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Background: Argentina's smoking rates remain high. We aim to estimate Argentina age-specific histories of smoking initiation, cessation, prevalence, and intensity by birth-cohort to inform policy interventions. Methods: Modeling study. Data from three Argentinian nationally representative surveys conducted from 2004 to 2018 (n = 268,193) were used to generate smoking histories. The Cancer Intervention and Surveillance Modeling (CISNET) Network Lung Working Group age, period, and cohort modeling approach was used to calculate smoking initiation and cessation probabilities, ever and current smoking prevalence, and intensity (cigarettes per day, CPD) by age, sex, and birth cohort from 1950 to 2018. Findings: Ever smoking prevalence increases with age up to 25 and decreases with birth cohorts after 1990. Smoking initiation peaks between 15 and 18 years of age. Among females, initiation probabilities increased until the 1955 cohort, reaching a second peak in 1980-85 cohorts and declining thereafter. Males have higher initiation probabilities than females. Among males, initiation has decreased since the 1950 birth cohort, with a slight increase around the 1985 cohort. Current smoking prevalence has been decreasing since the 1960 birth cohort, except for a peak in 1980-85 cohorts. Cessation increases with age. Mean CPD increases with age and peaks around age 40, appearing flat in females since the 1985 cohort. Interpretation: Recent birth cohorts seem to be experiencing lower rates of initiation, stable rates of quitting and lower current and ever smoking prevalence. The stabilization of cessation probabilities and mean CPD indicate the need to strengthen existing tobacco control measures and advance new ones. Funding: NIH/NCI U01CA253858 grant.

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BACKGROUND: The well-established inverse relationship between socioeconomic status (SES) and risk of developing coronary heart disease (CHD) cannot be explained solely by differences in traditional risk factors. OBJECTIVE: To model the role SES plays in the burden of premature CHD in Argentina. MATERIALS AND METHODS: We used the Cardiovascular Disease Policy Model-Argentina to project incident CHD events and mortality in low and high-SES Argentinean adults 35 to 64 years of age from 2015 to 2024. Using data from the 2018 National Risk Factor Survey, we defined low SES as not finishing high-school and/or reporting a household income in quintiles 1 or 2. We designed simulations to apportion CHD outcomes in low SES adults to: (1) differences in the prevalence of traditional risk factors between low and high SES adults; (2) nontraditional risk associated with low SES status; (3) preventable events if risk factors were improved to ideal levels; and (4) underlying age- and sex-based risk. RESULTS: 56% of Argentina´s 35- to 64-year-old population has low SES. Both high and low SES groups have poor control of traditional risk factors. Compared with high SES population, low SES population had nearly 2-fold higher rates of incident CHD and CHD deaths per 10 000 person-years (incident CHD: men 80.8 [95%CI 76.6-84.9] vs 42.9 [95%CI 37.4-48.1], women 39.0 [95%CI 36.-41.2] vs 18.6 [95%CI 16.3-20.9]; CHD deaths: men 10.0 [95%CI 9.5-10.5] vs 6.0 [95%CI 5.6-6.4], women 3.2 [95%CI 3.0-3.4] vs 1.8 [95%CI 1.7-1.9]). Nontraditional low SES risk accounts for 73.5% and 70.4% of the event rate gap between SES levels for incident CHD and CHD mortality rates, respectively. DISCUSSION: CHD prevention policies in Argentina should address contextual aspects linked to SES, such as access to education or healthcare, and should also aim to implement known clinical strategies to achieve better control of CHD risk factors in all socioeconomic levels.

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Abstract Background : Obesity rates in Latin America are increasing overall and among people with low socio economic status (SES). Obesity and SES disparities can vary by region-a valuable indicator of local drivers. The objective of this study was to examine regional and SES differences in obesity in Argentina. Methods : We used data from Argentina's 4th Na tional Risk Factors Survey (n = 29226) 2018 and defined obesity as BMI ≥ 30. Low SES was defined as not fin ished high school or having a household income in the lowest two quintiles. Descriptive analysis stratified by sex compared obesity rates by SES, province, and re gion. Age-adjusted logistic regression models explored the association between obesity, socioeconomic status, and region. Results : Obesity rates varied more by SES among women (39% for low SES vs. 26% for middle/high SES; p < 0.001) than among men (33% low SES vs. 29% middle/ high SES; p = 0.027). The Patagonian region had the high est obesity prevalence for both men (36%) and women (37%). A gender-stratified age-adjusted analysis with region and SES showed that low SES (OR 1.72, 95% CI 1.45, 2.03) and the Patagonian region (OR 1.29, 95% CI 1.02, 1.62) were the only significant predictors for women. Conclusions : SES associated disparities in obesity in Argentina were pronounced for women but not men. Disparities were particularly high in Patagonia. Further research is needed to understand the drivers behind these SES, regional, and gender disparities.

