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SARS-CoV-2 Seroprevalence in Tamil Nadu in October-November 2020

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Abstract and Figures

A population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.
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SARS-CoV-2 Seroprevalence in Tamil Nadu in October-November 2020
Anup Malani, Sabareesh Ramachandran, Vaidehi Tandel, Rajeswari Parasa,
S. Sudharshini, V. Prakash, Y. Yogananth, S. Raju, T.S. Selvavinayagam*
Abstract
A population-representative serological study was conducted in all districts of the state of Tamil
Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence
was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged
from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas
(36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to
males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher
seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated
seroprevalence implies that at least 22.6 million persons were infected by the end of
November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies
an infection fatality rate of 0.052%.
Introduction
Knowledge of population-level immunity is critical for understanding the epidemiology of SARS-
CoV-2 (COVID-19) and formulating effective infection control, including the allocation of scarce
vaccines. Tamil Nadu is the 6th most populous state in India, with roughly 72 million persons1. It
has reported roughly 820,000 COVID-19 cases and 12,000 deaths, ranked 4th and 2nd highest,
respectively, among Indian states2. Reported cases are not, however, gathered from
population-representative samples.
The state conducted a population-level seroprevalence survey of 26,640 adults across the 37
districts of the state in October-November 2020. We report seroprevalence estimates from this
survey by district, by demographic groups, and by urban status. We also compare the results of
the survey to estimates from reported cases to measure the degree to which serological
surveys underestimate population immunity.
Methods
* Malani: University of Chicago, USA; Ramachandran: University of California San Diego, USA; Tandel: independent;
Parasa: IDFC Institute, India; Sudharshini, Prakash, Yogananth, Raju, and Selvavinayagam: Directorate of Public
Health & Preventative Medicine, Government of Tamil Nadu.
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.03.21250949doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
2
This study was approved by the Directorate of Public Health & Preventative Medicine,
Government of Tamil Nadu, and the Institutional Ethics Committee of Madras Medical College,
Chennai, India.
Outcomes. The first primary endpoint of the study is the rate of positive results on CLIA
antibody tests at the district-level. The second primary endpoint is seroprevalence rates at the
district-level once seropositivity rates are adjusted for test inaccuracy.
There are multiple secondary endpoints. One set is seroprevalence (a) by age categories and
sex, (b) by rural or urban status, and (c) at the state level. A final secondary outcome is the
difference between population immunity estimated by serological survey and by reported
cases.
Sample and location. Individuals residing in Tamil Nadu and ages 18 years and older were
eligible for this study. The exclusion criteria were refusal to consent and contraindication to
venipuncture.
Sample size. The state sought to sample roughly 300 persons per 1 million population based on
the 2011 Indian Census. The state’s population is organized into districts, districts are
organized into health unit districts (HUD), and HUDs are organized into clusters, defined as a
street in urban areas and habitations in rural areas. Within each cluster the study aimed to
sample 30 individuals. Therefore, the sample rate can be converted into a target number of
clusters per HUD. In total 888 clusters were sampled.
Sample selection. The study selected participants within a HUD in three steps. First, within
each HUD, the study randomly selected clusters. Second, within each cluster, we selected a
random GPS starting point. Third, we sampled one participant from households adjacent to
that starting point until 30 persons consented within a cluster. Within each household, the
member asked to provide a biosample was selected via the Kish method3.
Data collection and timing. Data was collected between October 19 - November 30, 2020.
Each participant was asked to complete a health questionnaire and provide 5ml venous blood
collected in EDTA vacutainers. Serum was analyzed for IgG antibodies to the SARS-CoV-2 spike
protein using either the iFlash-SARS-CoV-2 IgG (Shenzhen YHLO Biotech; sensitivity of 95.9%
and specificity of 95.7% per manufacturer)4 or the Vitros anti-SARS-CoV-2 IgG CLIA kit (Ortho-
Clinical Diagnostics; sensitivity of 90% and specificity of 100% per manufacturer)5. Clusters
were characterized as rural if they were in Census-defined villages. The government shared
data on each reported COVID-19 case and death, including date and demographics.
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Statistical analysis. We estimate the proportion of positive CLIA tests by district by estimating
a weighted logit regression of test result on district indicators and reporting the inverse logit of
the coefficient for each district indicator. Observations are weighted by the inverse of sampling
probability for their age and gender groups. Standard errors calculated account for correlations
at the cluster level.
We estimate the seroprevalence by district in two steps. First, we calculate the weighted
proportion of positive tests at the district level everywhere except Chennai, where we calculate
it at the health unit district (HUD), a subset of districts. All samples in a district use the same
CLIA kit, except in Chennai, where all samples in a HUD do so. We estimate a weighted logit
regressions of test results on district indicators outside Chennai and HUD indicators in Chennai
and take the inverse logit of the coefficient for each jurisdiction indicator. Observations are
weighted by the inverse of sampling probability for their age and gender groups. Standard
errors are clustered at the cluster level. Second, for each jurisdiction, we predict
seroprevalence using the Rogan-Gladden formula, test parameters for the kit used in each
jurisdiction, and regression estimates of seropositive proportion by jurisdiction. In Chennai
district, we calculate seroprevalence at the district level as a weighted average of
seroprevalence at the HUD level using as weights the share of clusters in each HUD. (We
employ this approach to Chennai in the estimators below.)
