These were: proportion of quarantined population “Q-proportion” among newly diagnosed COVID-19 cases/week, ratio of quarantined people to cases, and ratio of negative tests to new cases,
Trang 1Real world evidence of trace, test, isolation, and quarantine impact on the COVID-19
pandemic response performance
Juhwan Oh1, Seung-sik Hwang2*, Khuong Quynh Long3, Minkyung Kim4, Kunhee Park5, Seunghyun Kwon6,
Osvaldo Enrique Artaza Barrios7, Irene Torres8, Matthew M Kavanagh9, Naoki Kondo10, S Patrick Kaucher11,
Hoang Van Minh12, Dong Roman Xu13, Mikael Rostila14, Caroline Benski15, Mellissa Withers16, Borwornsom
Leerapan17, Myoungsoon You2, Cristiani Vieira Machado18, Chang-Chuan Chan19, Hwa-Young Lee20, Jeonghyun
Shin1, Hyejin Jeong21, Sung-In Kim22, Soon Ae Kim4, Soo Kyung Park23, Judith McCool24, Lawrence O Gostin9,
S.V Subramanian20, Jeffrey F Markuns25 27, Yun-Chul Hong1 27, Chris Bullen24 27, Jong-Koo Lee 1 27, Martin
McKee26 27
1Seoul National University College of Medicine, Seoul, Republic of Korea
2Seoul National University Graduate School of Public Health, Seoul, Republic of Korea
3Hasselt University, Hasselt, Belgium
4Korea Foundation for International Healthcare, Seoul, Republic of Korea
5Gyunggi Province in Suwon, Republic of Korea
6Korea Disease Control and Prevention in Osong, Republic of Korea
7The University of the Americas in Santiago, Chile
8Fundacion Octaedro in Quito, Ecuador,
9Georgetown University in Washington, D.C USA
10Kyoto University School of Public Health in Kyoto, Japan
11Mailman School of Public Health, Columbia University in New York, USA
12Hanoi University of Public Health, Hanoi, Vietnam
13Southern Medical University in Guangzhou, Guangdong, China
14Stockholm University in Stockholm, Sweden
15University Hospital of Geneva, Geneva, Switzerland
16University of Southern California in Los Angeles, CA, USA
17Mahidol University Faculty of Medicine Ramathibodi Hospital in Bangkok, Thailand
18Oswaldo Cruz Foundation in Rio de Janeiro, Brazil
19National Taiwan University School of Public Health, Taipei, Taiwan
20Harvard T H Chan School of Public Health, Boston, MA, USA
21Seoul National University Hospital, Seoul, Republic of Korea
22Daegu Correctional Institution in Dagegu, Republic of Korea
23National Health Insurance Research Institute in Wonju, Republic of Korea
Preprint not peer reviewed
Trang 224The University of Auckland School of Population Health, Auckland, New Zealand
25Boston University School of Medicine, Boston, MA, USA
26London School of Hygiene and Tropical Medicine, London, UK
27These authors contributed equally as co-senior authors
*Corresponding author:
Dr Seung-sik Hwang
cyberdoc@snu.ac.kr
Seoul National University Graduate School of Public Health, Seoul, Republic of Korea
Preprint not peer reviewed
Trang 3There is continuing uncertainty about the effectiveness of testing, tracing, isolation, and quarantine (TTIQ) policies
during the pandemic
Methods
We developed proxy indicators of the implementation of TTIQ policies at subnational and national (Republic of
Korea), and international level (111 countries) from the beginning of 2020 to September 2021 These were:
proportion of quarantined population (“Q-proportion”) among newly diagnosed COVID-19 cases/week, ratio of
quarantined people to cases, and ratio of negative tests to new cases, with higher values suggesting more complete
TTIQ We used linear regression to analyze the association between TTIQ indicators and 1-week lagged cases and
cumulative deaths, separating periods before and after vaccines becoming available
Findings
We found consistently inverse associations between TTIQ indicators and COVID-19 outcomes, with gradual
attenuation as vaccination coverage rose Q-proportion overall (β= -0·091; p-value < 0·001) and log-transformed
quarantined population per case (β ranges from -0·626; p < 0.001 to -0·288; p= 0·023) in each of 9 provinces were
negatively associated with log-transformed 1-week lagged incidence in Korea overall The strength of association
decreased with greater vaccination coverage The ratio of negative test results/new case was also inversely
associated with incidence (β= -1·19; p-value < 0·001) in Korea Globally, increasing negative test ratio was
significantly associated with lower cumulative cases and deaths per capita, more so earlier in the pandemic
Jurisdictions with lower vaccination coverage showed the strongest association
Interpretation
A real-world evaluation demonstrates an association between performance of testing, contact tracing, isolation, and
quarantine and better disease outcomes
Funding
Ministry of Foreign Affairs, Republic of Korea
Preprint not peer reviewed
Trang 4In many countries the focus of responses to COVID-19 changed in 2021 as vaccination and, to a lesser extent,
advances in therapeutics taking center stage.(1,2) The attractions are obvious The non-pharmaceutical interventions
(NPI) that proved so effective early in the pandemic(3,4) involved widespread social disruption, economic hardship,
and collateral damage to health services.(5,6) Yet this new approach has struggled to cope with the Delta variant,
