Policy Responses Against the COVID-19 Pandemic in Latin America: Interrupted Series Analyses of Local Governments
Conditions: Covid19, Infectious Disease, Death, SARS-CoV Infection
Sponsor: University of Chile

Study protocol 
Policy responses against the COVID-19 pandemic in Latin America: interrupted series 
analyses of local governments 
 
Trial registration number: pending  
Protocol version: 1.1, March 23, 2021  
Revision history: this protocol has not been revised 
 
Roles and responsibilities 
Principal investigators:  
Dr. Sebastián Peña, Finnish Institute for Health and Welfare, Helsinki, Finland 
Dr. Cristóbal Cuadrado, Escuela de Salud Pública, Universidad de Chile, Santiago, Chile 
 
Collaborators: 
Helena Morais, Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais 
Karen Basualto, Faculty of Medicine, Tampere University, Tampere, Finland 
Dr. María José Monsálves, Facultad de Medicina y Ciencia, Universidad San Sebastián, 
Santiago, Chile  
Dr. José Ignacio Nasif-Muñoz, Faculté de Médicine et des Sciences de la Santé, Université de 
Sherbrooke, Quebec, Canada 
Dr. Maja Niksic, Faculty of Epidemiology and Population Health, London School of Hygiene 
and Tropical Medicine, United Kingdom 
Dr. Sandra Cortés, Escuela de Medicina, Departamento de Salud Pública, Centro Avanzado de 
Enfermedades Crónicas, Centro de Desarrollo Urbano Sustentable, Pontificia Universidad 
Católica de Chile, Santiago, Chile 
Ariadne Rivera-Aguirre, New York University School of Medicine, New York, New York, USA 
 
 
 
Signatures 
 
 
 
 
 
 
 
Dr. Sebastián Peña 
 
 
 
 
 
Dr. Cristóbal Cuadrado 

Background 
 
The COVID-19 pandemic is spreading rapidly worldwide. By January 22, 2021, there were more 
than 112 million confirmed SARS-COV-2 cases and nearly 2.5 million deaths attributable to 
COVID-19.1 Latin America, the region with the highest income inequality,2 remains as one of the 
worst-hit areas worldwide. Latin America accounts for 8.4% of the global population, but 20.3% 
of the total SARS-COV-2 cases and 30.2% of the COVID-19 deaths to date.1 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 Chinese government reported the first cases of pneumonia of unknown origin on December 
31, 2019,3 followed by a rapid increase in the Hubei province in China during the first weeks of 
January 2020, expanding in continental China and neighboring countries. The World Health 
Organization declared COVID-19 a public health emergency of international concern on January 
30, 20204 and a pandemic on March 11, 2020.5 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 
26,6 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.7 Effectiveness studies of social distancing policies in China,8,9 India,10 European 
countries11,12, the United States13 and worldwide 14 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.15-17 Likewise, few studies have 
explored variation at the local level in the effectiveness of COVID-19 policies.10,13,16 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, we 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 we find 
evidence of moderate to substantial heterogeneity at the local level, we aim to explore potential 
explanations for this heterogeneity. We 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 
The study is a natural experiment where we exploit 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.  
 
We will include all countries fulfilling the following eligibility criteria: (1) Spanish or Portuguese 
speaking countries in Latin America, and (2) availability of open data at the subnational level for 
either of the outcomes. Appendix I presents the eligible countries, the exclusion criteria and the 
sources of data for each excluded country. 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 
We will examine the impact of several interventions to tackle the COVID-19 pandemic. These 
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.18 Table 1 describes the policy 
interventions in each policy domain included in the study.  
 
Table 1. Policy interventions included in the study 
 
Domain 
Policy interventions 
Viral spread (for both outcomes) 
 
Total lockdown 
Partial lockdown (geographical, step-wise/graduated 
response) 
Curfew 
School closure 
Closure of shopping malls, gyms, churches, parks 
Remote work 
Restrictions to national/subnational mobility 
Prohibition of mass gatherings 
 
Health systems response (for COVID-19 deaths 
outcome) 
Interventions to increase testing capacity 
Interventions to increase the number of ICU/critical beds 
 
 
Mitigation strategies (for both outcomes) 
 
Direct social assistance (in-kind/cash) 
Cash transfer 
Withdrawal of pension funds 
 
Comparator 
In an interrupted time series analysis, 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). 
 

Outcomes 
We will assess three primary outcomes. The first outcome will be the 7-day moving average of 
daily confirmed cases of COVID-19/SARS-CoV-2. The second outcome is the time-varying 
reproductive number between the current and previous period.19 The third outcome is the 7-day 
moving average of the daily number of deaths by COVID-19 in Latin American subnational 
regions. For the statistical modelling, these averages will be rounded to the nearest integer. We 
will consider a 7-day lag as the primary lag. Details on sensitivity analyses can be found in the 
Statistical Analysis Plan. 
 