We estimate the seroprevalence in the state by aggregating the seroprevalence across districts
weighted by 2011 census data on the relative populations of districts.
We estimate seroprevalence by demographic group in three steps. First, we calculate the
proportion of positive tests at the jurisdiction-by-demographic group level using logit
regressions of test results on jurisdiction-by-demographic group indicators. Demographic
groups indicators are sex x age for 6 age bins. Standard errors are clustered at the cluster level.
Second, we predict district-by-demographic group level seroprevalence using the Rogan-
Gladden formula. Third, we compute the weighted average of seroprevalence at the
demographic-group level using as weights the share of demographic-group population in each
district using data from the 2011 Indian census.
We estimate seroprevalence by urban status in the same manner we estimate it for
demographic groups with two changes. First, we use the urban status of a cluster in lieu of
demographic status of an individual at each step. Second, observations in our regression are
weighted by inverse of the sampling probability for their urban status.
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We estimate the infection fatality rate (IFR) for a population (defined by state, district,
demographic group, or urban status) by dividing total SARS-CoV-2-related deaths reported as of
two days after the last date of sampling in a district by the estimated size of previously infected
persons in that population. We obtain data on deaths by district and demographic group from
daily reports from the Tamil Nadu government. The date of the death count reflects that fact
that the delay the delay between infection and death is on average two days longer than the
delay between infection and seroconversion.6,7
We estimate the size of a population that was previously infected by multiplying our
seroprevalence estimates for that population by the size of those populations as reported in
the 2011 census. We estimate the degree of undercounting of cases in a population by dividing
the estimated number previously infected in the population by the number of officially
reported cases in that population as of 1 week before the median sampling date (median
October 23, 2020). We obtain data on officially reported cases from the government of Tamil
Nadu. The lag accounts for the delay both between infection and seropositive status and
between infection and prevalence testing.
We calculate the Pearson’s correlation coefficient between IFR and age at the individual level,
between undercounting rate and testing rate (tests per million as of median date of testing) by
district, and between the number of SARS-CoV-2-related deaths and the testing rate by district.
Statistical tests comparing groups are performed using a two-sided Wald test with 95%.
All statistical analyses were conducted with Microsoft Excel 365 (Microsoft, USA) and Stata 16
(StataCorp, USA). All plots were generated in R.
Results
One person aged 16 was incorrectly consented and dropped from the analysis. The study was
unable to obtain lab results for 287 additional observations. The study employs 26,135 test
results. Because numerous clusters had fewer than 30 persons consenting, the study yielded
292 samples per million population.
Table 1 reports the demographic characteristics of the sample. Age is missing for 8 persons and
6 persons are transgender. The sample has substantially more females and fewer persons age
18-29 than the general population.
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Seropositivity varies dramatically across the state, from 12.1% in The Nilgris to 49.3% in
Perambalur district (Figure 1). Seroprevalence has a similar pattern to seropositivity (Figure 2).
Seroprevalence is significantly greater in urban areas (36.9%; rural, 26.9%; p<0.001) (Table 2).
State level seroprevalence is 31.6% (95% CI: 30.4-32.8%).
Seroprevalence is not significantly different across sexes (females, 30.8%; males; 30.3%; p=0.25)
(Table 2). Seroprevalence among the elderly (70+: 25.8%) is significantly lower than among the
working age populations (age 40-49: 31.6%; p<0.001) or the young (18-29: 30.7%; p<0.001),
respectively (Table 2).
The IFR varies substantially across the state, from 0.007% in Perambalur to 0.203% in Chennai
(Table 3). The IFR increases with age (𝜌=0.8791; p=0.021) and is higher among males (0.11%)
than among females (0.04%; p=<0.001) (Table 4).
Ratio of actual cases to confirmed cases ranges from 9 in Chennai to 144 in Perambalur (Table
3). There is a negative and significant (𝜌=-0.58; p<0.001) correlation between testing rate and
the undercount rate (Figure 3).
Discussion
Overall seroprevalence (31.6%) implies that at least 22.7 million persons were infected by
November 30, 2020, the last day of serological sampling. Thus, the actual number of infections
is roughly 36 times larger than the number of confirmed cases, which totaled 670.392 by 15
October 2020, 7 days before the date the median biosample is collected.
Seroprevalence is highest among working age populations. The lower seroprevalence among
the young is not informative about the value of closing schools because the young in our
sample were over age 18. Moreover, the difference in seroprevalence among the young and
working ages is not significant. The significantly lower seroprevalence among the old is
different than what was found in a recent Karnataka seroprevalence study8 and suggests that
the elderly in Tamil Nadu have been somewhat protected even in multigenerational
households.
Higher seroprevalence in urban areas is consistent with higher density in urban districts (Figure
4). It does not seem positively correlated with mobility as measured by average decline in non-
residential Google mobility measures.
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The IFR across the state is 0.052%, nominally lower than that in Karnataka8 and Mumbai9. The
lower IFR is not due to lower infection rate amongst the elderly because IFR is nominally lower
even among the elderly. Higher IFR among males is consistent with the literature10.
Our study has several limitations. One is that, because antibody concentrations in infected
persons decline over time11, our estimate of seroprevalence may underestimate the level of
prior infection and perhaps natural immunity.
Second, we may underestimate IFR. The number of deaths per million is positively correlated
(𝜌=0.96; p<0.001) with testing rate per million at the district level (Table 3). Perhaps increasing
the testing rate would show greater deaths from SARS-CoV-2.
