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Characterization of persons with reported SARS-CoV-2 infection in the Oklahoma City tri-county area: Evidence from the first 12 months of transmission

Published:March 12, 2022DOI:https://doi.org/10.1016/j.ajic.2022.03.007

      Abstract

      Background

      To describe characteristics, hospitalization, and death for reported cases of SARS-CoV-2 infection in the Oklahoma City tri-county area.

      Methods

      We extracted notified cases of SARS-CoV-2 infection for our study area and used descriptive statistics and modeling to examine case characteristics and calculate the odds of hospitalization and death in relation to a range of explanatory variables.

      Results

      Between March 12th, 2020 and February 28th, 2021, 124,925 cases of SARS-CoV-2 infection were reported from the study region. Being male, White or Black/African American, aged 50 years or older, presenting with apnea, cough, and shortness of breath, and having diabetes was associated with increased odds of hospitalization. The odds of dying were significantly associated with being Black/African American, presenting with cough and fever, having kidney disease and diabetes and being aged 70 years or older.

      Conclusions

      The first cohort analysis of SARS-CoV-2 positive individuals in the Oklahoma City tri-county area confirms comorbidities and age as important predictors of COVID-19 hospitalization or death. As a novel aspect, we show that early symptoms of breathing difficulties in particular are associated with hospitalization and death. Initial case assessment and SARS-CoV-2 guidelines should continue to focus on age, comorbidities, and early symptoms.

      Graphical abstract

      Key words

      Since the start of the COVID-19 pandemic in early 2020, the United States has experienced the highest rate of reported positive cases of SARS-CoV-2 infection and related deaths.

      Ritchie H., Mathieu E., Rodés-Guirao L., et al. Coronavirus pandemic (COVID-19).Our World Data. Accessed August 25, 2021. https://ourworldindata.org/covid-cases.

      Throughout 2020 and 2021, the pandemic has been studied in great detail with respect to many different epidemiological facts including risks for age and gender, ethnicity and comorbidities in the population.
      • Rentsch C.T.
      • Kidwai-Khan F.
      • Tate J.P.
      • et al.
      Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: a nationwide cohort study.
      • Harrison S.L.
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      • Lip G.Y.H.
      Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: a federated electronic medical record analysis.
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      Association of race with mortality among patients hospitalized with coronavirus disease 2019 (COVID-19) at 92 US hospitals.
      The majority of these studies were based on data from large cities or selected population groups such as veterans, hospitalized patients or specific ethnic groups. Only a limited number of publications have used routinely collected surveillance data from smaller urban areas focusing on both hospitalized and nonhospitalized SARS-CoV-2 positive individuals.
      • Rentsch C.T.
      • Kidwai-Khan F.
      • Tate J.P.
      • et al.
      Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: a nationwide cohort study.
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      • Almonte M.
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      • Ali M.
      • et al.
      Association between COVID-19 outcomes and patient characteristics: a study in an inner-city community hospital.

      Bernet P. OVID-19 infections and mortality in Florida counties: roles of race, ethnicity, segregation, and 2020 election results. J Racial Ethn Health Disparities, in press

      • Khose S.
      • Moore J.X.
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      Epidemiology of the 2020 pandemic of COVID-19 in the State of Texas: the first month of community spread.
      • Marcello R.K.
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      Characteristics and outcomes of COVID-19 patients in New York City’s public hospital system.
      • Ojinnaka C.O.
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      Factors associated with COVID-related mortality: the case of Texas.
      • Tai D.B.G.
      • Shah A.
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      • Sia I.G.
      • Wieland M.L.
      The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States.
      • Ferguson J.M.
      • Abdel Magid H.S.
      • Purnell A.L.
      • Kiang M.V.
      • Osborne T.F.
      Differences in COVID-19 testing and test positivity among veterans, United States, 2020.
      In general, published evidence suggests that males are more likely to be infected, hospitalized and die from severe COVID-19 than females.
      • Jin J.-M.
      • Bai P.
      • He W.
      • et al.
      Gender differences in patients with COVID-19: focus on severity and mortality.
      ,
      • Bwire G.M.
      Coronavirus: why men are more vulnerable to Covid-19 than women?.
      It has also been shown that persons with certain types of comorbidities and persons older than 70 years of age have a higher risk of infection, hospitalization and death compared to healthier, younger patients.
      • Zheng Z.
      • Peng F.
      • Xu B.
      • et al.
      Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis.
      Finally, several studies have concluded that certain ethnic minorities are disproportionately affected by infection and disease.
      • Harrison S.L.
      • Fazio-Eynullayeva E.
      • Lane D.A.
      • Underhill P.
      • Lip G.Y.H.
      Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: a federated electronic medical record analysis.
      ,
      • Tai D.B.G.
      • Shah A.
      • Doubeni C.A.
      • Sia I.G.
      • Wieland M.L.
      The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States.
      ,
      • Pijls B.G.
      • Jolani S.
      • Atherley A.
      • et al.
      Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies.
      Oklahoma is located in the South-Central United States Great Plains and has a population of approximately 4 million. The first case of SARS-CoV-2 infection was reported in the State on March 6th 2020, with continued high levels of transmission and increasing rates of hospitalizations throughout the year.