even in those few countries that have achieved high rates of vaccine roll out.(7) Meanwhile, in large parts of the
world the prospect of achieving high vaccination rates remains a distant hope, for many reasons, reflecting problems
of both supply,(8)with inadequate production, a consequence in part of policies on intellectual property,(9)and
inequitable distribution,(10) and of demand, including weak health system infrastructure, vaccine hesitancy,(11) and
concerted campaigns of disinformation.(12) Even if high levels of global coverage could be achieved rapidly,
waning vaccine effectiveness and the emergence of new variants of concern capable of evading immunity induced
by current vaccines or past infection have challenged the hope that the first generation of vaccines would be the
‘magic bullet’.(13,14) Further outbreaks are inevitable(15) and NPIs will remain an essential part of the
armamentarium of policy responses.(16–19)
NPIs can be divided into general and targeted The former, affecting everyone living in a particular area, include
stay-at-home orders and school closures The latter apply to individuals and their contacts, based on testing, contact
tracing, and isolation and quarantine (TTIQ) The latter approach can be effective in certain circumstances without
the need for generalized restrictions, as seen in the 1918 influenza pandemic.(20) Yet, while previously
unimaginable advances in surveillance and testing should have made targeted approaches easier than in the past,
many countries have opted for generalized restrictions when cases rose.(21)
There are many reasons why this happened but, most often, it was because the capacity to implement TTIQ was
inadequate or overwhelmed This is apparent in countries where it worked, at least initially, as in New Zealand,
Vietnam, Taiwan, and Singapore).(22–25) They “bought time” by imposing generalized restrictions early, in some
cases helped by having invested substantially in preparedness following their experience with SARS in 2003 In
other countries, such as the United States, United Kingdom, and some countries in Western Europe, the initial
restrictions were delayed, with even a few days having major consequences, exacerbated by underinvestment in
preparedness and, in some, specific policy failures.(26,27) In others, particularly middle- and low-income countries
facing resource constraints, weak public health capacity, and in particular ability to undertake mass testing,(28)
limited what was possible
It is, however, only fair to recognize that policymakers faced with the threat of a pandemic were in a difficult
position It was not obvious how effective TTIQ would be and, even now, evaluations are limited, often based on
experience in particular settings, such as the U.S state of Oregon or Guangzhou in China.(4) There is thus an urgent
need to assess the effectiveness of TTIQ in limiting pandemic spread To this end, we have examined the
association between cumulative COVID-19 cases and deaths and the implementation of TTIQ interventions, taking
account of vaccination coverage, beginning with the experience of one country, the Republic of Korea, and
extending our analysis, to the extent possible given data availability, in 111 jurisdictions worldwide
Method
Study population and data The first analysis, of Korea, used three different data sources, the publicly available
national dataset from by Seoul National University Asia Research Center sourced from the Korean Disease Control
Agency: 1) cumulative COVID-19 cases, 2) deaths, 3) vaccinated people, and 4) number of negative test results of
Korea; the authors’ curation of 5) daily numbers of quarantined population per case from the daily reports of the
nine provincial governments, that report numbers quarantined; and the authors’ curation of 6) the proportion of the
quarantined population among newly diagnosed COVID-19 cases per week (“Q-proportion”) reporting from the
Korean Disease Control and Prevention Agency(29) which is publicly available every Monday The international
data covered 111 jurisdictions using three sets of publicly available data: cumulative COVID-19 cases, 2) deaths and
3) vaccinated people per million in each jurisdiction population retrieved from the dataset curated by Our World in
Data,(30) which is sourced from multiple national databases by the Center for Systems Science and Engineering at
Johns Hopkins University
Preprint not peer reviewed
Trang 5Variables Independent variables We used three proxy or latent variables to estimate the implementation of TTIQ
If the system is working well, the number of people tested and quarantined per case will be high as will the
proportion of negative tests (as a very high positivity rate suggests that true cases are being missed) In the national
analysis, where we had the most granular data, we first used the proportion of the quarantined population among
newly diagnosed COVID-19 cases per week This was based on the hypothesis that the greater the completeness of
