Covid19NCT04816318
Policy Responses Against the COVID-19 Pandemic in Latin America
An observational study of Covid19 and Infectious Disease, run by University of Chile at 1 site in Chile.
Trial summary
Latin America is one of the worst-hit areas from the COVID-19 pandemic worldwide. Policy responses to COVID-19 in Latin America have sought to reduce viral spread, increase the capacity of the health system response, mitigate negative consequences, and strengthen governance. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Covid19, Infectious Disease, Death
- What is being tested?
- Social and public health measures against COVID-19
- How many people
- 10,000 participants
- Where
- 1 siteChile
- Study dates
- 28 Apr 2021to 31 May 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The study covers the population of included countries in Latin America. Preliminarily this represents nine countries, covering 80.9% of the total population in Latin America
Who may be able to join
- Country will be eligible if they are (1) Spanish or Portuguese speaking countries in Latin America, (2) availability of open data at the subnational level for any of the outcomes
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
Chile1 site
Escuela de Salud Pública
Santiago, Santiago Metropolitan, 8380453
Helena Morais, MEcon
Maria José Monsalves, PhD
About this study
Official title: Policy Responses Against the COVID-19 Pandemic in Latin America: Interrupted Series Analyses of Local Governments
Latin America is one of the worst-hit areas from the COVID-19 pandemic worldwide. Policy responses to COVID-19 in Latin America have sought to reduce viral spread, increase the capacity of the health system response, mitigate negative consequences, and strengthen governance. Few studies have examined the effectiveness of COVID-19 policies in Latin America or explored subnational variation in their effectiveness.
In this observational study, the investigators will use a two-stage interrupted time series to estimate the effectiveness of nonpharmaceutical interventions in third-tier subnational units on SARS-COV2 transmission and COVID-19 mortality in Latin America. The investigators will estimate the effects in each local government, and then run a random-effects meta-analysis to obtain pooled effects for each intervention (and combinations of) and heterogeneity estimates. Finally, the investigators will explore potential explanations for the heterogeneity at the local level.
Read the detailed description
The COVID-19 pandemic is spreading rapidly worldwide. Latin America, the region with the highest income inequality, remains as one of the worst-hit areas worldwide. Despite accounting for 8.4% of the global population, Latin America has witnessed 20.3% of the total SARS-COV-2 cases and 30.2% of the COVID-19 deaths to date. Several countries in the region are among the worst-hit worldwide. Brazil has had more than 11 million SARS-COV-2 cases and Mexico, Argentina and Colombia have exceeded the 2 million cases each. Similarly, the five most populated countries in the region (Brazil, Argentina, Mexico, Colombia and Peru) exceed 600,000 SARS-COV-2-related deaths. The pandemic reached Latin America later than other continents, and the first case of COVID-19 in the region was reported in Brazil on February 26, followed by a case in Mexico on February 28, 2020 and subsequently spreading throughout the region during March 2020.
Policy responses to COVID-19 in Latin America have sought to reduce viral spread, increase the capacity of the health system response, mitigate negative consequences, and strengthen governance. Effectiveness studies of social distancing policies in China, India, European countries, the United States and worldwide have shown that these appear to be effective to reduce viral transmission.
Despite the heavy burden of the COVID-19 in Latin American countries, there have been few studies examining the effectiveness of COVID-19 policies. Likewise, few studies have explored variation at the local level in the effectiveness of COVID-19 policies. Inequalities in policy effectiveness can arise due to within-country differences at the local level due to their geographical, sociodemographic, mobility patterns, and governance differences. In Latin America, high levels of poverty, urban density, household crowding, lack of safety nets, unemployment and precarious work cluster geographically and coexist with structural inequities in governance and built environments, thus creating barriers for effective compliance with preventive recommendations and for the implementation of well-functioning contact tracing and isolation mechanisms. Understanding the effectiveness of policies at the local level and exploring potential explanations for effect heterogeneity is essential to reduce the burden of the ongoing COVID-19 pandemic and inform the preparedness for future pandemics.
In this study, the investigators aim, first, to estimate the effectiveness of nonpharmaceutical interventions on SARS-COV2 transmission and COVID-19 mortality in Latin America; second, to examine the effect heterogeneity of transmission and mortality at the local level. Third, assuming there is evidence of moderate to substantial heterogeneity at the local level, the investigators aim to explore potential explanations for this heterogeneity. The study will use an interrupted time series method to estimate their effects in each local government, and random effects meta-analysis and meta-regression to obtain pooled effects, heterogeneity estimates and potential explanations.
Methods Design and setting: Natural experiment exploiting the variation in the temporal and spatial implementation of policy interventions, aimed to reduce the spread and mortality of COVID-19 in Latin America. The unit of analysis are local governments, i.e. third-tier administrative levels such as municipalities, districts or cantons.
Eligibility criteria: See below. To date, eligible countries are Argentina, Brazil, Chile, Colombia, Costa Rica, Guatemala, Mexico, Paraguay, and Peru. These countries represent 80.9% of the population in Latin America, and the vast majority of SARS-CoV-2 cases and COVID-19 deaths.
Interventions: Interventions include (i) policies aimed at reducing viral transmission, (ii) policies aimed at increasing the capacity of the health system's response, and (iii) policies aimed at mitigating the negative consequences of the epidemic and potential adverse effects of interventions. We will use the PoliMap taxonomy to categorise the examined policies.
Comparator: Counterfactual outcome defined as the projection of the pre-intervention trend to simulate what would have happened if the policy had not occurred.
Data sources: COVID-19 cases and deaths data, as well as the covariates, from official government sources, such as the Ministry of Health and Ministry of Science and Technology. The intervention information will come from legal documents, official statements, and quantitative accounts from trustable sources.
Covariates: First model at the local level does not include covariates (see below). Second model (i.e. the meta-analysis), we will examine the change in heterogeneity after adjusting for several covariates at the local level. Local level covariates include projected population size in 2020, demographic density, age-structure of the population, household density and socioeconomic status. We will use data from official sources of information, primarily the latest national population census in each included country.
Statistical analysis: See the Statistical Analysis Plan for details on the modelling assumptions. The study will use an interrupted time series design, where each local government acts as its own control. The main strength of this design is its capacity to distinguish the effect of the intervention from secular change. The study will use a Poisson regression to model the count data (for both outcomes) and accounting for overdispersion and secular trends. A full discussion on potential biases and violations of assumptions can be found in the Statistical Analysis Plan.
In a second stage, the investigators will use random effects meta analysis to pool the effect estimates for each intervention or combination of interventions. This analysis informs whether any implemented intervention was effective to reduce COVID-19 cases and deaths and the degree of heterogeneity between the effects at the local level. If there is evidence of moderate to high levels of heterogeneity (defined as higher than 50%), the investigators will also use standard meta-regression techniques to assess whether local level determinants (see Covariates) can explain the observed heterogeneity. The investigators will build the models and test the analytical strategy using publicly available data on COVID-19 cases and deaths from Finland and Sweden from January 1 to March 31.
- Conditions
- Covid19, Infectious Disease, Death, SARS-CoV Infection
- Being tested
- Social and public health measures against COVID-19, Control
- Keywords
- Effectiveness, Health policy, Heterogeneity, Latin America
- Medical subject headings
- COVID-19, Communicable Diseases, Death, Severe Acute Respiratory Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Social and public health measures against COVID-19Other
Also called Non-pharmaceutical interventions against COVID-19
- Viral spread (for both outcomes) 1.1. Total lockdown 1.2 Partial lockdown (geographical, step-wise/graduated response) 1.3 Curfew 1.4 School closure 1.5 Closure of shopping malls, gyms, churches, parks 1.6 Remote work 1.7 Restrictions to national/subnational mobility 1.8 Prohibition of mass gatherings
- Health systems response (for COVID-19 deaths outcome) 2.1 Interventions to increase testing capacity 2.2 Interventions to increase the number of ICU/critical beds
- Mitigation strategies (for both outcomes) 3.1 Direct social assistance (in-kind/cash) 3.2 Cash transfer 3.3 Withdrawal of pension funds
ControlOther
The comparator is a counterfactual outcome defined as the projection of the pre-intervention trend to simulate what would have happened if the policy had not occurred (see Statistical Analysis Plan for definitions)
| Study group | Role | What they receive |
|---|---|---|
| Social and public health measures against COVID-19 | Public Health and Social measures against COVID-19. This group refers to the population exposed to public health and social measures against COVID-19 Receives: Other: Social and public health measures against COVID-19 | |
| Control | The comparator is the pre-intervention period Receives: Other: Control |
What is being measured
The primary outcome is the main question the study is designed to answer.
7-day moving average of daily confirmed cases of COVID-19/SARS-CoV-2
Measured: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
Time-varying reproductive number of confirmed cases of COVID-19/SARS-CoV-2
Measured: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
7-day moving average of daily confirmed deaths of COVID-19/SARS-CoV-2
Measured: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2021
- Study start28 Apr 2021 (estimated)
- Primary completion31 May 2021 (estimated)
- Study completion31 May 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (20)Papers the sponsor has linked to this study.
- Other trials for Covid19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Ecologic or community
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Chile
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sebastián Peña, University of Chile
- Investigators
- Sebastián Peña, MD, PhD, Principal investigator, Escuela de Salud PúblicaCristóbal Cuadrado, MD, PhD, Principal investigator, Escuela de Salud Pública
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04816318
- Sponsor's protocol ID
- ANID-COVID 0960
- Status last verified
- Apr 2021
- First submitted
- 23 Mar 2021
- Last updated
- 28 Apr 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The study uses open access data available at the municipal/district/canton level. The investigators will include the data and statistical code as a Supplementary Appendix in the published papers.
Publications
United Nations Department of Economic and Social Affairs. World Social Report 2020: Inequality in a rapidly changing world. New York, United States: United Nations; 2020
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. No abstract available.
PubMed 32087114The Lancet. COVID-19 in Brazil: "So what?". Lancet. 2020 May 9;395(10235):1461. doi: 10.1016/S0140-6736(20)31095-3. No abstract available.
PubMed 32386576Martinez-Gutierrez S., Cuadrado C., Peña S. Chile's response to the coronavirus pandemic. 2020. Available at: https://www.cambridge.org/core/blog/2020/04/11/chiles-response-to-the-coronavirus-pandemic/ (accessed April 12, 2020)
Ali ST, Wang L, Lau EHY, Xu XK, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions. Science. 2020 Aug 28;369(6507):1106-1109. doi: 10.1126/science.abc9004. Epub 2020 Jul 21.
PubMed 32694200Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, Huang J, He N, Yu H, Lin X, Wei S, Wu T. Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA. 2020 May 19;323(19):1915-1923. doi: 10.1001/jama.2020.6130.
PubMed 32275295Salvatore M, Basu D, Ray D, Kleinsasser M, Purkayastha S, Bhattacharyya R, Mukherjee B. Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations. BMJ Open. 2020 Dec 10;10(12):e041778. doi: 10.1136/bmjopen-2020-041778.
PubMed 33303462Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M; Imperial College COVID-19 Response Team; Ghani AC, Donnelly CA, Riley S, Vollmer MAC, Ferguson NM, Okell LC, Bhatt S. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020 Aug;584(7820):257-261. doi: 10.1038/s41586-020-2405-7. Epub 2020 Jun 8.
PubMed 32512579Hyafil A, Morina D. Analysis of the impact of lockdown on the reproduction number of the SARS-Cov-2 in Spain. Gac Sanit. 2021 Sep-Oct;35(5):453-458. doi: 10.1016/j.gaceta.2020.05.003. Epub 2020 May 23.
PubMed 32571528Siedner MJ, Harling G, Reynolds Z, Gilbert RF, Haneuse S, Venkataramani AS, Tsai AC. Correction: Social distancing to slow the US COVID-19 epidemic: Longitudinal pretest-posttest comparison group study. PLoS Med. 2020 Oct 6;17(10):e1003376. doi: 10.1371/journal.pmed.1003376. eCollection 2020 Oct.
PubMed 33022016Islam N, Sharp SJ, Chowell G, Shabnam S, Kawachi I, Lacey B, Massaro JM, D'Agostino RB Sr, White M. Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. BMJ. 2020 Jul 15;370:m2743. doi: 10.1136/bmj.m2743.
PubMed 32669358Candido DS, Claro IM, de Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, du Plessis L, Pereira RHM, Sales FCS, Manuli ER, Theze J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, da Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete CA Jr, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi AD, Braga CKV, Gerber AL, de C Guimaraes AP, Gaburo N Jr, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco RS Jr, Granja F, Garcia MT, Moretti ML, Perroud MW Jr, Castineiras TMPP, Lazari CS, Hill SC, de Souza Santos AA, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, de Sa CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML; Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Genomic Network; Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu CH, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. 2020 Sep 4;369(6508):1255-1260. doi: 10.1126/science.abd2161. Epub 2020 Jul 23.
PubMed 32703910Bennett M. All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile. World Dev. 2021 Jan;137:105208. doi: 10.1016/j.worlddev.2020.105208. Epub 2020 Sep 24.
PubMed 32994662Silva L, Figueiredo Filho D, Fernandes A. The effect of lockdown on the COVID-19 epidemic in Brazil: evidence from an interrupted time series design. Cad Saude Publica. 2020 Oct 19;36(10):e00213920. doi: 10.1590/0102-311X00213920. eCollection 2020.
PubMed 33084836Peña S., Cuadrado C., Rivera-Aguirre A., Hasdell R., Nazif-Munoz J., Yusuf M. et al. PoliMap: A taxonomy proposal for mapping and understanding the global policy response to COVID-19. OSF Preprint. 2020. Available at: https://osf.io/h6mvs (accessed March 22, 2021)
Study at a glance
From the registry record, last updated by the sponsor on 28 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Policy Responses Against the COVID-19 Pandemic in Latin America: Interrupted Series Analyses of Local Governments
- ClinicalTrials.gov ID
- NCT04816318
- Acronym / sponsor ID
- ANID-COVID 0960
- Conditions
- Covid19, Infectious Disease, Death, SARS-CoV Infection
- Keywords
- Effectiveness, Health policy, Heterogeneity, Latin America
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- University of ChileUniversity, hospital or other
- Study start
- 28 Apr 2021 (estimated)
- Primary completion
- 31 May 2021 (estimated)
- Study completion
- 31 May 2021 (estimated)
- First posted
- 25 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Latin America is one of the worst-hit areas from the COVID-19 pandemic worldwide. Policy responses to COVID-19 in Latin America have sought to reduce viral spread, increase the capacity of the health system response, mitigate negative consequences, and strengthen governance. Few studies have examined the effectiveness of COVID-19 policies in Latin America or explored subnational variation in their effectiveness.
In this observational study, the investigators will use a two-stage interrupted time series to estimate the effectiveness of nonpharmaceutical interventions in third-tier subnational units on SARS-COV2 transmission and COVID-19 mortality in Latin America. The investigators will estimate the effects in each local government, and then run a random-effects meta-analysis to obtain pooled effects for each intervention (and combinations of) and heterogeneity estimates. Finally, the investigators will explore potential explanations for the heterogeneity at the local level.
Detailed description
The COVID-19 pandemic is spreading rapidly worldwide. Latin America, the region with the highest income inequality, remains as one of the worst-hit areas worldwide. Despite accounting for 8.4% of the global population, Latin America has witnessed 20.3% of the total SARS-COV-2 cases and 30.2% of the COVID-19 deaths to date. Several countries in the region are among the worst-hit worldwide. Brazil has had more than 11 million SARS-COV-2 cases and Mexico, Argentina and Colombia have exceeded the 2 million cases each. Similarly, the five most populated countries in the region (Brazil, Argentina, Mexico, Colombia and Peru) exceed 600,000 SARS-COV-2-related deaths. The pandemic reached Latin America later than other continents, and the first case of COVID-19 in the region was reported in Brazil on February 26, followed by a case in Mexico on February 28, 2020 and subsequently spreading throughout the region during March 2020.
Policy responses to COVID-19 in Latin America have sought to reduce viral spread, increase the capacity of the health system response, mitigate negative consequences, and strengthen governance. Effectiveness studies of social distancing policies in China, India, European countries, the United States and worldwide have shown that these appear to be effective to reduce viral transmission.
Despite the heavy burden of the COVID-19 in Latin American countries, there have been few studies examining the effectiveness of COVID-19 policies. Likewise, few studies have explored variation at the local level in the effectiveness of COVID-19 policies. Inequalities in policy effectiveness can arise due to within-country differences at the local level due to their geographical, sociodemographic, mobility patterns, and governance differences. In Latin America, high levels of poverty, urban density, household crowding, lack of safety nets, unemployment and precarious work cluster geographically and coexist with structural inequities in governance and built environments, thus creating barriers for effective compliance with preventive recommendations and for the implementation of well-functioning contact tracing and isolation mechanisms. Understanding the effectiveness of policies at the local level and exploring potential explanations for effect heterogeneity is essential to reduce the burden of the ongoing COVID-19 pandemic and inform the preparedness for future pandemics.
In this study, the investigators aim, first, to estimate the effectiveness of nonpharmaceutical interventions on SARS-COV2 transmission and COVID-19 mortality in Latin America; second, to examine the effect heterogeneity of transmission and mortality at the local level. Third, assuming there is evidence of moderate to substantial heterogeneity at the local level, the investigators aim to explore potential explanations for this heterogeneity. The study will use an interrupted time series method to estimate their effects in each local government, and random effects meta-analysis and meta-regression to obtain pooled effects, heterogeneity estimates and potential explanations.
Methods Design and setting: Natural experiment exploiting the variation in the temporal and spatial implementation of policy interventions, aimed to reduce the spread and mortality of COVID-19 in Latin America. The unit of analysis are local governments, i.e. third-tier administrative levels such as municipalities, districts or cantons.
Eligibility criteria: See below. To date, eligible countries are Argentina, Brazil, Chile, Colombia, Costa Rica, Guatemala, Mexico, Paraguay, and Peru. These countries represent 80.9% of the population in Latin America, and the vast majority of SARS-CoV-2 cases and COVID-19 deaths.
Interventions: Interventions include (i) policies aimed at reducing viral transmission, (ii) policies aimed at increasing the capacity of the health system's response, and (iii) policies aimed at mitigating the negative consequences of the epidemic and potential adverse effects of interventions. We will use the PoliMap taxonomy to categorise the examined policies.
Comparator: Counterfactual outcome defined as the projection of the pre-intervention trend to simulate what would have happened if the policy had not occurred.
Data sources: COVID-19 cases and deaths data, as well as the covariates, from official government sources, such as the Ministry of Health and Ministry of Science and Technology. The intervention information will come from legal documents, official statements, and quantitative accounts from trustable sources.
Covariates: First model at the local level does not include covariates (see below). Second model (i.e. the meta-analysis), we will examine the change in heterogeneity after adjusting for several covariates at the local level. Local level covariates include projected population size in 2020, demographic density, age-structure of the population, household density and socioeconomic status. We will use data from official sources of information, primarily the latest national population census in each included country.
Statistical analysis: See the Statistical Analysis Plan for details on the modelling assumptions. The study will use an interrupted time series design, where each local government acts as its own control. The main strength of this design is its capacity to distinguish the effect of the intervention from secular change. The study will use a Poisson regression to model the count data (for both outcomes) and accounting for overdispersion and secular trends. A full discussion on potential biases and violations of assumptions can be found in the Statistical Analysis Plan.