      COVID-19 (340) [Internet]. COVID-19. Accessed June 8, 2022. https://oklahoma.gov/covid19.html

      Oklahoma has a diverse population with many ethnic and rural communities that are known to be particularly vulnerable to the SARS-CoV-2 infection, COVID-19 disease and severe outcomes. Oklahoma City is the State Capital, with an estimated population of 681,000 persons in 2021.

      U.S. Census Bureau QuickFacts: Oklahoma City city, Oklahoma [Internet]. [cited 2022 Mar 3]. Available from: https://www.census.gov/quickfacts/oklahomacitycityoklahoma

      The city demographics are relatively diverse with 53% of the population reported as White Non-Hispanic origin, 21% Hispanic, 14% Non-Hispanic Black and 3% American Indian and Alaskan Native in 2019. No state-wide mask mandate was issued during the pandemic, however a number of counties and cities, including Oklahoma City and surrounding counties, adopted mask mandates. Furthermore, state-wide restrictions such as business closures, limits to gatherings, indoor sports closures, and sanitation requirements were also implemented to limit the spread of COVID-19.
      This work presents a thorough analysis of reported COVID-19 cases in the tri-county region of Oklahoma City. The aim of the study is to describe persons from the tri-county area of Oklahoma City who had a confirmed positive SARS-CoV-2 test, in order to understand how certain patient characteristics were related to hospitalization and death and to suggest evidence-based guidelines for assessment of SARS-CoV-2 positive individuals.

      Materials and methods

      In this retrospective observational cohort study, we extracted notified COVID-19 cases (SARS-CoV-2 infection) from March 12th, 2020 until February 28th 2021 for the tri-county region (Oklahoma, Cleveland and Canadian counties) from Oklahoma State Department of Health (OSDH) surveillance data through the Public Health Disease Detection of Oklahoma (PHIDDO) reporting system. Providers and laboratories report confirmed and probable COVID-19 cases to PHIDDO following a diagnosis. Provided that sufficient resources are available, case investigators at County Health Departments interview cases in their respective counties and update PHIDDO with additional information. PHIDDO data is synced daily to an SQL server maintained by OSDH. A COVID-19 case is diagnosed through a molecular (eg, RT-PCR, NAAT, LAMP) or antigen (eg, rapid test) test from a sample collected by way of saliva, throat, or nose swab.
      For each confirmed case, we extracted the following: (1) date of notification in the system, (2) symptom onset date, (3) age, (4) gender, (5) ethnic group, (6) location (county, city/town, and zip code), (7) occupation, (8) presence of various symptoms at the time of testing, (9) information on chronic comorbidities and (10) whether the person was hospitalized and/or died following their diagnosis. All data on symptom onset, presence of symptoms, and presence of comorbidities were self-reported by the patient and covered symptoms/comorbidities as defined in a simple questionnaire without ICD codes. Patients were also asked to self-identify their ethnicity, gender and occupation. Data on hospitalizations and deaths reflect hospitalizations and deaths specifically due to COVID-19, with COVID-19 listed as the main cause on hospital admission records and death certificates, respectively. We extracted daily numbers of administered and positive PCR tests for SARS-CoV-2 infection for the State of Oklahoma from the COVID Tracking Project website.

