SEROLOGICAL TESTING OF BLOOD DONORS TO CHARACTERISE THE IMPACT OF COVID-19 IN MELBOURNE, AUSTRALIA, 2020 Article Swipe
Dorothy A. Machalek
,
Kaitlyn Vette
,
Marnie Downes
,
John B. Carlin
,
Suellen Nicholson
,
Rena Hirani
,
David O. Irving
,
Iain B. Gosbell
,
Heather F. Gidding
,
Hannah Shilling
,
Eithandee Aung
,
Kristine Macartney
,
John Kaldor
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.03.11.22272185
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.03.11.22272185
Rapidly identifying and isolating people with acute SARS-CoV-2 infection has been a core strategy to contain COVID-19 in Australia, but a proportion of infections go undetected. We estimated SARS-CoV-2 specific antibody prevalence (seroprevalence) among blood donors in metropolitan Melbourne following a COVID-19 outbreak in the city between June and September 2020. The aim was to determine the extent of infection spread and whether seroprevalence varied demographically in proportion to reported cases of infection. The design involved stratified sampling of residual specimens from blood donors (aged 20–69 years) in three postcode groups defined by low (7 cases/1,000 population) COVID-19 incidence based on case notification data. All specimens were tested using the Wantai SARS-CoV-2 total antibody assay. Seroprevalence was estimated with adjustment for test sensitivity and specificity for the Melbourne metropolitan blood donor and residential populations, using multilevel regression and poststratification. Overall, 4,799 specimens were collected between 23 November and 17 December 2020. Seroprevalence for blood donors was 0.87% (90% credible interval: 0.25–1.49%). The highest estimates, of 1.13% (0.25–2.15%) and 1.11% (0.28–1.95%), respectively, were observed among donors living in the lowest socioeconomic areas (Quintiles 1 and 2) and lowest at 0.69% (0.14–1.39%) among donors living in the highest socioeconomic areas (Quintile 5). When extrapolated to the Melbourne residential population, overall seroprevalence was 0.90% (0.26–1.51%), with estimates by demography groups similar to those for the blood donors. The results suggest a lack of extensive community transmission and good COVID-19 case ascertainment based on routine testing during Victoria’s second epidemic wave. Residual blood donor samples provide a practical epidemiological tool for estimating seroprevalence and information on population patterns of infection, against which the effectiveness of ongoing responses to the pandemic can be assessed.
Related Topics
Concepts
Seroprevalence
Medicine
Population
Serology
Outbreak
Incidence (geometry)
Demography
Epidemiology
Socioeconomic status
Coronavirus disease 2019 (COVID-19)
Veterinary medicine
Environmental health
Immunology
Antibody
Internal medicine
Virology
Disease
Infectious disease (medical specialty)
Physics
Optics
Sociology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.03.11.22272185
- https://www.medrxiv.org/content/medrxiv/early/2022/03/13/2022.03.11.22272185.full.pdf
- OA Status
- green
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221066708
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221066708Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.03.11.22272185Digital Object Identifier
- Title
-
SEROLOGICAL TESTING OF BLOOD DONORS TO CHARACTERISE THE IMPACT OF COVID-19 IN MELBOURNE, AUSTRALIA, 2020Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-13Full publication date if available
- Authors
-
Dorothy A. Machalek, Kaitlyn Vette, Marnie Downes, John B. Carlin, Suellen Nicholson, Rena Hirani, David O. Irving, Iain B. Gosbell, Heather F. Gidding, Hannah Shilling, Eithandee Aung, Kristine Macartney, John KaldorList of authors in order
- Landing page
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https://doi.org/10.1101/2022.03.11.22272185Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2022/03/13/2022.03.11.22272185.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2022/03/13/2022.03.11.22272185.full.pdfDirect OA link when available
- Concepts
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Seroprevalence, Medicine, Population, Serology, Outbreak, Incidence (geometry), Demography, Epidemiology, Socioeconomic status, Coronavirus disease 2019 (COVID-19), Veterinary medicine, Environmental health, Immunology, Antibody, Internal medicine, Virology, Disease, Infectious disease (medical specialty), Physics, Optics, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.blood | 35, 83, 139, 163, 232, 258 |
| abstract_inverted_index.cases | 71 |
| abstract_inverted_index.data. | 113 |
| abstract_inverted_index.donor | 140, 259 |
| abstract_inverted_index.those | 229 |
| abstract_inverted_index.three | 89 |
| abstract_inverted_index.total | 122 |
| abstract_inverted_index.using | 118, 144 |
| abstract_inverted_index.wave. | 256 |
| abstract_inverted_index.which | 277 |
| abstract_inverted_index.(>7 | 104 |
| abstract_inverted_index.(<3 | 95 |
| abstract_inverted_index.Wantai | 120 |
| abstract_inverted_index.assay. | 124 |
| abstract_inverted_index.design | 75 |
| abstract_inverted_index.donors | 36, 84, 164, 184, 201 |
| abstract_inverted_index.during | 252 |
| abstract_inverted_index.extent | 58 |
| abstract_inverted_index.groups | 91, 226 |
