Characterizing participants who respond to text, email, phone calls, or postcards in a SARS-CoV-2 prevalence study.

INTRODUCTION: Multiple modalities and frequencies of contact are needed to maximize recruitment in many public health surveys. The purpose of this analysis is to characterize respondents to a statewide SARS-CoV-2 testing study whose participation followed either postcard, phone outreach or electronic means of invitation. In addition, we examine how participant characteristics differ based upon the number of contacts needed to elicit participation. METHODS: This is a cross-sectional analysis of survey data collected from participants who were randomly selected to represent Indiana residents and were invited to be tested for Covid-19 in April 2020. Participants received invitations via postcard, text/emails, and/or robocalls/texts based upon available contact information. The modality, and frequency of contacts, that prompted participation was determined by when the notification was sent and when the participant responded and subsequently registered to participate in the study. Chi square analyses were used to determine differences between groups and significant findings were analyzed using multinomial logistic regression. RESULTS: Respondents included 3,658 individuals and were stratified by postcards (7.9%), text/emails (26.5%), and robocalls/text (65.7%) with 19.7% registering after 1 contact, 47.9% after 2 contacts, and 32.4% after 3 contacts encouraging participation. Females made up 54.6% of the sample and responded at a higher rate for postcards (8.2% vs. 7.5%) and text/emails (28.1 vs. 24.6%) as compared to males (χ2 = 7.43, p = 0.025). Compared to males, females responded at a higher percentage after 1 contact (21.4 vs. 17.9%, χ2 = 7.6, p = 0.023). Those over 60 years responded most often after 2 contacts (χ2 = 27.5, p < 0.001) when compared to others at younger age groups. In regression analysis, participant sex (p = 0.036) age (p = 0.005), educational attainment (p = < 0.0001), and being motivated by "free testing" (p = 0.036) were correlated with participation in the prevalence study. DISCUSSION: Researchers should be aware that the modality of contact as well as the number of prompts used could influence differential participation in public health studies. Our findings can inform researchers developing studies that rely on selective participation by study subjects. We explore how to increase participation within targeted demographic groups using specific modalities and examining frequency of contact.

Laws Governing State Health Official Appointments: A Legal and Historical Review.

OBJECTIVE: The current study presents the legal epidemiological review of State Health Official (SHO) appointment laws, including the evolution of educational and experience requirements of SHOs over time. Findings can inform the discussion about state laws and the substantive, multidisciplinary qualifications essential to successfully leading state public health agencies in the 21st century. METHODS: Standard policy surveillance methods were used to collect and assess the statutes governing SHO appointment and eligibility from all 50 states and the District of Columbia between 1995 and 2020. RESULTS: SHOs are most frequently appointed by their jurisdiction's Health Secretary (n = 17), followed by Governor nominations with legislative approval (n = 15), and 13 states where the Governor is the sole SHO appointing authority. While a large majority of jurisdictions require certain professional and/or educational minimum qualifications to serve as an SHO, 11 states have no professional or experiential minimum qualifications. The most common minimum requirement found was possessing a medical degree, which is required in 22 jurisdictions (including Washington, District of Columbia). Twelve of these states require the physician to have additional education or experience, such as the possession of experience in public health (n = 5), experience in both public health and management (n = 3), or holding an additional health-related degree (n = 2). Four states added a medical degree as a requirement for SHOs over the last 25 years, while 5 states removed their medical degree requirement. CONCLUSIONS: States should reassess their eligibility requirements for SHO service in light of the advancement and demands of public health leadership in the 21st century, as many states continue to look to those with medical training as the primary source for such leadership.

Daniel J McCarty, MD: A Giant of Rheumatology.

There is no doubt that Dr Daniel J McCarty warrants inclusion among the giants of rheumatology. He has made major contributions to both clinical and scientific knowledge in our field, and his impact has been long-lasting and paradigm shifting. He is perhaps best known for his pioneering work in crystal arthritis, but as an astute clinician, he is also responsible for describing several other novel rheumatic conditions and developing innovative treatment protocols.

Design and Implementation of the Diabetes Impact Project: A Multisector Partnership to Reduce Diabetes Burden in Indianapolis Communities.