      The Data [Internet]. The COVID Tracking Project. Accessed June 8, 2022. https://covidtracking.com/data

      Patients with “no information provided” for specific variables (eg, symptoms or comorbidities) were excluded from the analyses for that particular variable. The number of persons excluded from the analysis for each explanatory variable is listed in Table 1 where possible. All data were analyzed using basic descriptive statistics and univariate and multivariate logistic regression. Multivariate regression models were adjusted for the confounding effect of gender, age, county of residence, ethnicity, and presence of comorbidities. We also adjusted multivariate models for interactions between these confounders. A backwards variable selection approach was employed.
      Table 1Characteristics, hospitalizations and deaths for persons reported with a SARS-CoV-2 infection in the Oklahoma City tri-county area, March 2020-February 2021
      CharacteristicNumber (%)Hospitalizations (%)
      Percentages refer to the percentage of persons who were hospitalized or died who had the specific characteristic (in some cases this does not add up to 100%),
      Deaths (%)
      Percentages refer to the percentage of persons who were hospitalized or died who had the specific characteristic (in some cases this does not add up to 100%),
      Odds of hospitalization, OR (95% CI, P)Odds of death, OR (95% CI, P)
      Overall124,9254,076 (3.2)1,858 (1.5)--
      Sex
       Male

      Female
       59,128 (47.3)

      65,797 (52.7)
       2,138 (52.5)

      1,938 (47.5)
       1,052 (56.6)

      806 (43.4)
       1.6 (1.2-1.8, <.001)

      Reference
       1.5 (1.2-1.8, <.001)

      Reference
      Age group
       0-5

      5-18

      18-29

      30-39

      40-49

      50-59

      60-69

      70-79

      80-89

      90-99

      100+

      No information provided
       2,684 (2.1)

      14,446 (11.6)

      29,842 (23.9)

      21,824 (17.5)

      19,071 (15.3)

      15,703 (12.6)

      11,389 (9.1)

      6,239 (5.0)

      2,846 (2.3)

      834 (0.7)

      38 (0.03)

      9 (0.0)
       19 (0.5)

      41 (1.0)

      106 (2.6)

      263 (6.5)

      446 (10.9)

      681 (16.7)

      831 (20.4)

      948 (23.3)

      557 (13.7)

      182 (4.5)

      2 (0.05)

      0 (0)
       1 (0.05)

      0 (0)

      6 (0.3)

      30 (1.6)

      69 (3.7)

      177 (9.5)

      322 (17.3)

      480 (25.8)

      530 (28.5)

      235 (12.6)

      8 (0.4)

      0 (0)
       1.0 (0.6-1.9, .968)

      0.9 (0.6-1.3, .467)

      Reference

      3.8 (2.8-5.1, <.001)

      10.7 (7.7-14.9, <.001)

      13.8 (10.1-19.0, <.001)

      12.9 (9.6-17.4, <.001)

      15.4 (11.4-20.9, <.001)

      11.9 (8.7-16.5, <.001)

      9.2 (6.1-14.0, <.001)

      1.1 (0.6-2.1, .655)

      -
       1.9 (0.2-15.5, .548)

      (empty)

      Reference

      6.8 (2.8-16.4, <.001)

      18.0 (17.8-41.4, <.001)

      56.3 (25.0-127, <.001)

      146 (65.2-328, <.001))

      398 (178-893, <.001)

      998 (445-2236, <.001)

      1603 (708-3629, <.001)

      1272 (396-4085, <.001)

      -
      Ethnic group
       White

      Hispanic or Latino

      Black or African American

      American/Alaskan Native

      No information provided
       45,854 (36.7)

      15,580 (12.5)

      11,012 (8.8)

      4,333 (3.5)

      48,146 (38.5)
       2,715 (66.6)

      553 (13.6)

      616 (15.1)

      192 (4.7)

      0 (0)
       1,243 (66.9)

      152 (8.2)

      184 (9.9)

      6 (0.3)

      273 (14.7)
       Reference

      0.8 (0.6-2.4, .467)

      2.9 (1.1-5.6, <.05)

      0.9 (0.8-3.1, .504)

      -
       Reference

      1.5 (1.3-6.1, <.001)

      1.4 (1.2-4.5, <.001)

      0.9 (0.7-1.1, .440)

      -
      Comorbidities
       No comorbidities

      Cardiac disease

      Diabetes

      Lung disease

      Kidney disease

      Liver disease

      Two comorbidities

      Three comorbidities

      Four or 5 comorbidities

      No information provided
       34,373 (27.5)

      6,898 (5.5)

      5,108 (4.1)

      5,081 (4.1)

      1,030 (0.8)

      580 (0.5)

      2,317 (1.9)

      526 (0.4)

      90 (0.1)

      71,855 (57.5)
       558 (13.7)