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Figures and tables
Figure 1. Proportion of positive CLIA tests by district.
Figure 2. Seroprevalence by district.
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Figure 3. Relationship between rate of undercounting and testing rate.
Notes. Each point represents a district. The x-axis presents
the number of tests in a district divided by the 2011 Census
population in that district. The y-axis presents the ratio of
actual cases to confirmed cases. Actual cases are the
estimated seroprevalence (%) in the district times its 2011
Census population. The confirmed cases are counts up to 7
days before the median date of serological sampling in the
district.
Figure 4. Relationship between seroprevalence, population density, and mobility.
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Table 1. Demographics of sample, as compared to 2011 Census.
Sample
Mean
Lower
bound
Upper
bound
Gender
Female
61%
60%
61%
Male
39%
39%
40%
Age
18-29
23%
22%
23%
30-39
23%
23%
24%
40-49
20%
20%
21%
50-59
16%
16%
17%
60-69
11%
11%
12%
70+
6%
6%
6%
Obs.
26,135
Note. Census 2011 number for ages 18-29 includes only
those ages 20-29.
Table 2. Seroprevalence by type of region, sex, and age.
Variable
Seropre-
valence
CI lower
bound
CI upper
bound
Region
Rural
0.251
0.242
0.261
Urban
0.367
0.357
0.377
Sex
Male
0.304
0.296
0.312
Female
0.321
0.311
0.33
Age
18-29
0.311
0.303
0.318
30-39
0.32
0.312
0.327
40-49
0.333
0.325
0.34
50-59
0.332
0.324
0.339
60-69
0.284
0.277
0.291
70+
0.252
0.245
0.258
Note. Calculated over a sample representative of the
entire state.
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Table 3. Infection fatality rate, undercount of infections, and testing by district.
District Deaths
Confirmed
cases
Tests
conducted
Seropre-
valence (%)
Population IFR (%)
Ratio of
actual to
confirmed
cases
Ariyalur 47 4191 76279 26.52% 754894 0.0235% 48
Chengalpattu 673 40241 399263 34.19% 2556244 0.0770% 22
Chennai 3862 207390 1350491 40.94% 4646732 0.2030% 9
Coimbatore 557 37932 458054 20.43% 3458045 0.0789% 19
Cuddalore 271 22170 263947 33.37% 2605914 0.0312% 39
Dharmapuri 48 4954 100746 19.06% 1506843 0.0167% 58
Dindigul 186 9465 168422 26.88% 2159775 0.0320% 61
Erode 124 8644 207777 18.88% 2251744 0.0292% 49
Kallakurichi 103 9802 133384 38.66% 1370281 0.0194% 54
Kancheepuram 385 23941 328692 34.30% 1166401 0.0962% 17
Kanniyakumari 246 14158 226184 35.40% 1870374 0.0372% 47
Karur 44 3664 65578 16.16% 1064493 0.0256% 47
Krishnagiri 106 5857 68403 18.92% 1883731 0.0297% 61
Madurai 424 18014 364893 38.00% 3038252 0.0367% 64
Nagapattinam 110 5959 93702 21.99% 1616450 0.0309% 60
Namakkal 94 7845 125838 17.04% 1726601 0.0320% 37
Perambalur 21 2010 43049 51.05% 565223 0.0073% 144
Pudukkottai 148 10001 137332 25.21% 1618345 0.0363% 41
Ramanathapuram 127 5778 105173 35.30% 1353445 0.0266% 83
Ranipet 177 14326 128022 45.09% 1210277 0.0324% 38
Salem 425 25144 377688 22.44% 3482056 0.0544% 31
Sivagangai 126 5580 107979 26.68% 1339101 0.0353% 64
Tenkasi 153 7702 108215 48.24% 1407627 0.0225% 88
Thanjavur 221 14486 257680 26.58% 2405890 0.0346% 44
The Nilgiris 38 5510 120113 11.12% 735394 0.0465% 15
Theni 193 15888 170530 44.33% 1245899 0.0349% 35
Thiruchirappalli 168 11645 196207 32.79% 2722290 0.0188% 77
Thiruvarur 99 8620 133411 21.56% 1264277 0.0363% 32
Thoothukudi 132 14391 198278 37.91% 1750176 0.0199% 46
Tirunelveli 208 13684 198389 43.47% 1665253 0.0287% 53
Tirupathur 117 5843 113189 23.93% 1111812 0.0440% 46
Tiruppur 176 10209 187803 19.71% 2479052 0.0360% 48
Tiruvallur 619 35320 450341 34.85% 3728104 0.0476% 37
Tiruvannamalai 262 16804 216551 36.18% 2464875 0.0294% 53
Vellore 306 16854 208871 27.72% 1614242 0.0684% 27
Villupuram 105 12712 205430 32.25% 2093003 0.0156% 53
Virudhunagar 221 14980 266562 37.92% 1942288 0.0300% 49
Notes. Death counts are up to 2 days after date of serological sampling. Test (RT-PCR, not serological)
and confirmed case counts are up to 7 days before the median date of serological sampling.
Population is from 2011 Census. Ratio of actual cases to confimred cases uses seroprevlanece times
the population as the numerator and confirmed cases as the denominator.
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Table 4. Infection fatality rate by sex and age.