| abstract_inverted_index.living | 185, 202 |
| abstract_inverted_index.lowest | 188, 196 |
| abstract_inverted_index.medium | 98 |
| abstract_inverted_index.people | 5 |
| abstract_inverted_index.second | 254 |
| abstract_inverted_index.spread | 61 |
| abstract_inverted_index.tested | 117 |
| abstract_inverted_index.varied | 65 |
| abstract_inverted_index.years) | 87 |
| abstract_inverted_index.20–69 | 86 |
| abstract_inverted_index.Rapidly | 1 |
| abstract_inverted_index.against | 276 |
| abstract_inverted_index.between | 47, 154 |
| abstract_inverted_index.contain | 16 |
| abstract_inverted_index.defined | 92 |
| abstract_inverted_index.donors. | 233 |
| abstract_inverted_index.highest | 172, 205 |
| abstract_inverted_index.ongoing | 281 |
| abstract_inverted_index.overall | 217 |
| abstract_inverted_index.provide | 261 |
| abstract_inverted_index.results | 235 |
| abstract_inverted_index.routine | 250 |
| abstract_inverted_index.samples | 260 |
| abstract_inverted_index.similar | 227 |
| abstract_inverted_index.suggest | 236 |
| abstract_inverted_index.testing | 251 |
| abstract_inverted_index.whether | 63 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.COVID-19 | 17, 42, 107, 245 |
| abstract_inverted_index.December | 159 |
| abstract_inverted_index.November | 156 |
| abstract_inverted_index.Overall, | 149 |
| abstract_inverted_index.Residual | 257 |
| abstract_inverted_index.antibody | 31, 123 |
| abstract_inverted_index.credible | 168 |
| abstract_inverted_index.epidemic | 255 |
| abstract_inverted_index.involved | 76 |
| abstract_inverted_index.observed | 182 |
| abstract_inverted_index.outbreak | 43 |
| abstract_inverted_index.pandemic | 285 |
| abstract_inverted_index.patterns | 273 |
| abstract_inverted_index.postcode | 90 |
| abstract_inverted_index.reported | 70 |
| abstract_inverted_index.residual | 80 |
| abstract_inverted_index.sampling | 78 |
| abstract_inverted_index.specific | 30 |
| abstract_inverted_index.strategy | 14 |
| abstract_inverted_index.(Quintile | 208 |
| abstract_inverted_index.Melbourne | 39, 137, 214 |
| abstract_inverted_index.September | 50 |
| abstract_inverted_index.assessed. | 288 |
| abstract_inverted_index.collected | 153 |
| abstract_inverted_index.community | 241 |
| abstract_inverted_index.determine | 56 |
| abstract_inverted_index.estimated | 28, 127 |
| abstract_inverted_index.estimates | 223 |
| abstract_inverted_index.extensive | 240 |
| abstract_inverted_index.following | 40 |
| abstract_inverted_index.incidence | 108 |
| abstract_inverted_index.infection | 9, 60 |
| abstract_inverted_index.interval: | 169 |
| abstract_inverted_index.isolating | 4 |
| abstract_inverted_index.practical | 263 |
| abstract_inverted_index.responses | 282 |
| abstract_inverted_index.specimens | 81, 115, 151 |
| abstract_inverted_index.(Quintiles | 191 |
| abstract_inverted_index.Australia, | 19 |
| abstract_inverted_index.SARS-CoV-2 | 8, 29, 121 |
| abstract_inverted_index.adjustment | 129 |
| abstract_inverted_index.demography | 225 |
| abstract_inverted_index.estimates, | 173 |
| abstract_inverted_index.estimating | 267 |
| abstract_inverted_index.infection, | 275 |
| abstract_inverted_index.infection. | 73 |
| abstract_inverted_index.infections | 24 |
| abstract_inverted_index.multilevel | 145 |
| abstract_inverted_index.population | 272 |
| abstract_inverted_index.prevalence | 32 |
| abstract_inverted_index.proportion | 22, 68 |
| abstract_inverted_index.regression | 146 |
| abstract_inverted_index.stratified | 77 |
| abstract_inverted_index.cases/1,000 | 96, 100, 105 |
| abstract_inverted_index.identifying | 2 |
| abstract_inverted_index.information | 270 |
| abstract_inverted_index.population) | 101, 106 |
| abstract_inverted_index.population, | 216 |
| abstract_inverted_index.residential | 142, 215 |
| abstract_inverted_index.sensitivity | 132 |
| abstract_inverted_index.specificity | 134 |
| abstract_inverted_index.undetected. | 26 |
| abstract_inverted_index.Victoria’s | 253 |
| abstract_inverted_index.extrapolated | 211 |
| abstract_inverted_index.metropolitan | 38, 138 |
| abstract_inverted_index.notification | 112 |
| abstract_inverted_index.population), | 97 |
| abstract_inverted_index.populations, | 143 |
| abstract_inverted_index.transmission | 242 |
| abstract_inverted_index.ascertainment | 247 |
| abstract_inverted_index.effectiveness | 279 |
| abstract_inverted_index.respectively, | 180 |
| abstract_inverted_index.socioeconomic | 189, 206 |
| abstract_inverted_index.(0.14–1.39%) | 199 |
| abstract_inverted_index.(0.25–2.15%) | 176 |
| abstract_inverted_index.0.25–1.49%). | 170 |
| abstract_inverted_index.Seroprevalence | 125, 161 |
| abstract_inverted_index.seroprevalence | 64, 218, 268 |
| abstract_inverted_index.(0.26–1.51%), | 221 |
| abstract_inverted_index.(0.28–1.95%), | 179 |
| abstract_inverted_index.demographically | 66 |
| abstract_inverted_index.epidemiological | 264 |
| abstract_inverted_index.(seroprevalence) | 33 |
| abstract_inverted_index.poststratification. | 148 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5019977582 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I1331142282, https://openalex.org/I31746571 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.03923107 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |