Policy Responses Against the COVID-19 Pandemic in Latin America
NCT ID: NCT04816318
Last Updated: 2021-04-28
Study Results
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Basic Information
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UNKNOWN
10000 participants
OBSERVATIONAL
2021-04-28
2021-05-31
Brief Summary
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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.
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Detailed Description
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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
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Study Design
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ECOLOGIC_OR_COMMUNITY
PROSPECTIVE
Study Groups
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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
Social and public health measures against COVID-19
1. 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
Control
The comparator is the pre-intervention period
Control
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)
Interventions
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Social and public health measures against COVID-19
1. 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
Control
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)
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
Yes
Sponsors
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University of Chile
OTHER
Responsible Party
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Sebastián Peña
Co-investigator
Principal Investigators
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Sebastián Peña, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Escuela de Salud Pública
Cristóbal Cuadrado, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Escuela de Salud Pública
Locations
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Escuela de Salud Pública
Santiago, Santiago Metropolitan, Chile
Countries
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Central Contacts
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References
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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.
The 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.
Martinez-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.
Pan 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.
Salvatore 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.
Flaxman 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.
Hyafil 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.
Siedner 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.
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Candido 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.
Bennett 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.
Silva 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.
Peña 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)
Li Y, Campbell H, Kulkarni D, Harpur A, Nundy M, Wang X, Nair H; Usher Network for COVID-19 Evidence Reviews (UNCOVER) group. The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries. Lancet Infect Dis. 2021 Feb;21(2):193-202. doi: 10.1016/S1473-3099(20)30785-4. Epub 2020 Oct 22.
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Provided Documents
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Document Type: Study Protocol
Document Type: Statistical Analysis Plan
Related Links
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PoliMap mapping visualisation tool and database
Other Identifiers
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ANID-COVID 0960
Identifier Type: -
Identifier Source: org_study_id
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