      1,140 (28.0)

      826 (20.3)

      525 (12.9)

      293 (7.2)

      71 (1.7)

      335 (8.2)

      146 (3.6)

      32 (0.8)

      -
       1,043 (56.1)

      534 (28.7)

      327 (17.6)

      212 (11.4)

      105 (5.7)

      30 (1.6)

      116 (6.2)

      59 (3.2)

      19 (1.0)

      -
       Reference

      1.9 (1.2-2.7, <.001)

      1.7 (1.3-2.2, <.001)

      1.6 (1.2-2.2, <.001)

      1.4 (1.0-2.0, <.05)

      1.1 (0.7-1.5, .067)

      2.2 (1.6-2.9, <.001)

      2.6 (1.6-4.2, <.001)

      3.1 (1.1-10.5, <.05)

      -
       Reference

      7.4 (5.9-9.2, <.001)

      2.4 (1.9-3.0, <.001)

      1.8 (1.4-2.3, <.001)

      5.6 (4.2-7.3, <.001)

      1.1 (0.6-1.8, .814)

      20.6 15.5-27.3, (<.001)

      49.2 (34.8-69.4, <.001)

      136.4 (26.1-712, .001)

      -
      Smoking
       Never smoked

      Current/previous smoker

      No information provided
       37,832 (30.3)

      10,485 (8.4)

      76,608 (61.3)
       1,167 (28.6)

      485 (11.9)

      2,424 (59.5)
       247 (13.3)

      155 (8.3)

      1,456 (78.4)
       Reference

      3.9 (2.7-5.8, <.001)

      -
       Reference

      1.6 (1.1-2.6, <.001)

      -
      Symptoms
       Asymptomatic
      Patient specifically indicated being asymptomatic,


      Malaise/myalgia

      Fever/chills

      Gastrointestinal complaints
      Abdominal pain, vomiting, nausea and/or diarrhea.


      Headache

      Cough

      Runny nose

      Breathing difficulties

      Sore throat

      Anorexia

      Chest pain

      Loss of taste and smell

      One symptom only

      2-4 symptoms

      5-7 symptoms

      8+ symptoms

      No information provided
       73,603 (58.9)

      59,189 (47.4)

      46,284 (37.0)

      41,678 (33.4)

      33,596 (26.7)

      29,276 (23.4)

      21,658 (17.3)

      19,779 (15.8)

      18,832 (15.1)

      16,081 (12.9)

      9,640 (7.7)

      551 (0.4)

      4,679 (3.7)

      16,104 (12.9)

      14,936 (12.0)

      15,926 (12.7)

      7,496 (6.0)
       1,463 (35.9)

      2,561 (62.2)

      2,559 (62.8)

      3,588 (88.0)

      927 (22.7)

      1,590 (39.0)

      481 (11.8)

      2,233 (54.8)

      502 (12.3)

      900 (22.1)

      616 (15.1)

      26 (0.6)

      296 (7.3)

      936 (23.0)

      601 14.7)

      780 (19.2)

      -
       986 (53.1)

      550 (29.6)

      598 (32.2)

      507 (27.3)

      107 (5.8)

      421 (22.7)

      93 (5.0)

      642 (34.6)

      79 (4.3)

      171 (9.2)

      94 (5.1)

      1 (0.1)

      207 (11.1)

      440 (23.7)

      129 (6.9)

      96 (5.1)

      -
       Reference

      2.6 (2.1-2.9, <.001)

      2.6 (1.8-2.8, <.001)

      1.6 (1.2-2.0, <.01)

      1.2 (0.9-1.6, .249)

      2.5 (1.9-3.4, <.001)

      0.8 (0.6-1.1, .156)

      5.7 (4.8-6.1, <.001)

      1.0 (0.8-1.4, .788)

      1.8 (1.3-2.4, <.001)

      1.8 (1.3-2.5, <.01)

      1.1 (0.3-3.6, .902)

      1.9 (1.5-2.6, <.001)

      3.9 (2.7-5.8, <.001)

      5.1 (3.2-7.8, <.001)

      10.9 (3.8-34.2,<.01)

      -
       Reference

      0.9 (0.4-1.2, .08)

      1.4 (1.1-1.8, <.01)

      1.2 (0.9-1.5, .621)

      0.2 (0.1-0.3, <.001)

      2.3 (1.9-2.8, <.001)

      0.5 (0.3-0.7, <.001)