tracing, the greater the proportion of newly confirmed cases that will be found among traced-quarantined population Conversely, when tracing is incomplete most newly confirmed cases will not occur among the quarantined
population Second, based on our hypothesis that more effective tracing would generate a greater number of
quarantined people for each newly confirmed case, we used the daily log-transformed number of quarantined people
per newly confirmed case at provincial level In both the national and global analysis, we used the log-transformed
country-specific ratio of cumulative negative tests per case as a proxy for effective TTIQ implementation on the
assumption that a more proactive tracing policy generates more negative test results per newly confirmed case
Outcome variables We used as our outcome measure the 1-week lagged newly confirmed cases per million for
national level and per thousand people for provincial level analysis of Korea For the global analysis we used the
cumulative number of new cases per million population, and the cumulative number of deaths per million population
in each country All outcome variables were log-transformed
Analyses In the national analysis, we examined associations between the 1-week lagged number of confirmed cases
per unit population (outcome variable) and each of the three proxy latent variables of TTIQ as independent
variables In the global analysis, we examined associations between the cumulative number of deaths per million
population (log) and number of negative tests per case (log) and the number of negative tests per case (negative test
results ratio); and associations between the cumulative number of deaths per million population (log) and number of
negative tests per case (log) We performed a subgroup analysis by designating an early phase (2020) and a late
phase (2021), based on when vaccines became available
For the Korean data analysis, reflecting data availability, we conducted three analyses First, we analyzed data from
the week of October 3rd-9th, 2020 through the week of September 12th-18th, 2021 for the Q-proportion As the
independent variable, we regressed the publicly available weekly Q-proportion with the outcome variable, which
was expressed as 7-day averaged daily COVID-19 cases by million, lagged by 1 week, and log transformed (Figure
1) Second, we analyzed the data from July 1, 2021 through September 14, 2021 for the quarantine analysis (the nine provinces) For the independent variable, we divided the daily numbers of quarantine population by the newly
confirmed COVID-19 cases (“Quarantined population per case ratio”), then we divided the ratio by 14 to account
for the mandatory 2 week quarantine period enforced by the Korean government, and finally log transformed the
ratio For the outcome variable, we divided daily COVID-19 cases by one thousand, lagged the values by 1 week,
and finally log transformed the values We divided nine provinces into two categories: (A) higher and (B) lower
vaccination coverage (Figure 2A, 2B, and supplementary table) Third, we analyzed the data from Feb 22 (100th case day), 2020 through Sep 16, 2021 for testing For the independent variable, we divided the number of negative tests
by the newly confirmed case, and log transformed the values For the outcome variable, we divided daily COVID-19 cases by million, lagged the values by 1 week, and finally log transformed the values (Figure 3)
The period for global analysis for negative test ratio was Jan 1, 2020 through Sep 16, 2021 For the independent
variable, we divided the number of negative tests by the newly confirmed case (“Negative test results ratio”), and
log transformed the ratio For the outcome variable, we divided the cumulative number of COVID-19 cases and
deaths by million, lagged the values by 1 week, and finally log transformed the values We divided 111 jurisdictions
into a tertile based on the vaccine coverage per million population (Figure 5)
We used linear regression analysis in Stata 17 software (StataCorp 2021 Stata Statistical Software: Release 17
College Station, TX: StataCorp LLC.): Level of significance with P=0.05
Results
In the national analysis the Q-proportions ranging between 29·9 and 66·4 %, were negatively associated with the
log-transformed 1-week lagged new case incidence per million population (β= -0·091; p-value < 0·001) during the
period Oct 3-9, 2020, to Sep 12-18, 2021 (Figure 1) The log transformed quarantined population per newly
confirmed case (range 5 to 33 quarantined people per case (mean) in each province) was also negatively associated
Preprint not peer reviewed
Trang 6with the log transformed 1-week lagged confirmed new daily cases in the 9 Korean provinces, of which Seoul
(population vaccination rate rank 6 of 9 provinces with 50% coverage) showed the largest association (β = -0·626;
p-value < 0·001), the province with the lowest vaccinated population (47·7%) showed the 2nd largest association
(β= -0·603; p-value < 0·001), whereas the province with the most vaccinated population (58.3%) showed the
smallest association (β= -0·288; p-value = 0·023 during the period July 1, 2021 to September 14, 2021 (Figure 2;