CONTEXT: Community-level health disparities have not arisen suddenly but are the result of long-term systemic inequities. This article describes the design and implementation of a community-engaged multisector partnership to address health disparities by reducing the diabetes burden in 3 Indianapolis communities through the implementation of evidence-based strategies across the prevention continuum. PROGRAM: The project has 5 foundational design principles: engage partners from multiple sectors to address community health, focus on geographic communities most affected by the health disparity, practice authentic community engagement, commit for the long term, and utilize a holistic approach spanning the prevention continuum. IMPLEMENTATION: The design principles are incorporated into the following project components in each community: (1) health system community health workers (hCHWs), (2) neighborhood CHWs (nCHWs), (3) community health promotion initiatives, and (4) resident steering committees, as well as a backbone organization responsible for overall coordination, project communication, evaluation, and partnership coordination. EVALUATION: This complex multilevel intervention is being evaluated using data sources and methodologies suited to each project component and its purpose overall. Each component is being evaluated independently and included holistically to measure the impact of the project on the health and culture of health in the communities. Key Performance Indicators were established upon project initiation as our common metrics for the partnership. Because complex interventions aiming at population-level change take time, we evaluate Diabetes Impact Project-Indianapolis Neighborhoods (DIP-IN), assuming its impact will take many years to achieve. DISCUSSION: Health disparities such as the diabetes prevalence in project communities have not arisen suddenly but are the result of long-term systemic inequities. This complex issue requires a complex holistic solution with long-term commitment, trusted partnerships, and investment from diverse sectors as seen in this project. Implications for policy and practice include the need to identify stable funding mechanisms to support these types of holistic approaches.

Successive Wave Analysis to Assess Nonresponse Bias in a Statewide Random Sample Testing Study for SARS-CoV-2.

INTRODUCTION: Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation. METHODS: Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19-related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave. RESULTS: Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants. CONCLUSIONS: Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.

Institutional factors associated with hospital partnerships for population health: A pooled cross-sectional analysis.

BACKGROUND: Hospitals are increasingly engaging in partnerships to address population health in response to national policies, such as value-based payment models. However, little is known about how institutional factors influence hospital partnerships for population health. PURPOSE: Guided by institutional theory, we examine the association between institutional pressures (coercive, normative, and mimetic isomorphism) and hospital partnerships for population health. METHODOLOGY: A pooled cross-sectional analysis used an unbalanced panel of 10,777 hospital-year observations representing respondents to a supplemental question of the American Hospital Association's annual survey (2015-2017). The analysis included descriptive and bivariate statistics, and regression models that adjusted for repeated observations to examine the relationship between key independent variables and partnerships over time. FINDINGS: In regression analyses, we found the most support for measures of coercive (e.g., regulatory factors) isomorphism, with nonprofit status, participation in accountable care organizations, and acceptance of bundled payments, all being consistently and significantly associated with partnerships across all organization types. Modest increases were observed from 2015 to 2017 for hospital partnerships with public health organizations (+2.8% points, p < .001), governmental organizations (+2.0% points, p = .009), schools (+4.1% points, p < .001), and businesses (+2.2% points, p = .007). PRACTICE IMPLICATIONS: Our results suggest that institutional factors, particularly those related to regulatory policies and programs, may influence hospital partnerships to support population health. Findings from this study can assist hospital leaders in assessing the factors that can support or impede the creation of partnerships to support their population health efforts.

Indoor Public Mask-Wearing Behavior Changes in Response to National, State, and Local COVID-19 Policies.

OBJECTIVE: To estimate changes in public mask-wearing behavior in response to public health policies during COVID-19. DESIGN: Panel of observed public mask-wearing. SETTING: Counts of adult behavior in Marion County, Indiana, between November 15, 2020, and May 31, 2021. DETERMINANTS OF INTEREST: (1) Removal of state masking requirement; (2) introduction of the National Strategy for the COVID-19 Response and Pandemic Preparedness; (3) the Centers for Disease Control and Prevention (CDC) recommendation that vaccinated individuals did not need to wear masks in public; and (4) COVID-19 vaccine availability. OUTCOME: Percent observed with correct mask-wearing. ANALYSES: Fixed-effects models estimated the association between policies and mask-wearing. RESULTS: Ending Indiana's mask requirement was not associated with changes in correct mask-wearing. The CDC's recommendation was associated with a decrease of 12.3 percentage points in correct mask-wearing (95% CI, -23.47 to -1.05; P = .032). CONCLUSIONS: Behavior encouraged by local mask requirements appeared to be resilient to changes in state policy. CDC recommendations appeared influential.

Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates.

BACKGROUND: Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. METHODS: Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. RESULTS: Compared to people with self-identified "excellent" or very good health status, those reporting "good" or "fair" health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. CONCLUSIONS: In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Primary Care-Based Case Conferences and Reductions in Health Care Utilization.