      9.8 (7.9-10.9, <.001)

      0.5 (0.3-0.6, .001)

      1.5 (1.2-1.9, <.001)

      1.4 (1.1-1.7, <.05)

      0.1 (0.1-0.7, <.05)

      1.3 (1.1-1.5, <.05)

      1.1 (0.9-1.3, .354)

      0.5 (0.2-1.5, .234)

      0.5 (0.1-3.7, .522)

      -
      Occupation (over 18s only)
       Retired

      Unemployed

      Health care worker

      Food worker

      Student (college/university)

      Teacher

      No information provided
       3,434 (2.7)

      2,420 (1.9)

      2,398 (1.9)

      1,528 (1.2)

      1,466 (1.2)

      1,245 (1.0)

      105,078 (84.1)
       129 (3.2)

      90 (2.2)

      92 (2.3)

      67 (1.6)

      54 (1.3)

      41 (1.0)

      -
       71 (3.8)

      48 (2.6)

      19 (1.0)

      22 (1.2)

      13 (0.7)

      35 (1.9)

      -
       1.3 (0.8-2.3, .262)

      Reference

      1.4 (0.8-2.3, .256)

      1.4 (0.8-2.3, .258)

      0.8 (0.6-1.2, .307)

      0.7 (0.4-1.4, .339)

      -
       1.4 (1.1-1.8, <.01)

      Reference

      1.3 (0.9-1.8, .134)

      0.8 (0.5-1.2, .225)

      0.4 (0.2-0.7, <.001)

      1.7 (1.2-2.3, <.01)

      -
      low asterisk Percentages refer to the percentage of persons who were hospitalized or died who had the specific characteristic (in some cases this does not add up to 100%),
      Patient specifically indicated being asymptomatic,
      Abdominal pain, vomiting, nausea and/or diarrhea.
      Data analyses were performed in STATA 17.0 (StataCorp 2019, College Station, TX). The study was declared exempt from IRB oversight.

      Results

      Case trends and demography

      Between March 12th, 2020 and February 28th, 2021, a total of 124,925 cases of SARS-CoV-2 infection were reported from the study region (Table 1, Fig 1). Of these, 80,540 (64.5%) of cases were reported from Oklahoma county (the most populous) followed by Cleveland (22.8%) and Canadian (12.7%) counties.
      Fig 1
      Fig 1Notified cases of SARS-CoV-2 infection and PCR test positivity*, Oklahoma City tri-county area 2020-2021.
      * PCR test positivity reflects 7-day moving average of percentages for Oklahoma State as a whole.
      We observed limited transmission in the early pandemic phase from March to June 2020. This was followed by a substantial increase in cases at the end of June 2020 and the largest increase in disease transmission in November-December 2020, accounting for 44% all cases during the study period. (Fig 1). From early January through February, 2021, there was a notable decrease in reported cases and the number of SARS-CoV-2 positive tests in the State as a whole (Fig 1).
      The average age of the reported cases was 38.8 years (median 37 years) ranging from 1 month to 102 years (Table 1). The highest proportion of infections was diagnosed among young adults aged 18-29 years with 23.9% of reported cases, persons aged 30-39 years (17.5%) and the 40-49-year group (15.3%) (Table 1). People identifying as White had the highest number of cases (36.7%), followed by Hispanic or Latino (12.5%) and African American or Black (8.8%).

      Hospitalizations and deaths

      A total of 4,076 (3.2%) persons with a confirmed SARS-CoV-2 infection in the tri-county Oklahoma City area were hospitalized and 1,858 (1.5%) died. The highest percentage of hospitalizations was observed among the White ethnic group (66.6%) and Black/African Americans (15.1%). This mirrored the results for deaths where 66.9% of reported deaths were among White persons and 9.9% among Black/African Americans. Compared to the average hospitalization and death rates during the study period, we observed initial high rates from March until end of June 2020, ranging between 20% and 50% (Fig 2). After this point, both hospitalization and death rates stabilized at 4%-10% for hospitalizations and 1%-3% for deaths, remaining at those levels until the end of the study period (Fig 2).
      Fig 2
      Fig 2COVID-19 case-hospitalization and case-fatality rates, Oklahoma City 2020-2021.
      The average age of hospitalized patients was 63 years (median 65), ranging from 0 to 102 years. Being 30 years or older was associated with significantly higher odds of hospitalization and this generally increased with age, apart from persons aged older than 90 years (Table 1).
      The average age of persons who died from COVID-19 was 74 years (median 76), ranging from 2 to 108 years old. The odds of dying increased with age, reaching a peak for persons aged 80-89 years (Table 1). Compared to White persons, Black, or African Americans had the highest odds of being hospitalized and dying, followed by American Indians or Alaskan Natives (Table 1).