Age
Sex
Deaths
Seropre-
valence
IFR
0-17
Female
18
N/A
18-29
Female
50
29.15%
0.002%
30-39
Female
119
32.65%
0.006%
40-49
Female
301
32.58%
0.019%
50-59
Female
660
32.67%
0.060%
60-69
Female
974
28.65%
0.143%
70+
Female
1091
27.84%
0.266%
0-17
Male
14
N/A
18-29
Male
71
32.21%
0.003%
30-39
Male
249
28.87%
0.015%
40-49
Male
681
30.53%
0.045%
50-59
Male
1761
31.35%
0.164%
60-69
Male
2491
28.81%
0.380%
70+
Male
3235
25.28%
0.923%
Notes. Death numbers are total up to 2 days after last date of
surveillance. IFR for ages -17 is unavailable because this group is
excluded from the sero-surveillance sampling.
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12
SUPPLEMENT
Seroprevalence in Tamil Nadu in October-November 2020
Anup Malani, Vaidehi Tandel, Rajeswari Parasa, Sabareesh Ramachandran,
S. Sudharshini, V. Prakash, Y. Yoganathan, S. Raju, T.S. Selvavinayagam
Methods
Sampling. Numerous clusters had less or more than 30 persons sampled. We report those in
Table S 1. We retain all samples because it is unclear which samples to drop from clusters with
>30 observations.
Table S 1. Number of samples per cluster.
Samples
per cluster
Number
of clusters
1
4
4
1
8
1
10
1
11
1
20
1
24
1
26
1
28
10
29
31
30
818
31
16
32
2
36
1
Sample size. Sampling 300 per million would lead to different sample sizes per district.
Because some clusters yielded less than 30 persons per cluster, the study produced just 292
samples per 1 million population. The following table presents the sample size obtained and
the resulting minimum detectable effect in each district. These are reported in Table S 2.
Assuming a design effect of 2, the implied minimum detectable effect (MDE) per district varies
from 3.3 (Chennai) to 13.6 (Nagapattinam) percentage points.
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13
Table S 2. Sample size obtained per district and implied minimum detectable effect.
Sample. Suspected or confirmed current or prior COVID-19 infection was not an exclusion
criterion. If a participant was currently receiving medical care for COVID-19, a family member
or proxy was used to complete the questionnaire on the participant’s behalf; however, the
blood sample was taken from the participant.
Data collection. Blood was collected in EDTA vacutainers. Serum was isolated and stored in
Eppendorf tubes. Serum was analyzed using either of two chemiluminescent immunoassay
(CLIA) kits.
The first kit was the iFlash-SARS-CoV-2 IgG kit from Shenzhen YHLO Biotech. Per the
manufacturer, it has a sensitivity of 95.9% (95% CI: 93.3-97.5%) and specificity of 95.7% (95% CI:
92.5-97.6%)4. Independent analysis estimated a sensitivity of 93% (95% CI: 84.397.7%) and
specificity of 92.9% (95% CI: 85.397.4%)12.
District
Sample
size
MDE District
Sample
size
MDE
Ariyalur 270 0.119 Ramanathapuram 480 0.089
Chengalpattu 720 0.073 Ranipet 420 0.096
Chennai 3613 0.033 Salem 1260 0.055
Coimbatore 1182 0.057 Sivagangai 450 0.092
Cuddalore 870 0.066 Tenkasi 420 0.096
Dharmapuri 567 0.082 Thanjavur 834 0.068
Dindigul 717 0.073 The Nilgiris 223 0.131
Erode 745 0.072 Theni 420 0.096
Kallakurichi 596 0.080 Thiruchirappalli 957 0.063
Kancheepuram 480 0.089 Thiruvarur 420 0.096
Kanniyakumari 659 0.076 Thoothukudi 547 0.084
Karur 390 0.099 Tirunelveli 630 0.078
Krishnagiri 690 0.075 Tirupathur 329 0.108
Madurai 1140 0.058 Tiruppur 740 0.072
Mayiladuthurai 320 0.110 Tiruvallur 745 0.072
Nagapattinam 209 0.136 Tiruvannamalai 810 0.069
Namakkal 600 0.080 Vellore 592 0.081
Perambalur 209 0.136 Villupuram 594 0.080
Pudukkottai 599 0.080 Virudhunagar 688 0.075
Note. MDE is calculated assuming a prevalence of 0.5, confidence level of
95%, and a design effect of 2.
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14
The second kit was the Vitros anti-SARS-CoV-2 IgG CLIA from Ortho-Clinical Diagnostics. Per the
manufacturer it has 90% sensitivity (95% CI: 76.3-97.2%) and 100% specificity (95% CI: 99.1
100.0%)5. FDA evaluation suggests it has 100% sensitivity (95% CI: 88.7-100%) and 100%
specificity (95% CI: 95.4-100%)13. Independent analysis estimated that it has a sensitivity of
98.8% (95% CI: 92.9-100%) and specificity of 97.3% (95% CI: 85-100%)14.
All the samples in a district are analyzed using the same kit, with the exception of the Chennai.
In Chennai 2 HUDs used 1 kit, one used the other. Table S 3 reports the test kit used in each
district.
Table S 3. Test kit used in each district.
