The effect of early-stage public health policies in the transmission of COVID-19 for South American countries.

OBJECTIVES: The analysis of transmission dynamics is crucial to determine whether mitigation or suppression measures reduce the spread of coronavirus disease 2019 (COVID-19). This study sought to estimate the basic (R0 ) and time-varying (Rt ) reproduction number of COVID-19 and contrast the public health measures for ten South American countries. METHODS: Data was obtained from the European Centre for Disease Prevention and Control. Country-specific R0 values during the first two weeks of the outbreak and Rt values after 90 days were estimated. RESULTS: Countries used a combination of isolation, physical distancing, quarantine, and community-wide containment measures to staunch the spread of COVID-19 at different points in time. R0 ranged from 1.52 (95% confidence interval: 1.13-1.99) in Venezuela to 3.83 (3.04-4.75) in Chile, whereas Rt after 90 days ranged from 0.71 (95% credible interval: 0.39-1.05) in Uruguay to 1.20 (1.19-1.20) in Brazil. Different R0 and Rt values may be related to the testing capacity of each country. CONCLUSION: R0 in the early phase of the outbreak varied across the South American countries. The public health measures adopted in the initial period of the pandemic appear to have reduced Rt over time in each country, albeit to different levels.

OBJETIVOS: Estimar el número de reproducción básico (R0 ) y el número de reproducción efectivo (Rt ) de la COVID-19 y contrastarlos con las medidas de salud pública implementadas en diez países de América del Sur. MÉTODOS: Los datos se obtuvieron del Centro Europeo para la Prevención y el Control de las Enfermedades. Se estimó el R0 de cada país durante las dos primeras semanas del brote y el Rt después de 90 días. RESULTADOS: Los países utilizaron una combinación de aislamiento, distanciamiento físico, cuarentena y medidas de contención en toda la comunidad para detener la propagación de la COVID-19 en diferentes momentos. El R0 osciló entre 1,52 (IC95%: 1,13-1,99) en Venezuela y 3,83 (IC95%: 3,04-4,75) en Chile, mientras que el Rt después de 90 días varió entre 0,71 (intervalo de credibilidad 95%: 0,39-1,05) en Uruguay y 1,20 (intervalo de credibilidad 95%: 1,19-1,20) en Brasil. Los diferentes valores de R0 y Rt pueden estar relacionados con la capacidad de llevar a cabo pruebas de detección viral de cada país. CONCLUSIÓN: Los valores del R0 en la fase inicial del brote variaron entre los países sudamericanos. Las medidas de salud pública adoptadas en el período inicial de la pandemia parecen haber reducido el Rt con el tiempo en cada país, aunque en niveles diferentes.

The effect of early-stage public health policies in the transmission of COVID-19 for South American countries

[ABSTRACT]. Objectives. The analysis of transmission dynamics is crucial to determine whether mitigation or suppression measures reduce the spread of coronavirus disease 2019 (COVID-19). This study sought to estimate the basic (R0) and time-varying (Rt) reproduction number of COVID-19 and contrast the public health measures for ten South American countries. Methods. Data was obtained from the European Centre for Disease Prevention and Control. Country-specific R0 values during the first two weeks of the outbreak and Rt values after 90 days were estimated. Results. Countries used a combination of isolation, physical distancing, quarantine, and community-wide containment measures to staunch the spread of COVID-19 at different points in time. R0 ranged from 1.52 (95% confidence interval: 1.13-1.99) in Venezuela to 3.83 (3.04-4.75) in Chile, whereas Rt after 90 days ranged from 0.71 (95% credible interval: 0.39-1.05) in Uruguay to 1.20 (1.19-1.20) in Brazil. Different R0 and Rt values may be related to the testing capacity of each country. Conclusion. R0 in the early phase of the outbreak varied across the South American countries. The public health measures adopted in the initial period of the pandemic appear to have reduced Rt over time in each country, albeit to different levels.