In a second stage, the investigators will use random effects meta analysis to pool the effect estimates for each intervention or combination of interventions. This analysis informs whether any implemented intervention was effective to reduce COVID-19 cases and deaths and the degree of heterogeneity between the effects at the local level. If there is evidence of moderate to high levels of heterogeneity (defined as higher than 50%), the investigators will also use standard meta-regression techniques to assess whether local level determinants (see Covariates) can explain the observed heterogeneity. The investigators will build the models and test the analytical strategy using publicly available data on COVID-19 cases and deaths from Finland and Sweden from January 1 to March 31.
Study design
- Observational model
- Ecologic or community
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Social and public health measures against COVID-19 | – | Other: Social and public health measures against COVID-19Public Health and Social measures against COVID-19. This group refers to the population exposed to public health and social measures against COVID-19 |
| Control | – | Other: ControlThe comparator is the pre-intervention period |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study covers the population of included countries in Latin America. Preliminarily this represents nine countries, covering 80.9% of the total population in Latin America
Key inclusion criteria (1 listed)
- Country will be eligible if they are (1) Spanish or Portuguese speaking countries in Latin America, (2) availability of open data at the subnational level for any of the outcomes
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Social and public health measures against COVID-19Also: Non-pharmaceutical interventions against COVID-191. Viral spread (for both outcomes) 1.1. Total lockdown 1.2 Partial lockdown (geographical, step-wise/graduated response) 1.3 Curfew 1.4 School closure 1.5 Closure of shopping malls, gyms, churches, parks 1.6 Remote work 1.7 Restrictions to national/subnational mobility 1.8 Prohibition of mass gatherings 2. Health systems response (for COVID-19 deaths outcome) 2.1 Interventions to increase testing capacity 2.2 Interventions to increase the number of ICU/critical beds 3. Mitigation strategies (for both outcomes) 3.1 Direct social assistance (in-kind/cash) 3.2 Cash transfer 3.3 Withdrawal of pension funds | Other | Social and public health measures against COVID-19 |
| ControlThe comparator is a counterfactual outcome defined as the projection of the pre-intervention trend to simulate what would have happened if the policy had not occurred (see Statistical Analysis Plan for definitions) | Other | Control |
Primary outcome
7-day moving average of daily confirmed cases of COVID-19/SARS-CoV-2
Time frame: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
Time-varying reproductive number of confirmed cases of COVID-19/SARS-CoV-2
Time frame: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
7-day moving average of daily confirmed deaths of COVID-19/SARS-CoV-2
Time frame: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Apr 2021.
- BackgroundUnited Nations Department of Economic and Social Affairs. World Social Report 2020: Inequality in a rapidly changing world. New York, United States: United Nations; 2020
- Background: PubMed 32087114Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. No abstract available.
- Background: PubMed 32386576The Lancet. COVID-19 in Brazil: "So what?". Lancet. 2020 May 9;395(10235):1461. doi: 10.1016/S0140-6736(20)31095-3. No abstract available.
- BackgroundMartinez-Gutierrez S., Cuadrado C., Peña S. Chile's response to the coronavirus pandemic. 2020. Available at: https://www.cambridge.org/core/blog/2020/04/11/chiles-response-to-the-coronavirus-pandemic/ (accessed April 12, 2020)
- Background: PubMed 32694200Ali ST, Wang L, Lau EHY, Xu XK, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions. Science. 2020 Aug 28;369(6507):1106-1109. doi: 10.1126/science.abc9004. Epub 2020 Jul 21.
- Background: PubMed 32275295Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, Huang J, He N, Yu H, Lin X, Wei S, Wu T. Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA. 2020 May 19;323(19):1915-1923. doi: 10.1001/jama.2020.6130.