District
Type of Kit
District
Type of Kit
Ariyalur
CPC Kit
Ramanathapuram
CPC Kit
Chengalpattu
Ortho Kit
Ranipet
Ortho Kit
Chennai
CPC & Ortho kits*
Salem
Ortho Kit
Coimbatore
Ortho Kit
Sivagangai
CPC Kit
Cuddalore
CPC Kit
Tenkasi
CPC Kit
Dharmapuri
Ortho Kit
Thanjavur
CPC Kit
Dindigul
CPC Kit
The Nilgiris
Ortho Kit
Erode
Ortho Kit
Theni
CPC Kit
Kallakurichi
CPC Kit
Thiruchirappalli
CPC Kit
Kancheepuram
Ortho Kit
Thiruvarur
CPC Kit
Kanniyakumari
CPC Kit
Thoothukudi
CPC Kit
Karur
CPC Kit
Tirunelveli
CPC Kit
Krishnagiri
Ortho Kit
Tirupathur
Ortho Kit
Madurai
CPC Kit
Tiruppur
Ortho Kit
Mayiladuthurai
CPC Kit
Tiruvallur
Ortho Kit
Nagapattinam
CPC Kit
Tiruvannamalai
Ortho Kit
Namakkal
Ortho Kit
Vellore
Ortho Kit
Perambalur
CPC Kit
Villupuram
CPC Kit
Pudukkottai
CPC Kit
Virudhunagar
CPC Kit
Notes. 33 out of 122 clusters in Chennai used the CPC test kit.
Statistical analysis.
Nagapattinam district was split into Nagapattinam and Mayiladuthurai districts in March 2020,
after the state started reported data on confirmed cases but before we conducted our
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.03.21250949doi: medRxiv preprint
15
serological survey. We aggregate these two districts together in our estimates of seropositivity
and seroprevalence.
In Chennai, we do not have the population by HUDs. Since the samples were drawn
proportional to population, we divide the district population across the HUDs in proportion to
the sample size.
When estimating our district-level seroprevalence, the weights for our regression analysis
employ data from the 2011 Census for the population in each age x gender category in each
district. We estimate the sampling probability for demographic group (age category x sex) as
the number of observations in that group in the sample in a district divided by the census
population in that group in a district.
When estimating our urban- and rural-level seroprevalence, the weights for our regression
analysis employ data from the 2011 Census for the population in each urban/rural category in
each district. We estimate the sampling probability for urban/rural group as the number of
observations in that group in the sample in a district divided by the census population in that
group in a district.
We calculate the sampling probabilities for each regression observation at the level of 2011-
defined districts (of which there are 32) rather than the 2020-defined districts (of which there
are 38), HUDs or clusters because the population is available only at the level of the old 32
districts. Likewise, we calculate district weights when we aggregate estimates across districts
using the 32, 2001 districts. The 38, 2020 districts are all the same or bifurcations of the 32,
2011 districts. Fortunately, in all bifurcated districts, the same kit was used. Therefore, we can
combine all bifurcated districts into older 2011 districts for purposes of calculating sampling
probabilities in regression analyses or weights when aggregating estimates.
Results
Table S 4 provides the data behind Figure 2, which reports seroprevalence by district. Table S 5
reports seroprevalence by district and demographics.
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.03.21250949doi: medRxiv preprint
16
Table S 4. Seroprevalence by district.
District
Seropre-
valence
CI lower
bound
CI upper
bound
District
Seropre-
valence
CI lower
bound
CI upper
bound
Ariyalur
0.275 0.213 0.337
Ramanathapuram
0.358 0.255 0.461
Chengalpattu
0.346 0.289 0.402 Ranipet 0.453 0.38 0.526
Chennai
0.41 0.382 0.437 Salem 0.224 0.176 0.272
Coimbatore
0.21 0.157 0.263 Sivagangai 0.263 0.175 0.351
Cuddalore
0.351 0.291 0.41 Tenkasi 0.472 0.39 0.555
Dharmapuri
0.195 0.124 0.266 Thanjavur 0.271 0.221 0.321
Dindigul
0.257 0.174 0.339 The Nilgiris 0.114 0.021 0.206
Erode
0.165 0.116 0.214 Theni 0.449 0.344 0.554
Kallakurichi
0.384 0.314 0.454 Thiruchirappalli 0.328 0.274 0.383
Kancheepuram
0.345 0.279 0.411 Thiruvarur 0.215 0.144 0.286
Kanniyakumari
0.343 0.261 0.425 Thoothukudi 0.392 0.325 0.46
Karur
0.16 0.059 0.26 Tirunelveli 0.446 0.368 0.524
Krishnagiri
0.191 0.126 0.256 Tirupathur 0.234 0.138 0.33
Madurai
0.388 0.337 0.439 Tiruppur 0.191 0.138 0.244
Mayiladuthurai
0.273 0.165 0.38 Tiruvallur 0.341 0.291 0.391
Nagapattinam
0.128 -0.023 0.278 Tiruvannamalai 0.359 0.287 0.431
Namakkal
0.173 0.102 0.244 Vellore 0.293 0.225 0.362
Perambalur
0.493 0.386 0.6 Villupuram 0.334 0.273 0.395
Pudukkottai
0.254 0.185 0.323 Virudhunagar 0.388 0.288 0.489
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.03.21250949doi: medRxiv preprint
17
Table S 5. Seroprevalence by district and demographic group.