We define the outcomes following the official national sources definitions, as each country applies 
its criteria to define the occurrences and the deaths caused by COVID-19. It is important to note 
that those criteria are not always clearly stated, but all subnational units are under the same measure 
at each time.  
 
Data sources 
We will obtain COVID-19 cases and deaths data, as well as the covariates, on official government 
sources, such as the Ministry of Health and Ministry of Science and Technology (see 
Supplementary Appendix Table S2). The intervention information will come from legal 
documents, official statements, and quantitative accounts from trustable sources. 
 
Covariates 
The first model at the local level does not include covariates (see below). The 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 
The Statistical Analysis Plan provides details on the modelling assumptions. We will use an 
interrupted time series design, where each local government acts as its own control.20,21 The main 

strength of this design is its capacity to distinguish the effect of the intervention from secular 
change.22 We will use a Poisson regression to model the count data (for both outcomes) and 
accounting for overdispersion and secular trends.23 A full discussion on potential biases and 
violations of assumptions can be found in the Statistical Analysis Plan.  
 
In a second stage, we 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 we observe high levels of heterogeneity (defined as higher 
than 50%), we will also use standard meta-regression techniques to assess whether local level 
determinants (see Covariates) can explain the observed heterogeneity.  
 
We 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.  
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
References 
 

1. 
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real 
time. The Lancet Infectious Diseases 2020. 
2. 
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. 
3. 
World Health Organization. Report of the WHO-China Joint Mission on Coronavirus 
Disease 2019 (COVID-19)2020. https://www.who.int/docs/default-source/coronaviruse/who-
china-joint-mission-on-covid-19-final-report.pdf (accessed March 26, 2020). 
4. 
World Health Organization. Statement on the second meeting of the International Health 
Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-
nCoV). 
2020. 
https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-
meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-
outbreak-of-novel-coronavirus-(2019-ncov) (accessed March 30, 2020. 
5. 
World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report 1 - 21 
January 
20202020. 
https://www.who.int/docs/default-source/coronaviruse/situation-
reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 (accessed March 26, 2020). 
6. 
The Lancet. COVID-19 in Brazil: So what? The Lancet 2020; 395(10235): 1461. 
7. 
Martinez-Gutierrez S, Cuadrado C, Peña S. Chile’s response to the coronavirus pandemic. 
2020. 
https://www.cambridge.org/core/blog/2020/04/11/chiles-response-to-the-coronavirus-
pandemic/ (accessed April 12, 2020. 
8. 
Ali ST, Wang L, Lau EHY, et al. Serial interval of SARS-CoV-2 was shortened over time 
by nonpharmaceutical interventions. Science 2020; 369(6507): 1106-9. 
9. 
Pan A, Liu L, Wang C, et al. Association of Public Health Interventions With the 
Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA 2020; 323(19): 1915-23. 
10. 
Basu D, Salvatore M, Ray D, et al. A Comprehensive Public Health Evaluation of 
Lockdown as a Non-pharmaceutical Intervention on COVID-19 Spread in India: National Trends 
Masking State Level Variations. medRxiv : the preprint server for health sciences 2020: 
2020.05.25.20113043. 
11. 
Flaxman S, Mishra S, Gandy A, et al. Estimating the effects of non-pharmaceutical 
interventions on COVID-19 in Europe. Nature 2020; 584(7820): 257-61. 
12. 
Hyafil A, Moriña D. Analysis of the impact of lockdown on the reproduction number of 
the SARS-Cov-2 in Spain. Gaceta Sanitaria 2020. 
13. 
Siedner MJ, Harling G, Reynolds Z, et al. Social distancing to slow the US COVID-19 
epidemic: Longitudinal pretest–posttest comparison group study. PLOS Medicine 2020; 17(8): 
e1003244. 
14. 
Islam N, Sharp SJ, Chowell G, et al. Physical distancing interventions and incidence of 
coronavirus disease 2019: natural experiment in 149 countries. BMJ 2020; 370: m2743. 
15. 
Candido DS, Claro IM, de Jesus JG, et al. Evolution and epidemic spread of SARS-CoV-
2 in Brazil. Science 2020; 369(6508): 1255-60. 
16. 
Bennett M. All things equal? Heterogeneity in policy effectiveness against COVID-19 
spread in chile. World Development 2021; 137: 105208. 
17. 
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. Cadernos de Saúde Pública 
2020; 36. 
18. 
Peña S, Cuadrado C, Rivera-Aguirre A, et al. PoliMap: A taxonomy proposal for mapping 
and understanding the global policy response to COVID-19. OSF Preprint2020. 
https://osf.io/h6mvs (accessed March 22, 2021). 