- Background: PubMed 33303462Salvatore M, Basu D, Ray D, Kleinsasser M, Purkayastha S, Bhattacharyya R, Mukherjee B. Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations. BMJ Open. 2020 Dec 10;10(12):e041778. doi: 10.1136/bmjopen-2020-041778.
- Background: PubMed 32512579Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M; Imperial College COVID-19 Response Team; Ghani AC, Donnelly CA, Riley S, Vollmer MAC, Ferguson NM, Okell LC, Bhatt S. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020 Aug;584(7820):257-261. doi: 10.1038/s41586-020-2405-7. Epub 2020 Jun 8.
- Background: PubMed 32571528Hyafil A, Morina D. Analysis of the impact of lockdown on the reproduction number of the SARS-Cov-2 in Spain. Gac Sanit. 2021 Sep-Oct;35(5):453-458. doi: 10.1016/j.gaceta.2020.05.003. Epub 2020 May 23.
- Background: PubMed 33022016Siedner MJ, Harling G, Reynolds Z, Gilbert RF, Haneuse S, Venkataramani AS, Tsai AC. Correction: Social distancing to slow the US COVID-19 epidemic: Longitudinal pretest-posttest comparison group study. PLoS Med. 2020 Oct 6;17(10):e1003376. doi: 10.1371/journal.pmed.1003376. eCollection 2020 Oct.
- Background: PubMed 32669358Islam N, Sharp SJ, Chowell G, Shabnam S, Kawachi I, Lacey B, Massaro JM, D'Agostino RB Sr, White M. Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. BMJ. 2020 Jul 15;370:m2743. doi: 10.1136/bmj.m2743.
- Background: PubMed 32703910Candido DS, Claro IM, de Jesus JG, Souza WM, Moreira FRR, Dellicour S, Mellan TA, du Plessis L, Pereira RHM, Sales FCS, Manuli ER, Theze J, Almeida L, Menezes MT, Voloch CM, Fumagalli MJ, Coletti TM, da Silva CAM, Ramundo MS, Amorim MR, Hoeltgebaum HH, Mishra S, Gill MS, Carvalho LM, Buss LF, Prete CA Jr, Ashworth J, Nakaya HI, Peixoto PS, Brady OJ, Nicholls SM, Tanuri A, Rossi AD, Braga CKV, Gerber AL, de C Guimaraes AP, Gaburo N Jr, Alencar CS, Ferreira ACS, Lima CX, Levi JE, Granato C, Ferreira GM, Francisco RS Jr, Granja F, Garcia MT, Moretti ML, Perroud MW Jr, Castineiras TMPP, Lazari CS, Hill SC, de Souza Santos AA, Simeoni CL, Forato J, Sposito AC, Schreiber AZ, Santos MNN, de Sa CZ, Souza RP, Resende-Moreira LC, Teixeira MM, Hubner J, Leme PAF, Moreira RG, Nogueira ML; Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Genomic Network; Ferguson NM, Costa SF, Proenca-Modena JL, Vasconcelos ATR, Bhatt S, Lemey P, Wu CH, Rambaut A, Loman NJ, Aguiar RS, Pybus OG, Sabino EC, Faria NR. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. 2020 Sep 4;369(6508):1255-1260. doi: 10.1126/science.abd2161. Epub 2020 Jul 23.
- PoliMap mapping visualisation tool and databaseLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. The study uses open access data available at the municipal/district/canton level. The investigators will include the data and statistical code as a Supplementary Appendix in the published papers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sebastián Peña, MD, PhD principal investigator
Escuela de Salud Pública
Cristóbal Cuadrado, MD, PhD principal investigator
Escuela de Salud Pública
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Covid19 and Infectious Disease, run by University of Chile at 1 site in Chile.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Apr 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Chile. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Sebastián Peña, MD, PhD (+358452451360, sebastian.penafajuri@thl.fi), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
7-day moving average of daily confirmed cases of COVID-19/SARS-CoV-2 (time frame: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04816318, which also lists 20 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Apr 2021.