Female Male
District 18-29 30-39 40-49 50-59 60-69 70+ 18-29 30-39 40-49 50-59 60-69 70+
Ariyalur 16.8% 29.3% 24.6% 22.8% 24.3% 50.1% 29.8% 37.5% 19.8% 39.2% 50.1% 15.4%
Chengalpattu 34.1% 31.8% 40.8% 36.3% 32.6% 18.3% 43.3% 25.6% 39.8% 39.4% 31.1% 10.8%
Chennai 44.3% 41.7% 49.1% 44.0% 30.7% 43.7% 39.2% 35.4% 40.3% 47.0% 33.0% 29.8%
Coimbatore 15.9% 25.9% 21.9% 25.5% 21.6% 21.6% 21.5% 15.5% 22.1% 23.2% 14.6% 11.4%
Cuddalore 27.6% 43.6% 40.3% 51.4% 40.7% 25.8% 35.8% 29.6% 21.6% 34.5% 24.4% 5.9%
Dharmapuri 10.7% 16.5% 22.1% 26.8% 20.8% 16.1% 19.0% 26.6% 21.0% 22.4% 10.9% 16.1%
Dindigul 19.2% 33.7% 17.4% 24.0% 16.5% 28.7% 36.1% 28.9% 28.7% 31.9% 27.4% 21.2%
Erode 20.5% 13.9% 17.9% 13.8% 15.0% 18.4% 33.4% 26.7% 11.8% 13.6% 17.2% 3.7%
Kallakurichi 37.1% 29.0% 44.0% 58.7% 45.9% 19.8% 34.6% 38.7% 48.8% 39.2% 31.9% 39.2%
Kancheepuram 30.7% 50.8% 42.1% 26.1% 27.5% 68.1% 28.6% 40.4% 34.4% 26.8% 40.4% 17.7%
Kanniyakumari 34.8% 40.1% 42.0% 28.8% 24.5% 19.8% 39.7% 38.0% 39.2% 29.6% 40.7% 18.1%
Karur 11.3% 12.1% 26.4% 17.4% 29.1% 1.9% 15.4% 13.7% 14.8% 7.2% 9.2% 56.2%
Krishnagiri 21.5% 20.3% 18.5% 19.8% 37.8% N/A 17.8% 17.7% 18.0% 17.7% 10.3% 50.8%
Madurai 37.9% 34.7% 36.7% 48.9% 40.2% 38.0% 39.1% 25.8% 36.7% 46.9% 39.2% 54.0%
Nagapattinam 14.1% 16.9% 23.3% 22.8% 14.5% 16.0% 24.5% 24.1% 28.9% 38.6% 22.8% 12.8%
Namakkal 19.0% 19.3% 13.6% 17.4% 7.8% 4.5% 20.3% 18.0% 18.7% 16.1% 24.8% 7.4%
Perambalur 55.6% 50.1% 63.8% 60.0% 50.1% 9.2% 47.6% 54.3% 65.0% 44.0% 20.7% 39.2%
Pudukkottai 31.2% 26.6% 20.6% 23.5% 13.7% 54.3% 19.0% 18.9% 31.1% 30.4% 14.3% 42.3%
Ramanathapuram 44.8% 30.9% 33.0% 39.6% 51.6% 40.0% 23.8% 38.2% 41.7% 22.8% 28.9% 36.5%
Ranipet 47.3% 50.8% 41.1% 44.3% 63.8% 40.4% 56.2% 23.5% 38.8% 46.4% 47.7% 24.8%
Salem 9.3% 25.4% 24.4% 27.6% 18.6% 12.8% 24.3% 23.7% 29.8% 31.1% 23.8% 13.0%
Sivagangai 26.7% 26.7% 32.9% 22.8% 22.2% 16.5% 27.2% 22.8% 34.8% 20.3% 37.8% 14.5%
Tenkasi 50.8% 47.8% 55.6% 46.2% 38.7% 41.0% 51.6% 44.7% 44.0% 46.2% 65.0% 31.9%
Thanjavur 22.1% 28.8% 27.9% 27.2% 17.4% 31.9% 26.4% 24.9% 36.3% 22.8% 24.9% 31.9%
The Nilgiris 9.2% 24.8% 8.8% 13.6% 50.8% 50.8% 6.6% 9.2% 13.6% 16.1% 5.3% 13.6%
Theni 41.0% 45.3% 59.9% 62.7% 37.7% 41.5% 50.1% 50.1% 18.9% 31.9% 53.7% 24.3%
Thiruchirappalli 29.1% 32.8% 31.0% 34.4% 33.6% 38.4% 32.3% 30.2% 44.4% 32.6% 20.1% 42.3%
Thiruvarur 17.8% 21.5% 31.9% 25.0% 10.1% 50.1% 23.3% 19.8% 22.8% 17.4% 34.8% 0.5%
Thoothukudi 36.5% 42.6% 41.5% 36.8% 42.3% 13.7% 38.7% 39.2% 35.0% 51.7% 34.5% 13.7%
Tirunelveli 36.2% 46.7% 42.8% 52.1% 49.2% 27.2% 49.0% 37.8% 41.3% 44.4% 50.1% 48.3%
Tirupathur 19.2% 37.8% 20.0% 11.2% 32.0% 28.5% 25.9% 30.4% 9.2% 31.9% 50.8% 21.9%
Tiruppur 15.2% 18.3% 17.6% 19.6% 12.5% 6.8% 26.2% 23.4% 19.2% 26.8% 23.1% 14.1%
Tiruvallur 38.7% 39.8% 30.5% 31.8% 28.8% 47.3% 44.2% 32.0% 24.8% 24.2% 36.6% 22.8%
Tiruvannamalai 40.1% 35.5% 35.1% 30.3% 36.2% 17.1% 31.2% 37.0% 45.0% 37.2% 35.9% 48.3%
Vellore 28.1% 38.8% 35.5% 34.4% 14.4% 26.8% 35.7% 9.5% 29.6% 10.6% 28.9% 23.9%
Villupuram 36.1% 36.9% 41.7% 31.9% 45.7% 36.5% 26.7% 28.8% 17.4% 33.1% 22.8% 40.5%
Virudhunagar 34.6% 41.6% 47.5% 41.7% 30.4% 33.7% 30.7% 45.9% 35.5% 48.5% 22.0% 33.3%
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... Among children, the greatest reductions in mortality were evident at ages 5-9 years (appendix 2 p 9). At older ages, observed deaths translated to increases in all-cause mortality of between 12% (11)(12)(13)(14) at ages 30-39 years and 55% (53-57) at ages 60-69 years, with similar agespecific patterns observed among men and women (appendix 2 pp 7-8). Total excess mortality spanned 0·40 (0·35-0·45) to 96·90 (93·35-100·16) deaths per 1000 individuals between ages 30-39 years and age 80 years or older (table 1). ...