Case conferences are collaborative, interdisciplinary team meetings that facilitate consensus on individual patients' health management plans, coordinate services, and initiate referrals. This approach is well-suited to address the social needs and risks of complex patients. Evidence of this approach in primary care settings to change patient outcomes is limited. A panel of 976 patients from an urban, federally qualified health center were included in case conferences. Fixed-effects regression models estimated the effect of case conferences on admissions, emergency department (ED) visits, and missed outpatient appointments. Case conferencing was associated with a 6% reduction in the probability that the patient would have an ED visit in a given month and a 5% lower probability of an inpatient admission. The probability of missed primary care appointments increased. Case conferences are a potential strategy to address the multiple issues facing complex patients.

Impact of Medicaid expansion on smoking prevalence and quit attempts among those newly eligible, 2011-2019.

INTRODUCTION: Low-income populations have higher rates of smoking and are disproportionately affected by smoking-related illnesses. This study assessed the long-term impact of increased coverage for tobacco cessation through Medicaid expansion on past-year quit attempts and prevalence of cigarette smoking. METHODS: Using data from CDC's annual Behavioral Risk Factor Surveillance System 2011-2019, we conducted difference-in-difference regression analyses to compare changes in smoking prevalence and past-year quit attempts in expansion states versus non-expansion states. Our sample included non-pregnant adults (18-64 years old) without dependent children with incomes at or below 100% of the Federal Poverty Level (FPL). RESULTS: Regression analyses indicate that Medicaid expansion was associated with reduced smoking prevalence in the first two years post-expansion (ß=-0.019, p=0.04), but that this effect was not maintained at longer follow-up periods (ß=-0.006, p=0.49). Results of regression analyses also suggest that Medicaid expansion does not significantly impact quit attempts in the short-term (ß=-0.013, p=0.52) or at longer term follow-up (ß=-0.026, p=0.08). CONCLUSIONS: Expanded coverage for tobacco cessation services through Medicaid alone may not be enough to increase quit-attempts or sustain a reduction in overall prevalence of smoking in newly eligible populations over time. Medicaid programs should consider additional strategies, such as public education campaigns and removal of barriers, to support cessation among enrollees.

Hospital Partnerships for Population Health: A Systematic Review of the Literature.

EXECUTIVE SUMMARY: The U.S. healthcare system continues to experience high costs and suboptimal health outcomes that are largely influenced by social determinants of health. National policies such as the Affordable Care Act and value-based payment reforms incentivize healthcare systems to engage in strategies to improve population health. Healthcare systems are increasingly expanding or developing new partnerships with community-based organizations to support these efforts. We conducted a systematic review of peer-reviewed literature in the United States to identify examples of hospital-community partnerships; the main purposes or goals of partnerships; study designs used to assess partnerships; and potential outcomes (e.g., process- or health-related) associated with partnerships. Using robust keyword searches and a thorough reference review, we identified 37 articles published between January 2008 and December 2019 for inclusion. Most studies employed descriptive study designs (n = 21); health needs assessments were the most common partnership focus (n = 15); and community/social service (n = 21) and public health organizations (n = 15) were the most common partner types. Qualitative findings suggest hospital-community partnerships hold promise for breaking down silos, improving communication across sectors, and ensuring appropriate interventions for specific populations. Few studies in this review reported quantitative findings. In those that did, results were mixed, with the strongest support for improvements in measures of hospitalizations. This review provides an initial synthesis of hospital partnerships to address population health and presents valuable insights to hospital administrators, particularly those leading population health efforts.

How Many SARS-CoV-2-Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts.

CONTEXT: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. OBJECTIVE: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. DESIGN: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. SETTING: State of Indiana as of April 30, 2020. MAIN OUTCOME MEASURES: Demographic-stratified IHRs and case-hospitalization ratios. RESULTS: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. CONCLUSIONS: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand-especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected.

Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.

BACKGROUND: Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. METHODS: We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide prevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. RESULTS: Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR = 5.34, p<0.001), anosmia (OR = 4.08, p<0.001), ageusia (OR = 2.38, p = 0.006), and cough (OR = 2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. CONCLUSIONS: Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.

Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing.

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

Public Health Graduates and Employment in Governmental Public Health: Factors That Facilitate and Deter Working in This Setting.

