could underestimate the predictive value of symptoms that do not mandate a swab test. Furthermore, the group of excluded individuals without questionnaire data might bias the results despite being non-differential in origin and with similar characteristics overall. Also, due to the limitations of the SARS-CoV-2 rRT-PCR exam, both false negative and false positive results could be included, however, PCR remains is the gold standard for diagnosis. Finally, secondary to questionnaire phrasing loss of taste and loss of smell are combined to a single symptom, thus we could not appreciate the individual effect of report of either symptom.
      In conclusion, reporting of loss of taste or smell, suspicious exposure and fever are strongly associated with a positive SARS-CoV-2 test in a population of predominantly young and healthy adults. We recommend using theses self-reported symptoms to streamline testing in this population.

      Funding

      None.

      Conflict of Interest

      None declared.

      Author contribution

      Conceptualization: Ariel furer, Maya Nitecki.
      Methodology: Ariel furer, Maya Nitecki, Boris Taran.
      Formal Analysis: Estela Derazne.
      Data Curation: Ariel Furer, Roey Yosef, Itay Toledo, Maya Nitecki.
      Investigation: Ariel Furer, Maya Nitecki, Boris Taran.
      Writing –Original Draft: Maya Nitecki.
      WritingReview & Editing: Maya Nitecki, Boris Taran, Itay Ketko, Gil Geva, Gilad twig, Barak Gordon, Eva Abramovitch, Noam Fink.
      Visualization: Maya Nitecki, Ariel Furer, Gilad twig.
      Supervision: Ariel Furer, Gilad twig, Noam Fink, Barak Gordon, Eva Abramovitch.
      Project Administration: Ariel Furer.

      Appendix A. Supplementary data

      The following is the Supplementary data to this article:

      References

        • Lu H.
        • Stratton C.W.
        • Tang Y.W.
        Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle.
        J Med Virol. 2020; 92: 401-402https://doi.org/10.1002/jmv.25678
        • Organisation W.H.
        WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020.
        WHO Dir Gen Speeches. 2020; : 4
      1. WHO. WHO Coronavirus Disease Dashboard. Who 2020.

        • Esbin M.N.
        • Whitney O.N.
        • Chong S.
        • Maurer A.
        • Darzacq X.
        • Tjian R.
        Overcoming the bottleneck to widespread testing: A rapid review of nucleic acid testing approaches for COVID-19 detection.
        RNA. 2020; 26: 771-783https://doi.org/10.1261/rna.076232.120
        • Péré H.
        • Péré H.
        • Péré H.
        • Podglajen I.
        • Podglajen I.
        • Wack M.
        • et al.
        Nasal swab sampling for SARS-CoV-2: A convenient alternative in times of nasopharyngeal swab shortage.
        J Clin Microbiol. 2020; 58https://doi.org/10.1128/JCM.00721-20
      2. Beetz C, Skrahina V, Förster TM, Gaber H, Paul JJ, Curado F, et al. Rapid Large-Scale COVID-19 Testing During Shortages. Diagnostics (Basel, Switzerland) 2020;10. https://doi.org/10.3390/diagnostics10070464.

      3. Han C, Duan C, Zhang S, Spiegel B, Shi H, Wang W, et al. Digestive Symptoms in COVID-19 Patients With Mild Disease Severity: Clinical Presentation, Stool Viral RNA Testing, and Outcomes 2020. https://doi.org/10.14309/ajg.0000000000000664.

        • Gandhi M.
        • Yokoe D.S.
        • Havlir D.V.
        Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19.
        N Engl J Med. 2020; 382: 2158-2160https://doi.org/10.1056/NEJMe2009758
        • Xie J.
        • Tong Z.
        • Guan X.
        • Du B.
        • Qiu H.
        • Slutsky A.S.
        Critical care crisis and some recommendations during the COVID-19 epidemic in China.
        Intensive Care Med. 2020; 46: 837-840https://doi.org/10.1007/s00134-020-05979-7
        • Grasselli G.
        • Pesenti A.
        • Cecconi M.
        Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy.
        JAMA. 2020; 323: 1545https://doi.org/10.1001/jama.2020.4031
        • Shoer S.
        • Karady T.
        • Keshet A.
        • Shilo S.
        • Rossman H.
        • Gavrieli A.
        • et al.
        Who should we test for COVID-19? A triage model built from national symptom surveys.
        MedRxiv. 2020; (2020.05.18.20105569)https://doi.org/10.1101/2020.05.18.20105569
        • Tostmann A.
        • Bradley J.
        • Bousema T.
        • Yiek W.-K.
        • Holwerda M.
        • Bleeker-Rovers C.
        • et al.
        Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.
        Eurosurveillance. 2020; 25: 2000508https://doi.org/10.2807/1560-7917.ES.2020.25.16.2000508
        • Menni C.
        • Valdes A.M.
        • Freidin M.B.
        • Sudre C.H.
        • Nguyen L.H.
        • Drew D.A.
        • et al.
        Real-time tracking of self-reported symptoms to predict potential COVID-19.
        Nat Med. 2020; 26: 1037-1040https://doi.org/10.1038/s41591-020-0916-2
        • Salvatore P.P.
        • Sula E.
        • Coyle J.P.
        • PhD
        • Caruso E.
        • Smith A.R.
        • et al.
        Recent Increase in COVID-19 Cases Reported Among Adults Aged 18–22 Years — United States, May 31–September 5, 2020.
        MMWR Morb Mortal Wkly Rep. 2020; 69: 1419-1424https://doi.org/10.15585/mmwr.mm6939e4
      4. Simchoni M, Klopstock I, Furer A. COVID-19 among Young Adults. vol. 17. 2020.