19. 
Li Y, Campbell H, Kulkarni D, et al. The temporal association of introducing and lifting 
non-pharmaceutical interventions with the time-varying reproduction number (<em>R</em>) of 
SARS-CoV-2: a modelling study across 131 countries. The Lancet Infectious Diseases 2021; 
21(2): 193-202. 
20. 
Gebski V, Ellingson K, Edwards J, Jernigan J, Kleinbaum D. Modelling interrupted time 
series to evaluate prevention and control of infection in healthcare. Epidemiology and Infection 
2012; 140(12): 2131-41. 
21. 
Kontopantelis E, Doran T, Springate DA, Buchan I, Reeves D. Regression based quasi-
experimental approach when randomisation is not an option: interrupted time series analysis. BMJ 
: British Medical Journal 2015; 350: h2750. 
22. 
Taljaard M, McKenzie JE, Ramsay CR, Grimshaw JM. The use of segmented regression 
in analysing interrupted time series studies: an example in pre-hospital ambulance care. 
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55. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Supplementary Appendix  
 

Table S1. Countries included in the study and data sources. 
 
Country 
Official source 
Data source 
Argentina 
Dirección Nacional de Epidemiología y 
Análisis de Situación de Salud 
 
http://datos.salud.gob.ar/dataset/covid-19-casos-
registrados-en-la-republica-
argentina/archivo/fd657d02-a33a-498b-a91b-
2ef1a68b8d16 
Brazil 
Open DataSUS 
 
https://opendatasus.saude.gov.br/dataset/casos-
nacionais 
Chile 
Departamento de Estadísticas e 
Información de Salud 
 
https://deis.minsal.cl/#datosabiertos 
Colombia 
Instituto Nacional de Salud 
 
https://www.datos.gov.co/Salud-y-Protecci-n-
Social/Casos-positivos-de-COVID-19-en-
Colombia/gt2j-8ykr 
Guatemala 
Ministerio de Salud Pública y Asistencia 
Social 
https://tablerocovid.mspas.gob.gt/ 
Costa Rica 
Observatorio Geográfico en Salud - 
Ministerio de Salud 
http://geovision.uned.ac.cr/oges/#descargas 
Mexico 
Subdirector de Notificación y Registros 
Epidemiológicos 
https://datos.gob.mx/busca/dataset/informacion-
referente-a-casos-covid-19-en-mexico 
Paraguay 
Ministerio de Salud Pública y Bienestar 
Social 
https://public.tableau.com/profile/mspbs#!/vizho
me/COVID19PY-Registros/Descargardatos 
Peru 
Ministerio de Salud 
https://www.datosabiertos.gob.pe/group/datos-
abiertos-de-covid-19 
 
 
 
 
 
 

Table S2. Countries excluded from the study. 
 
Eligible Country 
Reason for exclusion 
Source 
Bolivia 
There is weekly data available at the municipal 
level, but we did not find daily data. 
https://datos.gob.bo/dataset/casos-
covid-19-acumulado-por-municipios 
Cuba 
Information is available in daily reports, but we 
did not find open data to download. 
https://salud.msp.gob.cu/ 
Ecuador 
Daily subnational data is available in charts, but it 
is not open to download. 
https://www.coronavirusecuador.co
m/data 
El Salvador 
Daily subnational data is available in charts, but it 
is not open to download. 
https://covid19.gob.sv/ 
French Guiana 
The official language is not Spanish or 
Portuguese. 
- 
Guiana 
The official language is not Spanish or 
Portuguese. 
- 
Haiti 
The official language is not Spanish or 
Portuguese 
- 
Honduras 
Information is available in reports, but we could 
not find data to download. 
http://www.salud.gob.hn/site/inde
x.php/covid19 
Nicaragua 
We did not find an official source of information.  
Panama 
There is a map and some charts with information, 
but data is not open to download. 
http://minsa.gob.pa/covid-19 
Republica Dominicana 
Information is available in reports, but we did not 
find open data to download. 
https://www.msp.gob.do/web/?pa
ge_id=6948 
Suriname 
The official language is not Spanish or 
Portuguese. 
- 
Uruguay 
There is information on cumulative cases and 
deaths available, but not daily subnational data on 
new events. 
https://catalogodatos.gub.uy/dataset/
sinae01-covid19 
Venezuela 
There is daily data available, but it is not 
disaggregated at the subnational level. 
https://covid19.patria.org.ve/api-
covid-19-venezuela/ 
 
 
 
 

Table S3. Covariates used in the study and availability at the local level. 
 
 
Local level availability 
 
Population size 
Population 
density 
Age structure 
Household 
density 
Socioeconomic 
status 
Argentina 
Yes 
Yes 
Yes 
Yes 
Yes 
Brazil 
Yes 
Yes 
Yes 
Yes 
Yes 
Chile 
Yes 
Yes 
Yes 
Yes 
Yes 
Colombia 
Yes 
Yes 
Yes 
Yes 
Yes 
Costa Rica 
Yes 
Yes 
Yes 
Yes 
Yes 
Guatemala 
Yes 
Yes 
Yes 
Yes 
Yes 
Mexico 
Yes 
Yes 
Yes 
Yes 
Yes 
Paraguay 
Yes 
Yes 
Yes 
Yes 
Yes 
Peru 
Yes 
Yes 
Yes 
Yes 
Yes