      Comorbidities and symptoms

      A total of 37,482 (30%) persons with a reported positive SARS-CoV-2 test indicated having at least one comorbidity; the most common of which were cardiovascular disease (5.5%), diabetes (4.1%) and lung disease (4.1%) (Table 1). Having any comorbidity, apart from liver disease, was associated with higher odds for hospitalization. This was most pronounced for cardiovascular disease and diabetes (Table 1). Persons with any type of comorbidity had higher odds of dying, particularly if they had kidney or cardiovascular disease (Table 1). The odds of dying increased with the number of comorbidities, and patients with 4 or 5 comorbidities had approximately 6 times higher odds of dying compared to patients with 2 comorbidities (Table 1).
      A total of 73,603 (58.9%) persons reported that they did not experience any symptoms in relation to their infection and were classified as “asymptomatic.” The most common symptoms reported were malaise/myalgia, fever/chills, gastrointestinal complaints, headache and cough (Table 1). Most symptoms recorded were associated with a higher risk of hospitalization, in particular breathing difficulties, malaise/myalgia, fever/chills and cough (Table 1). The more symptoms recorded by a patient, the higher the odds of hospitalization. Several symptoms were also associated with increased odds of dying; the most significant being breathing difficulties, cough and fever/chills (Table 1). There was no significant relationship between the number of symptoms and the odds of dying.

      Lifestyle and occupation

      Of the study population, a total of 8.4% (10,485 persons) reported currently smoking or having smoked previously. Being a current or previous smoker was significantly associated with an increased risk of both hospitalization and death (Table 1).
      Data on occupation were available for 41,766 (33.4%) of the reported cases. Of these, 19,847 (47.5%) provided specific details on the type of their occupation. The most frequently reported were retired (17.3%), unemployed (12.1%) and healthcare worker (12.0%). There were no significantly increased or decreased odds of hospitalization associated with type of occupation (Table 1). However, persons who were retired or worked as teachers had increased odds of dying while university students had reduced odds of death (Table 1).

      Multivariate analyses

      Being male, White or Black/African American, aged 50 years or older, having diabetes and presenting with breathing difficulties and cough was associated with increased odds of hospitalization (Table 2). Increased odds of death was significantly associated with being Black/African American, presenting with cough and fever/chills, having kidney disease, and diabetes and being aged 70 years or older (Table 2).
      Table 2Risk factors for hospitalization and death identified using multivariate regression among persons testing positive for SARS-CoV-2 in the Oklahoma City tri-county area, March 2020-February 2021
      HospitalizationsDeaths
      CharacteristicOR95% CI (P)CharacteristicOR95% CI (P)
      Male1.71.1-2.1 (<.05)Black or African American2.11.7-2.7 (<.05)
      White1.91.1-3.1 (<.05)Cough2.31.1-2.7 (<.05)
      Black or African American3.21.2-6.3 (<.001)Fever/chills1.81.3-2.3 (<.001)
      Breathing difficulties4.02.7-5.7 (<.001)Kidney disease4.32.9-6.1 (<.001)
      Cough2.41.7-3.5 (<.001)Diabetes2.81.4-2.4 (<.001)
      Diabetes2.41.9-3.7 (<.01)Aged 70-791.61.1-2.2 (<.05)
      Aged 50-594.01.1-16.7 (<.05)Aged 80-891.91.4-2.5 (<.05)
      Aged 60-694.11.1-15.7 (<.05)Aged 90-992.91.8-3.1 (<.05)
      Aged 70-794.81.3-18.2 (<.05)
      Aged 80-897.11.7-29.0 (<.01)
      Aged 90-997.41.1-51.9 (<.05)

      Discussion

      In this paper we describe the characteristics of persons who tested positive for SARS-CoV-2 infection during the first 12 months of transmission in the tri-county area of Oklahoma City, including individuals who were later hospitalized and died from COVID-19.
      Of the almost 125,000 persons who tested positive, approximately 3% were hospitalized and 1.5% died from their infection. In our study population, the odds of hospitalization and death increased significantly with age, confirming several previous findings that COVID-19 has the strongest impact on elderly persons.
      • Ojinnaka C.O.
      • Adepoju O.E.
      • Burgess A.V.
      • Woodard L.
      Factors associated with COVID-related mortality: the case of Texas.
      ,
      • Pijls B.G.
      • Jolani S.
      • Atherley A.
      • et al.
      Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies.
      ,