[RESUMEN]. Objetivos. Estimar el número de reproducción básico (R0) y el número de reproducción efectivo (Rt) de la COVID-19 y contrastarlos con las medidas de salud pública implementadas en diez países de América del Sur. Métodos. Los datos se obtuvieron del Centro Europeo para la Prevención y el Control de las Enfermedades. Se estimó el R0 de cada país durante las dos primeras semanas del brote y el Rt después de 90 días. Resultados. Los países utilizaron una combinación de aislamiento, distanciamiento físico, cuarentena y medidas de contención en toda la comunidad para detener la propagación de la COVID-19 en diferentes momentos. El R0 osciló entre 1,52 (IC95%: 1,13-1,99) en Venezuela y 3,83 (IC95%: 3,04-4,75) en Chile, mientras que el Rt después de 90 días varió entre 0,71 (intervalo de credibilidad 95%: 0,39-1,05) en Uruguay y 1,20 (intervalo de credibilidad 95%: 1,19-1,20) en Brasil. Los diferentes valores de R0 y Rt pueden estar relacionados con la capacidad de llevar a cabo pruebas de detección viral de cada país. Conclusión. Los valores del R0 en la fase inicial del brote variaron entre los países sudamericanos. Las medidas de salud pública adoptadas en el período inicial de la pandemia parecen haber reducido el Rt con el tiempo en cada país, aunque en niveles diferentes.

Geographic differences in overweight and obesity prevalence in Peruvian children, 2010-2015.

BACKGROUND: Childhood obesity is emerging as a major public health problem worldwide. To date, most studies of obesity and overweight in Peru are focused on adults, with few of them involving children, a population at a critical stage of development. The trend in overweight and obesity prevalence in Peruvian children under the age of five has not yet been determined. Thus, the objective of the present study is to evaluate the prevalence rates of overweight and obesity between 2010 and 2015 in children under the age of five, stratified by geographical areas in Peru. METHODS: Data were obtained from the Nutritional Status Information System of Peru. The total number of children evaluated was 14,155,914. For the Geographic Information Systems (GIS), the program Geo Da 1.8® was used to ascertain the spatial distribution of prevalence rates and was mapped for children under five. To assess the degree of spatial dependence, exploratory spatial data analysis was performed using the Moran's I statistic and was assessed with the Local Indicators of Spatial Association (LISA) analysis to identify geographic concentrations of high and low of obesity and overweight levels. RESULTS: Between 2010 and 2015, the national prevalence of childhood overweight and obesity ranged from 6.2%- 6.8% and 1.5%-2.7%, respectively. The highest prevalence of childhood overweight and obesity was found in 2014 and 2013, respectively. During these years, we observed that obesity decreased, but overweight remained stable. The highest prevalence of overweight and obesity was found in the departments located on the coast. Significant positive spatial autocorrelation was found for both overweight and obesity. The departments with the highest prevalence of overweight and obesity were concentrated in Lima, Callao, Ica, Moquegua and Tacna. The lowest were found in Loreto, Cusco and San Martin. CONCLUSION: The decrease in obesity and the stabilisation of overweight are positive results for the Peruvian childhood. However, in comparison with other Latin American countries, Peru still lags in obesity prevention.

The double burden of malnutrition: a threat for Peruvian childhood / La doble carga de la desnutrición: una amenaza para la niñez peruana

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[New strategies for the eradication of Aedes aegypti: what challenges do we face in Latin America?]

Not available.

[Peru, climate change and non-communicable diseases: ¿where are we and where are we headed?]. / Perú, cambio climático y enfermedades no trasmisibles: ¿Dónde estamos y a dónde vamos?

The world is facing a crisis of noncommunicable diseases alongside a state of climate volatility. Of the total health burden attributable to climate change, 99% will be assumed by developing countries. In light of climate change, Peru's best opportunity is to create mitigation and adaptation policies focused on developing health co-benefits, which will improve the state of noncommunicable diseases and the climate system. This article presents Peru's compromises in terms of mitigation of greenhouse gas emissions; identifies public health opportunities for Peru under the Paris Agreement signed in the 21st Conference of the Parties (COP21); and makes recommendations for evidence and policy generation for the National Academy of Medicine and the Peruvian government.