      5. Report on the epidemiological features of coronavirus disease 2019 (covid-19) outbreak in the republic of korea from january 19 to march 2, 2020.
        J Korean Med Sci. 2020; 35https://doi.org/10.3346/jkms.2020.35.e112
      6. Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy n.d. https://doi.org/10.1038/s41591-020-0883-7.

        • Gao Z.
        • Xu Y.
        • Sun C.
        • Wang X.
        • Guo Y.
        • Qiu S.
        • et al.
        A systematic review of asymptomatic infections with COVID-19.
        J Microbiol Immunol Infect. 2020; https://doi.org/10.1016/j.jmii.2020.05.001
        • Furer A.
        • Afek A.
        • Sommer A.
        • Keinan-Boker L.
        • Derazne E.
        • Levi Z.
        • et al.
        Adolescent obesity and midlife cancer risk: a population-based cohort study of 2·3 million adolescents in Israel.
        Lancet Diabetes Endocrinol. 2020; 8: 216-225https://doi.org/10.1016/S2213-8587(20)30019-X
      7. CDC. CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel- For Emergency Use Only. 2020.

        • Moore N.M.
        • Li H.
        • Schejbal D.
        • Lindsley J.
        • Hayden M.K.
        Comparison of two commercial molecular tests and a laboratory-developed modification of the CDC 2019-ncov reverse transcriptase PCR assay for the detection of sars-cov-2.
        J Clin Microbiol. 2020; 58https://doi.org/10.1128/JCM.00938-20
        • Simel D.L.
        • Samsa G.P.
        • Matchar D.B.
        Likelihood ratios with confidence: sample size estimation for diagnostic test studies.
        J Clin Epidemiol. 1991; 44: 763-770
        • Wilson E.B.
        Probable Inference, the Law of Succession, and Statistical Inference.
        J Am Stat Assoc. 1927; 22: 209-212https://doi.org/10.1080/01621459.1927.10502953
      8. Home Page, Ministry of Health n.d. https://www.health.gov.il/English/Pages/HomePage.aspx (accessed August 21, 2020).

      9. Testing in the U.S. | CDC n.d. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/testing-in-us.html (accessed August 21, 2020).

      10. Coronavirus: rolling out community testing for covid-19 in the NHS - The BMJ n.d. https://blogs.bmj.com/bmj/2020/02/17/coronavirus-rolling-out-community-testing-for-covid-19-in-the-nhs/(accessed August 21, 2020).

        • Riccardo F.
        • Ajelli M.
        • Andrianou X.
        • Bella A.
        • Del Manso M.
        • Fabiani M.
        • et al.
        Epidemiological characteristics of COVID-19 cases in Italy and estimates of the reproductive numbers one month into the epidemic.
        MedRxiv. 2020; (2020.04.08.20056861)https://doi.org/10.1101/2020.04.08.20056861
        • Wiersinga W.J.
        • Rhodes A.
        • Cheng A.C.
        • Peacock S.J.
        • Prescott H.C.
        Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review.
        JAMA - J Am Med Assoc. 2020; 324: 782-793https://doi.org/10.1001/jama.2020.12839
        • Lacy T.J.
        • Benedek D.M.
        Terrorism and weapons of mass destruction: managing the behavioral reaction in primary care. (Review Article).
        South Med J. 2003; 96: 394-400
        • Salmon Ceron D.
        • Bartier S.
        • Hautefort C.
        • Nguyen Y.
        • Nevoux J.
        • Hamel A.L.
        • et al.
        Self-reported loss of smell without nasal obstruction to identify COVID-19. The multicenter Coranosmia cohort study.
        J Infect. 2020; https://doi.org/10.1016/j.jinf.2020.07.005