- ICH GCP
- Registr klinických studií v USA
- Klinická studie NCT04816318
Policy Responses Against the COVID-19 Pandemic in Latin America
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.
Přehled studie
Postavení
Podmínky
Intervence / Léčba
Detailní popis
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.
Typ studie
Zápis (Očekávaný)
Kontakty a umístění
Studijní kontakt
- Jméno: Sebastián Peña, MD, PhD
- Telefonní číslo: +358452451360
- E-mail: sebastian.penafajuri@thl.fi
Studijní místa
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Región Metropolitana
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Santiago, Región Metropolitana, Chile, 8380453
- Escuela de Salud Pública
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Dílčí vyšetřovatel:
- Helena Morais, MEcon
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Dílčí vyšetřovatel:
- Maria José Monsalves, PhD
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Kritéria účasti
Kritéria způsobilosti
Věk způsobilý ke studiu
- Dítě
- Dospělý
- Starší dospělý
Přijímá zdravé dobrovolníky
Pohlaví způsobilá ke studiu
Metoda odběru vzorků
Studijní populace
Popis
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
Studijní plán
Jak je studie koncipována?
Detaily designu
Kohorty a intervence
Skupina / kohorta |
Intervence / Léčba |
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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
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Ostatní jména:
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Control
The comparator is the pre-intervention period
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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)
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Co je měření studie?
Primární výstupní opatření
Měření výsledku |
Časové okno |
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7-day moving average of daily confirmed cases of COVID-19/SARS-CoV-2
Časové okno: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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Time-varying reproductive number of confirmed cases of COVID-19/SARS-CoV-2
Časové okno: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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7-day moving average of daily confirmed deaths of COVID-19/SARS-CoV-2
Časové okno: Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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Intervention period of up to 30 days (intervention periods lower than 7 days will be considered as a combined set of interventions)
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Spolupracovníci a vyšetřovatelé
Sponzor
Vyšetřovatelé
- Vrchní vyšetřovatel: Sebastián Peña, MD, PhD, Escuela de Salud Pública
- Vrchní vyšetřovatel: Cristóbal Cuadrado, MD, PhD, Escuela de Salud Pública
Publikace a užitečné odkazy
Obecné publikace
- 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. Erratum In: Lancet Infect Dis. 2020 Sep;20(9):e215.
- Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017 Feb 1;46(1):348-355. doi: 10.1093/ije/dyw098. Erratum In: Int J Epidemiol. 2020 Aug 1;49(4):1414.
- 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
- 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.
- Islam 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.
- 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.
- Gebski V, Ellingson K, Edwards J, Jernigan J, Kleinbaum D. Modelling interrupted time series to evaluate prevention and control of infection in healthcare. Epidemiol Infect. 2012 Dec;140(12):2131-41. doi: 10.1017/S0950268812000179. Epub 2012 Feb 16.
- 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. 2015 Jun 9;350:h2750. doi: 10.1136/bmj.h2750.
- 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. Implement Sci. 2014 Jun 19;9:77. doi: 10.1186/1748-5908-9-77.
Užitečné odkazy
Termíny studijních záznamů
Hlavní termíny studia
Začátek studia (Očekávaný)
Primární dokončení (Očekávaný)
Dokončení studie (Očekávaný)
Termíny zápisu do studia
První předloženo
První předloženo, které splnilo kritéria kontroly kvality
První zveřejněno (Aktuální)
Aktualizace studijních záznamů
Poslední zveřejněná aktualizace (Aktuální)
Odeslaná poslední aktualizace, která splnila kritéria kontroly kvality
Naposledy ověřeno
Více informací
Termíny související s touto studií
Klíčová slova
Další relevantní podmínky MeSH
Další identifikační čísla studie
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Plán pro data jednotlivých účastníků (IPD)
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Popis plánu IPD
Časový rámec sdílení IPD
Kritéria přístupu pro sdílení IPD
Typ podpůrných informací pro sdílení IPD
- PROTOKOL STUDY
- MÍZA
- ANALYTIC_CODE
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