... Although the USA, UK, Italy, and Spain have older populations than that of India, these countries recorded 1·6-2·1 excess deaths per 1000 residents through June, 2021, 6 compared with 5·2 in Chennai. Seroprevalence studies in Chennai identified 41% prevalence of antibody reactivity in October-November, 2020, 11 and 82% in June-July, 2021, 12 at the conclusion of the first and second waves. Our findings show considerable excess mortality associated with this uncontrolled SARS-CoV-2 spread, confirming predictions from early modelling studies 13,14 and underscoring the practical limitations of efforts to mitigate COVID-19 mortality through shielding of older or high-risk individuals amid extensive community transmission. ...
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Objectives To estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries. Design Cross-sectional study. Setting India, multiple regions representing combined population >150 million. Participants Aggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths. Primary and secondary outcome measures The primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age. Results Among males aged 50–89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries. Conclusions Estimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.
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Introduction Fervorous investigation and dialogue surrounding the true number of SARS-CoV-2 related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nations devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India, through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from April 1, 2020 to June 30, 2021. Methods Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch), were searched on July 3, 2021 (with results verified through August 15, 2021). Altogether using a two-step approach, 4,765 initial citations were screened resulting in 37 citations included in the narrative review and 19 studies with 41 datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analyze IFR1 which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provide lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2 related IFRs in India. We also try to stratify our empirical results across the first and the second wave. In tandem, we present updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from April 1, 2020 to June 30, 2021. Results For India countrywide, underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3-29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4-11.9 with cumulative excess deaths ranging from 1.79-4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067 - 0.140) and 0.365% (95% CI: 0.264 - 0.504) to 0.485% (95% CI: 0.344 - 0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, the IFR1 generally appear to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290-1.316) to (0.241-0.651) %). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097 - 0.116) and 0.367% (95% CI: 0.358 - 0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data with the disadvantages being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion When incorporating case and death underreporting, the meta-analyzed cumulative infection fatality rate in India varies from 0.36%-0.48%, with a case underreporting factor ranging from 25-30 and a death underreporting factor ranging from 4-12. This implies, by June 30, 2021, India may have seen nearly 900 million infections and 1.7-4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (covid19india.org) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.
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Background Progress in characterising the humoral immune response to Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) has been rapid but areas of uncertainty persist. Assessment of the full range of evidence generated to date to understand the characteristics of the antibody response, its dynamics over time, its determinants and the immunity it confers will have a range of clinical and policy implications for this novel pathogen. This review comprehensively evaluated evidence describing the antibody response to SARS-CoV-2 published from 01/01/2020-26/06/2020. Methods Systematic review. Keyword-structured searches were carried out in MEDLINE, Embase and COVID-19 Primer. Articles were independently screened on title, abstract and full text by two researchers, with arbitration of disagreements. Data were double-extracted into a pre-designed template, and studies critically appraised using a modified version of the Public Health Ontario Meta-tool for Quality Appraisal of Public Health Evidence (MetaQAT) tool, with resolution of disagreements by consensus. Findings were narratively synthesised. Results 150 papers were included. Most studies (113 or 75%) were observational in design, were based wholly or primarily on data from hospitalised patients (108, 72%) and had important methodological limitations. Few considered mild or asymptomatic infection. Antibody dynamics were well described in the acute phase, up to around three months from disease onset, but the picture regarding correlates of the antibody response was inconsistent. IgM was consistently detected before IgG in included studies, peaking at weeks two to five and declining over a further three to five weeks post-symptom onset depending on the patient group; IgG peaked around weeks three to seven post-symptom onset then plateaued, generally persisting for at least eight weeks. Neutralising antibodies were detectable within seven to 15 days following disease onset, with levels increasing until days 14–22 before levelling and then decreasing, but titres were lower in those with asymptomatic or clinically mild disease. Specific and potent neutralising antibodies have been isolated from convalescent plasma. Cross-reactivity but limited cross-neutralisation with other human coronaviridae was reported. Evidence for protective immunity in vivo was limited to small, short-term animal studies, showing promising initial results in the immediate recovery phase. Conclusions Literature on antibody responses to SARS-CoV-2 is of variable quality with considerable heterogeneity of methods, study participants, outcomes measured and assays used. Although acute phase antibody dynamics are well described, longer-term patterns are much less well evidenced. Comprehensive assessment of the role of demographic characteristics and disease severity on antibody responses is needed. Initial findings of low neutralising antibody titres and possible waning of titres over time may have implications for sero-surveillance and disease control policy, although further evidence is needed. The detection of potent neutralising antibodies in convalescent plasma is important in the context of development of therapeutics and vaccines. Due to limitations with the existing evidence base, large, cross-national cohort studies using appropriate statistical analysis and standardised serological assays and clinical classifications should be prioritised.