Perú, cambio climático y enfermedades no trasmisibles: ¿Dónde estamos y a dónde vamos? / Peru, climate change and non-communicable diseases: ¿Where are we and where are we headed?

El mundo atraviesa una crisis de enfermedades no transmisibles (ENT) junto con un panorama de volatilidad climática. El 99% de la carga en salud del cambio climático será asumida por los países en vías de desarrollo. La mejor oportunidad que tiene el Perú frente a la inminencia del cambio climático recae en desarrollar políticas enfocadas en crear cobeneficios mediante las estrategias de mitigación y adaptación al cambio climático que, a la vez, representen mejoras sobre el panorama de las ENT y la salud en general. En este artículo presentamos los compromisos asumidos por el Perú en estrategias de mitigación de emisiones de gases de efecto invernadero (GEI); identificamos oportunidades en salud pública para el Perú en el Acuerdo de París de la 21.ª Conferencia de las Partes (COP21); y culminamos con las recomendaciones sobre la generación de evidencia y políticas para para la Academia y el Gobierno...

The world is facing a crisis of noncommunicable diseases alongside a state of climate volatility. Of the total health burden attributable to climate change, 99% will be assumed by developing countries. In light of climate change, PeruÆs best opportunity is to create mitigation and adaptation policies focused on developing health co-benefits, which will improve the state of noncommunicable diseases and the climate system. This article presents PeruÆs compromises in terms of mitigation of greenhouse gas emissions; identifies public health opportunities for Peru under the Paris Agreement signed in the 21st Conference of the Parties (COP21); and makes recommendations for evidence and policy generation for the National Academy of Medicine and the Peruvian government...

Patterns and Determinants of Physical Inactivity in Rural and Urban Areas in Peru: A Population-Based Study.

BACKGROUND: Physical inactivity and sedentary behaviors have been linked with impaired health outcomes. Establishing the physical inactivity profiles of a given population is needed to establish program targets and to contribute to international monitoring efforts. We report the prevalence of, and explore sociodemographical and built environment factors associated with physical inactivity in 4 resource-limited settings in Peru: rural Puno, urban Puno, Pampas de San Juan de Miraflores (urban), and Tumbes (semiurban). METHODS: Cross-sectional analysis of the CRONICAS Cohort Study's baseline assessment. Outcomes of interest were physical inactivity of leisure time (<600 MET-min/week) and transport-related physical activity (not reporting walking or cycling trips) domains of the IPAQ, as well as watching TV, as a proxy of sedentarism (≥2 hours per day). Exposures included demographic factors and perceptions about neighborhood's safety. Associations were explored using Poisson regression models with robust standard errors. Prevalence ratios (PR) and 95% confidence intervals (95% CI) are presented. RESULTS: Data from 3593 individuals were included: 48.5% males, mean age 55.1 (SD: 12.7) years. Physical inactivity was present at rates of 93.7% (95% CI 93.0%-94.5%) and 9.3% (95% CI 8.3%-10.2%) within the leisure time and transport domains, respectively. In addition, 41.7% (95% CI 40.1%-43.3%) of participants reported watching TV for more than 2 hours per day. Rates varied according to study settings (P < .001). In multivariable analysis, being from rural settings was associated with 3% higher prevalence of leisure time physical inactivity relative to highly urban Lima. The pattern was different for transport-related physical inactivity: both Puno sites had around 75% to 50% lower prevalence of physical inactivity. Too much traffic was associated with higher levels of transport-related physical inactivity (PR = 1.24; 95% CI 1.01-1.54). CONCLUSION: Our study showed high levels of inactivity and marked contrasting patterns by rural/urban sites. These findings highlight the need to generate synergies to expand nationwide physical activity surveillance systems.