supplementary table) The magnitude of the inverse associations was attenuated when vaccination coverage rates
were increased (β = -1·253; p-value < 0·001) The log-transformed ratio of negative test results per daily confirmed
new case (ranging from 7 to 2981 negative test results per case) were also inversely associated with the 1-week
lagged incidence of cases per million-unit population (β= -1·22 p-value < 0·001) during the period of Feb 22, 2020
to Sep 16, 2021 (Figure 3)
Turning to the global analysis, Figure 4 shows the distribution of cumulative cases or deaths per million population
for each country against the ratio of cumulative negative tests per case in 2020 and 2021 The highest negative test
ratio was in China (1890) at the end of 2020 and in Hong Kong (5573) at the end of 2021, while the lowest was in
Brazil in both periods, respectively The association between the negative test ratio (log) and cumulative cases per
population (log) in the global analyses were all significantly negative in all periods and both the early and the late
period subgroups The association was stronger for cumulative deaths (Figure 5A) than cumulative cases (Figure
5B) With cumulative deaths (Figure 5A), the association in the early phase was stronger (β= -0·95 (95% CI: -1·15
to -0·75); p < 0.001) than in the later phase (β= -0·60 (95% CI: -0·80 to -0·40); p < 0·001) A similar pattern was
found with cumulative cases (Figure 5B): the association in the early phase was stronger (β= -0·77 (95% CI: -0·96
to -0·57); p < 0·001) than in the later phase (β= -0·31 (95% CI: -0·50 to -0·12); p < 0·001) Consistently inverse
associations with gradual attenuation were found when the analysis was stratified by periods before and after
vaccines became available — in 2021, jurisdictions in the lowest tertile of vaccination coverage showed a stronger
inverse association (β= -1·51 (95% CI: -2·07 to -0·94); p < 0·001) than the highest tertile (β= -0·81 (95% CI: -1·11
to -0·51); p < 0·001), with a similar pattern in COVID-19 incidence (Figure 5C)
Discussion
Our study has some important limitations, most related to the validity of the data Especially in the global analysis,
we are dependent on the coverage, quality, and consistency of the data Early in the pandemic, the ability to track
incidence was limited by testing capacity everywhere and, even now, this remains the case in many places
Notwithstanding guidance from WHO, there are national differences in how deaths are attributed to COVID-19,(31)
as well as gaps in surveillance coverage in many parts of the world(29,32) and even, in some countries, possible data falsification.(33) This analysis does not allow us to isolate the TTIQ impact from the many other variables that
influence COVID-19 responses: i.e behavioral characteristics such as adherence to physical distancing and face
covering guidelines as well as indoor ventilation use and performance However, given the complex nature of these
relationships the analytic challenges of disentangling these factors would be formidable even if data on the omitted
variables were available
These results suggest that proactive implementation of TTIQ is associated with both reduced numbers of COVID-19
cases and deaths across multiple countries The greater Q-proportion (an indicator of effective contact-tracing), the
lower the 1-week lagged incidence A higher number of quarantined people per new case and a higher negative test
ratio were also associated with fewer cases one week later Globally, a higher negative test results ratio was
associated with fewer cumulative deaths and cases, in both 2020 and 2021 In a further analysis limited to the period
when vaccines became available, in 2021, it appears that TTIQ was more effective in countries unable to reach high
vaccination rates (noting that while these tended to be poorer countries with less testing capacity many also had
lower COVID burdens) The value of TTIQ was sustained, even as vaccine roll out has proceeded in both analyses
Our results support the value of TTIQ even with increasing vaccination rates, a finding that is likely to assume
greater importance should new variants with greater vaccine escape become widespread and because of likely
political unwillingness to impose further large-scale lockdowns in many countries, especially given evidence that the latter vary in their effectiveness.(34)
Conclusion
Preprint not peer reviewed
Trang 7We provide empirical evidence of the effectiveness of TTIQ in reducing cases and deaths using a real-world
evaluation, offering support for continued investment in the capacity to implement these measures However, further
studies, such as that using the Korean data above, are needed to corroborate our findings, linked to mixed methods
studies to understand how best to implement this approach in different contexts
Preprint not peer reviewed
Trang 8References
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Preprint not peer reviewed
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Preprint not peer reviewed