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The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2-14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3-4 days without truncation and at 5-9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.
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The role of serologic testing for SARS-CoV-2, both in the clinical and public health settings, will continue to evolve as we gain increasing insight into our immune response to the virus. Here, we evaluated four high throughput serologic tests for detection of anti-SARS-CoV-2 IgG antibodies, including assays from Abbott Laboratories (Abbott Park, IL), Epitope Diagnostics Inc. (San Diego, CA), Euroimmun (Lubeck, Germany), and Ortho-Clinical Diagnostics (Rochester, NY), using a panel of serially collected serum samples (N=224) from 56 patients with confirmed COVID-19, healthy donor sera from 2018 and a cross-reactivity serum panel collected in early 2020. Sensitivity of the Abbott, Epitope, Euroimmun and Ortho-Clinical IgG assays in convalescent serum samples collected more than 14 days post symptom onset or initial positive RT-PCR result was 92.9% (78/84), 88.1% (74/84), 97.6% (82/84) and 98.8% (83/84), respectively. Among unique convalescent patients, sensitivity of the Abbott, Epitope, Euroimmun and Ortho-Clinical anti-SARS-CoV-2 IgG assays was 97.3% (36/37), 73% (27/37), 94.6% (35/37) and 97.3% (36/37), respectively. Overall assay specificity and positive predictive values based on a 5% prevalence rate are 99.6%/92.8%, 99.6%/90.6%, 98.0%/71.2% and 99.6%/92.5%, respectively, for the Abbott, Epitope, Euroimmun and Ortho-Clinical IgG assays. In conclusion, we show high sensitivity in convalescent sera and high specificity for the Abbott, Euroimmun and Ortho-Clinical anti-SARS-CoV-2 IgG assays. With the unprecedented influx of commercially available serologic tests for detection of antibodies against SARS-CoV-2, it remains imperative that laboratories thoroughly evaluate such assays for accuracy prior to implementation.
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Background The evaluation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) specific antibody (Ab) assay performances is of the utmost importance in establishing and monitoring virus spread in the community. In this study focusing on IgG antibodies, we compare reliability of three chemiluminescent (CLIA) and two enzyme linked immunosorbent (ELISA) assays. Methods Sera from a total of 271 subjects, including 64 reverse transcription-polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 patients were tested for specific Ab using Maglumi (Snibe), Liaison (Diasorin), iFlash (Yhlo), Euroimmun (Medizinische Labordiagnostika AG) and Wantai (Wantai Biological Pharmacy) assays. Diagnostic sensitivity and specificity, positive and negative likelihood ratios were evaluated using manufacturers’ and optimized thresholds. Results Optimized thresholds (Maglumi 2 kAU/L, Liaison 6.2 kAU/L and iFlash 15.0 kAU/L) allowed us to achieve a negative likelihood ratio and an accuracy of: 0.06 and 93.5% for Maglumi; 0.03 and 93.1% for Liaison; 0.03 and 91% for iFlash. Diagnostic sensitivities and specificities were above 93.8% and 85.9%, respectively for all CLIA assays. Overall agreement was 90.3% (Cohen’s kappa = 0.805 and SE = 0.041) for CLIA, and 98.4% (Cohen’s kappa = 0.962 and SE = 0.126) for ELISA. Conclusions The results obtained indicate that, for CLIA assays, it might be possible to define thresholds that improve the negative likelihood ratio. Thus, a negative test result enables the identification of subjects at risk of being infected, who should then be closely monitored over time with a view to preventing further viral spread. Redefined thresholds, in addition, improved the overall inter-assay agreement, paving the way to a better harmonization of serologic tests.
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In modern survey methods growing emphasis is placed on the objective selection of the sample. For surveys of the general population, increasing use is made of area sampling to obtain probability samples of households. Heretofore, scant attention has been given to the question of how to make an objective selection among the members of the household.A procedure for selecting objectively one member of the household is given as used in four surveys of the adult population. Demographic data as found in the sample are compared with outside sources for available factors.* Presented at the 107th Annual Meeting of the American Statistical Association, New York City, December 30, 1947.
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Customer Notification: Sensitivity and Specificity of iFlash-SARS-Cov-2 IgG and IgM kits from
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Shenzhen YHLO Biotech Co. Ltd. Customer Notification: Sensitivity and Specificity of iFlash-SARS-Cov-2 IgG and IgM kits from Clinical Trials 2020.
Seroprevalence of anti-SARS-CoV-2 IgG antibodies in
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Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet 2020; 396(10247): 313-9.