Resumen Introducción : Las tasas de obesidad en América La tina están aumentando, tanto en la población general como entre las personas con bajo nivel socioeconómi co (NSE). Las disparidades en obesidad y NSE pueden variar ampliamente según la región, un indicador po tencialmente valioso de fenómenos causales locales. El objetivo de este estudio fue examinar las diferencias en la prevalencia de obesidad a nivel regional y según el NSE en Argentina. Métodos : Utilizamos datos de la 4ª Encuesta Nacio nal de Factores de Riesgo realizada en Argentina en 2018 (n = 29226). Definimos obesidad como índice de masa corporal ≥ 30, y bajo NSE como no haber termi nado la escuela secundaria o tener un ingreso familiar en los dos quintiles más bajos. El análisis descriptivo estratificado por sexo comparó la prevalencia de obe sidad por NSE, provincia y región. Además, utilizamos modelos de regresión logística ajustados por edad para explorar la asociación entre obesidad, nivel socioeconó mico y región, tanto globalmente como estratificando por sexo.367 Resultados : Las tasas de obesidad variaron más por NSE entre las mujeres (39% NSE bajo vs. 26% NSE medio/ alto; p < 0.001) que entre los hombres (33% NSE bajo vs. 29% NSE medio/alto; p = 0.027). La región patagónica tuvo la mayor prevalencia de obesidad tanto para hom bres (36%) como para mujeres (37%). Un análisis estra tificado por género, con región y NSE como covariables, mostró que el bajo NSE (OR 1.72, IC 95% 1.45, 2.03) y la región patagónica (OR 1.29, IC 95% 1.02, 1.62) fueron los únicos predictores significativos para las mujeres; nin guno se asoció significativamente con un mayor riesgo de obesidad para los hombres. Conclusiones : Las disparidades asociadas al NSE en la obesidad en Argentina fueron pronunciadas entre mujeres, pero no entre hombres. Las disparidades fueron particularmente altas en la Patagonia. Se necesita más estudios para comprender los factores detrás de estas disparidades de NSE, regionales y de género.

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BACKGROUND: Obesity rates in Latin America are increasing overall and among people with low socioeconomic status (SES). Obesity and SES disparities can vary by region-a valuable indicator of local drivers. The objective of this study was to examine regional and SES differences in obesity in Argentina. METHODS: We used data from Argentina's 4th National Risk Factors Survey (n = 29226) 2018 and defined obesity as BMI = 30. Low SES was defined as not finished high school or having a household income in the lowest two quintiles. Descriptive analysis stratified by sex compared obesity rates by SES, province, and region. Age-adjusted logistic regression models explored the association between obesity, socioeconomic status, and region. RESULTS: Obesity rates varied more by SES among women (39% for low SES vs. 26% for middle/high SES; p < 0.001) than among men (33% low SES vs. 29% middle/ high SES; p = 0.027). The Patagonian region had the highest obesity prevalence for both men (36%) and women (37%). A gender-stratified age-adjusted analysis with region and SES showed that low SES (OR 1.72, 95% CI 1.45, 2.03) and the Patagonian region (OR 1.29, 95% CI 1.02, 1.62) were the only significant predictors for women. CONCLUSIONS: SES associated disparities in obesity in Argentina were pronounced for women but not men. Disparities were particularly high in Patagonia. Further research is needed to understand the drivers behind these SES, regional, and gender disparities.

Introducción: Las tasas de obesidad en América Latina están aumentando, tanto en la población general como entre las personas con bajo nivel socioeconómico (NSE). Las disparidades en obesidad y NSE pueden variar ampliamente según la región, un indicador potencialmente valioso de fenómenos causales locales. El objetivo de este estudio fue examinar las diferencias en la prevalencia de obesidad a nivel regional y según el NSE en Argentina. Métodos: Utilizamos datos de la 4° Encuesta Nacional de Factores de Riesgo realizada en Argentina en 2018 (n = 29226). Definimos obesidad como índice de masa corporal = 30, y bajo NSE como no haber terminado la escuela secundaria o tener un ingreso familiar en los dos quintiles más bajos. El análisis descriptivo estratificado por sexo comparó la prevalencia de obesidad por NSE, provincia y región. Además, utilizamos modelos de regresión logística ajustados por edad para explorar la asociación entre obesidad, nivel socioeconómico y región, tanto globalmente como estratificando por sexo.Resultados: Las tasas de obesidad variaron más por NSE entre las mujeres (39% NSE bajo vs. 26% NSE medio/alto; p < 0.001) que entre los hombres (33% NSE bajo vs. 29% NSE medio/alto; p = 0.027). La región patagónica tuvo la mayor prevalencia de obesidad tanto para hombres (36%) como para mujeres (37%). Un análisis estratificado por género, con región y NSE como covariables, mostró que el bajo NSE (OR 1.72, IC 95% 1.45, 2.03) y la región patagónica (OR 1.29, IC 95% 1.02, 1.62) fueron los únicos predictores significativos para las mujeres; ninguno se asoció significativamente con un mayor riesgo de obesidad para los hombres. Conclusiones: Las disparidades asociadas al NSE en la obesidad en Argentina fueron pronunciadas entre mujeres, pero no entre hombres. Las disparidades fueron particularmente altas en la Patagonia. Se necesita más estudios para comprender los factores detrás de estas disparidades de NSE, regionales y de género.

