Policy Responses Against the COVID-19 Pandemic in Latin America

April 27, 2021 updated by: Sebastián Peña, University of Chile

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Anticipated)

10000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Chile
    • Región Metropolitana
      • Santiago, Región Metropolitana, Chile, 8380453
        • Escuela de Salud Pública
        • Sub-Investigator:
          • Helena Morais, MEcon
        • Sub-Investigator:
          • Maria José Monsalves, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Probability Sample

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

Description

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
Other: 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
Other Names:
  • Non-pharmaceutical interventions against COVID-19
Control
The comparator is the pre-intervention period
Other: 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)

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
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)
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)
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)
Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Chile

Investigators

  • Principal Investigator: Sebastián Peña, MD, PhD, Escuela de Salud Pública
  • Principal Investigator: Cristóbal Cuadrado, MD, PhD, Escuela de Salud Pública

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

April 28, 2021

Primary Completion (Anticipated)

May 31, 2021

Study Completion (Anticipated)

May 31, 2021

Study Registration Dates

First Submitted

March 23, 2021

First Submitted That Met QC Criteria

March 24, 2021

First Posted (Actual)

March 25, 2021

Study Record Updates

Last Update Posted (Actual)

April 28, 2021

Last Update Submitted That Met QC Criteria

April 27, 2021

Last Verified

April 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • ANID-COVID 0960

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

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.

IPD Sharing Time Frame

The Study Protocol and SAP will be available in the project OSF repository upon publication of the registration in ClinicalTrials.gov. The Statistical code will be published as a Supplementary Appendix with the publish paper or preprint.

IPD Sharing Access Criteria

Any interested party can access the data and documents

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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