      Bhaskaran K., Bacon S., Evans S.J., et al. Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform. Lancet Reg Health – Eur [Internet]. 2021 Jul 1 [cited 2021 Sep 22];6. Available from: https://www.thelancet.com/journals/lanepe/article/PIIS2666-7762(21)00086-7/fulltext

      Our results also confirm that existing comorbidities – particularly cardiovascular disease and diabetes - significantly increase the risk of developing severe disease, becoming hospitalized and dying.
      • Harrison S.L.
      • Fazio-Eynullayeva E.
      • Lane D.A.
      • Underhill P.
      • Lip G.Y.H.
      Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: a federated electronic medical record analysis.
      ,
      • Marcello R.K.
      • Dolle J.
      • Grami S.
      • et al.
      Characteristics and outcomes of COVID-19 patients in New York City’s public hospital system.
      ,
      • Ojinnaka C.O.
      • Adepoju O.E.
      • Burgess A.V.
      • Woodard L.
      Factors associated with COVID-related mortality: the case of Texas.
      ,
      • Zhang Y.-J.
      • Sun X.-F.
      • Xie B.
      • Feng W.-J.
      • Han S.-L.
      Exploration of severe Covid-19 associated risk factor in China: meta-analysis of current evidence.
      In our study population, persons who suffered from several comorbidities had increasing odds of both hospitalization and death.
      Although the role of comorbidities in relation to COVID-19 severity is relatively well understood, non-chronic symptomatic presentations at the time of testing have primarily been studied in relation to biomarkers and prognostic factors
      • Nuertey B.D.
      • Ekremet K.
      • Haidallah A.-R.
      • et al.
      Performance of COVID-19 associated symptoms and temperature checking as a screening tool for SARS-CoV-2 infection.
      • Sharma A.G.
      • Kumar V.
      • Sodani R.
      • et al.
      Predictors of mortality in children admitted with SARS-CoV-2 infection in tertiary care hospital in North India.
      • Larsen J.R.
      • Martin M.R.
      • Martin J.D.
      • Kuhn P.
      • Hicks J.B.
      Modeling the onset of symptoms of COVID-19.
      rather than examining the association initial symptoms and later hospitalization or death. Our study demonstrates some of the first population-derived results on symptoms in a cohort of SARS-CoV-2 positive individuals, regardless of hospitalization status. Firstly, almost 60% of the persons included in our study reported that they were asymptomatic. This is in line with other published findings and confirms that asymptomatic status is common for persons with confirmed infection.
      • Nikolai L.A.
      • Meyer C.G.
      • Kremsner P.G.
      • Velavan T.P.
      Asymptomatic SARS Coronavirus 2 infection: invisible yet invincible.
      Secondly, presenting with cough, general breathing difficulties, and fever/chills at the time of testing was generally associated with increased odds of both hospitalization and death. We also observed a dose-response relationship between the number of symptoms and the odds of hospitalization, but not of dying. This could suggest that the progression from hospitalization/disease to death is primarily affected by other factors such as comorbidities. Interestingly, only a small proportion of persons in our study reported loss of taste and smell, even though this symptom has been highlighted as one of the primary indicators of SARS-CoV-2 infection.
      • Sudre C.H.
      • Keshet A.
      • Graham M.S.
      • et al.
      Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study.
      However, considering that this symptom was only reportable as a free-text option, rather than a yes/no like the other symptoms examined, this result may not accurately reflect the true frequency of loss of taste and smell in the study cohort.
      Our multivariate modeling indicated that higher odds of hospitalization were significantly related to ethnicity (being White or African American/Black), having diabetes, experiencing breathing difficulties at time of testing, and being aged 50 years or older. Dying from COVID-19 was associated with being Black/African American, having diabetes and kidney disease, experiencing cough and fever at time of testing and being aged 70 years or older.
      SARS-CoV-2 infection and COVID-19 disease is known to disproportionately affect certain ethnic groups such as Hispanics or Latinos and African Americans.