OBJECTIVES: Previous surveys of public health graduates examine where they work; however, little is known about public health graduates' employment decisions or the factors that facilitate interest or deter interest in working in governmental public health settings. The purpose of the current pilot study was to build on the information previously collected in graduate surveys by expanding questions to undergraduates and asking about decisions and factors that influence choices of employment. METHODS: A pilot survey of graduates of public health programs was conducted. Respondents provided information about their degree programs, year of graduation, and current employment. Questions asked where they applied for jobs, factors they considered, experiences with the application processes, and so forth. Descriptive statistics were calculated using frequencies and proportions. Open-ended responses were qualitatively reviewed and general themes were extracted. RESULTS: Employment preferences were ranked the highest for not-for-profit organizations (ranked first among 21 of 62, 33.9%), followed by governmental public health agencies (ranked first among 18 of 62, 29.0%). Among master of public health graduates, 54.7% sought employment within this setting, although only 17.0% of those employed full time at the time of the survey were employed within a governmental public health agency. Job security (84.7%), competitive benefits (82.2%), identifying with the mission of the organization (82.2%), and opportunities for training/continuing education (80.6%) were the most influential, positive factors garnering interest in working in governmental public health. Factors that were the biggest deterrents included the ability to innovate (19.2%), competitive salary (17.8%), and autonomy/employee empowerment (15.3%). CONCLUSIONS: Approximately half of the respondents applied for a job within governmental public health in anticipation of or since graduating. However, only a quarter of employed respondents are currently working within governmental public health, suggesting a missed opportunity for recruiting the other quarter who applied and were interested in governmental positions.

Primary Care Case Conferences to Mitigate Social Determinants of Health: A Case Study from One FQHC System.

Objective: Given the increasing difficulty healthcare providers face in addressing patients' complex social circumstances and underlying health needs, organizations are considering team-based approaches including case conferences. We sought to document various perspectives on the facilitators and challenges of conducting case conferences in primary care settings. Study Design: Qualitative study using semi-structured telephone interviews. Methods: We conducted 22 qualitative interviews with members of case conferencing teams, including physicians, nurses, and social workers from a Federally Qualified Health Clinic, as well as local county public health nurses. Interviews were recorded, transcribed, and reviewed using thematic coding to identify key themes/subthemes. Results: Participants reported perceived benefits to patients, providers, and healthcare organizations including better care, increased inter-professional communication, and shared knowledge. Perceived challenges related to underlying organizational processes and priorities. Perceived facilitators for successful case conferences included generating and maintaining a list of patients to discuss during case conference sessions and team members being prepared to actively participate in addressing tasks and patient needs during each session. Participants offered recommendations for further improving case conferences for patients, providers, and organizations. Conclusions: Case conferences may be a feasible approach to understanding patient's complex social needs. Participants reported that case conferences may help mitigate the effects of these social issues and that they foster better inter-professional communication and care planning in primary care. The case conference model requires administrative support and organizational resources to be successful. Future research should explore how case conferences fit into a larger population health organizational strategy so that they are resourced commensurately.

Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.

BACKGROUND: Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. METHODS: We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide seroprevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. RESULTS: Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR=5.34, p<0.001), anosmia (OR=4.08, p<0.001), ageusia (OR=2.38, p=0.006), and cough (OR=2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. CONCLUSIONS: Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials. RESEARCH IN CONTEXT: Evidence before this study: Using multiple journal articles queried from MEDLINE as well as a Cochrane systematic review, we examined all studies that described symptoms known to be associated with COVID-19. We further examined the guidelines from WHO and CDC on the symptoms those public health authorities consider to be associated with COVID-19. Most of the evidence comes from China, Italy, and the United States. Collectively prior research and guidance suggests there are a dozen symptoms reported by individuals who tested positive for COVID-19 in multiple countries. Symptoms include fever, cough, fatigue, anosmia, ageusia, shortness of breath, chills, myalgias, headache, sore throat, chest pain, and gastrointestinal issues. The evidence is generally of low quality as it is descriptive in nature, and it is biased towards hospitalized patients. Most studies report the proportion of patients hospitalized or testing positive for infection who report one or more symptoms within 3-14 days prior to hospitalization or infection. There has been little validation of symptoms among hospitalized or non-hospitalized patients. Furthermore, according to a Cochrane review, no studies to date assess combinations of different signs and symptoms.Added value of this study: This study employs multiple, rigorous methods to examine the ability of specific symptoms as well as symptom combinations/groups to predict laboratory-confirmed (RT-PCR) infection of SARS-CoV-2. Furthermore, the study is unique in its large sample drawn exclusively from community-based populations rather than hospitalized patients.Implication of all the available evidence: Combining the evidence from this study with prior research suggests that anosmia and ageusia are key symptoms that differentiate COVID-19 from influenza-like symptoms. Clinical screening protocols for COVID-19 should look for these symptoms, which are not commonly asked of patients who present to urgent care or hospital with flu-like symptoms. KEY POINTS: Important symptoms specific to COVID-19 are fever, anosmia, ageusia, and cough. Two-thirds of symptoms were highly specific (>90.0%), yet most symptoms individually possessed a PPV <50.0%. This study confirms using robust methods the key symptoms associated with COVID-19 infection, and it also identifies combinations of symptoms strongly associated with positive infection.