      Bernet P. OVID-19 infections and mortality in Florida counties: roles of race, ethnicity, segregation, and 2020 election results. J Racial Ethn Health Disparities, in press

      ,
      • Tai D.B.G.
      • Shah A.
      • Doubeni C.A.
      • Sia I.G.
      • Wieland M.L.
      The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States.
      ,
      • Loomba R.S.
      • Aggarwal G.
      • Aggarwal S.
      • et al.
      Disparities in case frequency and mortality of coronavirus disease 2019 (COVID-19) among various states in the United States.
      We confirm this with our results showing that African American/Black persons with a positive SARS-CoV-2 test had approximately 3 times higher odds of hospitalization and 1.4 higher odds of dying compared to Non-Hispanic White persons. A similar scenario, although only for death, was observed for Hispanic/Latinos in our study who had significantly higher odds of dying.
      Our findings need to be considered in the light of several limitations. Firstly, all symptoms and existing comorbidities were self-reported by each patient, and it was not possible to confirm this information by screening of individual records. Overall, this is an important limitation of the study; however, we also consider the general possibility of each patient inaccurately reporting chronic conditions to be low. With respect to symptoms and comorbidities, these were not recorded in the questionnaire using standardized ICD codes and as such are subject to individual reporting and interpretation biases. For instance, the extent to which ‘cardiac disease’ includes hypertension is unknown, because this condition was reported at the discretion of the patient. Similarly, we are unable to determine whether the variable ‘lung disease’ includes chronic conditions such as asthma and Chronic Obstructive Pulmonary Disease. As a result of this, our results are applicable to general conditions rather than specific comorbidities listed in the official ICD list. Another limitation is the large number of records with ‘information not provided’ for comorbidities and symptoms in particular. These records were excluded from the analysis, making the analytical dataset smaller than expected. We also did not have comparative information about persons who tested negative for SARS-Cov-2 and were therefore not able to calculate true risks of hospitalization and death. Testing rates have fluctuated significantly during the pandemic because of factors related to test availability, variations in turnaround times, media and public health messaging, perceived risks as well as testing requirements for certain purposes. This, in turn, has impacted the number of persons testing positive and ultimately the results that we highlight in relation to trends over time. Finally, SARS-CoV-2 testing has been shown to be biased, with certain population groups including Black and African Americans being more likely to test for infection than others.
      • Rentsch C.T.
      • Kidwai-Khan F.
      • Tate J.P.
      • et al.
      Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: a nationwide cohort study.
      ,
      • Ferguson J.M.
      • Abdel Magid H.S.
      • Purnell A.L.
      • Kiang M.V.
      • Osborne T.F.
      Differences in COVID-19 testing and test positivity among veterans, United States, 2020.
      ,
      • Ballering A.V.
      • Oertelt-Prigione S.
      • Olde Hartman T.C.
      • Rosmalen J.G.M.
      Lifelines Corona Research Initiative
      Sex and gender-related differences in COVID-19 diagnoses and SARS-CoV-2 testing practices during the first wave of the pandemic: the Dutch lifelines COVID-19 cohort study.
      • Dalva-Baird N.P.
      • Alobuia W.M.
      • Bendavid E.
      • Bhattacharya J.
      Racial and ethnic inequities in the early distribution of U.S. COVID-19 testing sites and mortality.
      • Riou J.
      • Panczak R.
      • Althaus C.L.
      • et al.
      Socioeconomic position and the COVID-19 care cascade from testing to mortality in Switzerland: a population-based analysis.
      Demographic variations in test-seeking behavior could have impacted our results by introducing a proportionally higher case rate and an artificially inflated odds of hospitalization or death among this population group.
      In conclusion, we present the first cohort analysis of persons who have tested positive for SARS-CoV-2 infection in the tri-county area surrounding Oklahoma City. We show that in this part of the US, comorbidities and age are significant predictors of a severe outcome of SARS-CoV-2 infection and that the odds of being hospitalized or dying increases with age and number of comorbidities. Our study also includes novel considerations of initial symptoms and concludes that breathing difficulties in particular can lead to an increased likelihood of hospitalization and death. Because this study has analyzed all persons who tested positive for infection, regardless of their hospitalization status, we are able to make these conclusions which apply to a broader part of the population and especially the more diverse urban areas across the United States South-Central plains.

      Acknowledgments

      We are grateful for the support of the Oklahoma City County Health Department for providing access to and interpretation of data. We also thank Blaine Boding and Jessica Beetch for their general contributions to the study.

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