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INTRODUCTION: Tobacco packaging design is conceived to be attractive. Plain packaging of tobacco products reduces this attractiveness by standardising their shape, size, font and colours. METHODS: To evaluate the effect of applying plain packaging to tobacco products on cardiovascular events and mortality in Argentina, we used the Cardiovascular Disease Policy Model-Argentina, a local adaptation of a well-established computer simulation model that projects cardiovascular and mortality events for the population 35-94 years old using local demographic and consumption data, during the period 2015-2024. After a literature review, we estimated that the implementation of plain packaging of tobacco products would result in an absolute decrease in tobacco prevalence of 0.55% (base-case scenario) and performed a sensitivity analysis assuming a higher and lower decrease of 1.01% and 0.095%, respectively. RESULTS: Over the 2015-2024 period, the decrease in smoking prevalence associated with plain packaging (0.55%) is projected to avert 1880 myocardial infarctions (MI), 820 strokes and 4320 total deaths in Argentina. The higher estimate of smoking prevalence reduction (1.01%) would translate into 3450 fewer MIs, 1490 fewer strokes and 7920 fewer deaths, while the lower estimate of smoking prevalence reduction (0.095%) would result in 330 fewer MIs, 140 fewer strokes and 750 fewer deaths. CONCLUSIONS: The implementation of plain packaging of tobacco products could reduce cardiovascular events in Argentina, even in the absence of other tobacco control measures. Actual health benefits are likely higher than those presented here, since plain packaging may be most impactful by preventing young people from initiating smoking.

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El spottingo sangrado irregular no menstrual es uno de los principales efectos secundarios de los implantes anticonceptivos, situación que se recomienda discutir con la usuaria previo a la colocación, para evitar falsas expectativas o temores que lleven a la extracción temprana del dispositivo. A propósito de una paciente sin antecedentes relevantes que consultó al centro de salud por spotting desde la colocación del implante cuatro meses antes, decidimos revisar la evidencia sobre la efectividad de los distintos esquemas farmacológicos disponibles para el manejo de este evento adverso. Luego de realizar una búsqueda bibliográfica concluimos que, si bien existe sustento para indicar algunos de los esquemas farmacológicos, este es aún débil y son necesarios estudios clínicos adicionales que brinden evidencia sólida sobre qué esquema en particular utilizar, evaluando sus riesgos y beneficios. (AU)

Spotting or irregular non-menstrual bleeding is one of the main side effects of contraceptive implants, a situation that health professionals must discuss with the user prior to its placement in order to avoid false expectations or fears that lead to early removal of the implant. Regarding a patient with no relevant history who consulted the health center due to spotting four months after implant placement, we decided to review the evidence onthe effectiveness on different pharmacological schemes available for the management of this adverse event. After performing a literature search, we concluded that, although there is support for indicating some of the pharmacological schemes, this is still weak, and further clinical studies are needed to provide solid evidence on which particular scheme touse, assessing its risks and benefits. (AU)

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Background: Angiotensin receptor blockers (ARBs), such as telmisartan, have been postulated to treat Covid-19-induced lung inflammation. Methods: This is a parallel-group, randomized, two-arm, open-label, adaptive, multicenter superiority trial with 1:1 allocation ratio. Participants included patients from 18 years of age hospitalized with Covid-19 with 4 or fewer days since symptom onset enrolled at a university and a community hospital in Buenos Aires, Argentina. Exclusion criteria included prior intensive care unit (ICU) admission and use of ARBs/angiotensin converting enzyme inhibitors at randomization. Control arm received standard care alone and treatment arm telmisartan 80 mg twice daily for 14 days. Primary outcomes were C-reactive protein (CRP) plasma levels at day 5 and 8 after randomization. Secondary outcomes included time to discharge within 15 days, admission to ICU and death at 15- and 30-days. NCT04355936 (Completed). Findings: A pragmatic decision to end the study before the third interim analysis was made on Oct. 30th due to sharp reduction in recruitment. A total of 162 patients were randomized. 158 patients enrolled between May 14 and October 30 2020, were included in the analysis, 80 in the standard care and 78 in the telmisartan added to standard care group. Baseline absolute CRP serum levels were 5.53 ± 6.19 mg/dL (95% CI 6.91 to 4.15, n = 80) and 9.04 ± 7.69 (95% CI 9.04 to 10.82, n = 74) in the standard care and telmisartan added to standard care groups, respectively. Day 5 control-group CRP levels were 6.06 ± 6.95 mg/dL (95% CI 7.79-4.35, n = 66) while telmisartan group were 3.83 ± 5.08 mg/dL (95% CI 5.08-2.59, n = 66, p = 0.038). Day 8 CRP levels were 6.30 ± 8.19 mg/dL (95% CI 8.79-3.81, n = 44) and 2.37 ± 3.47 mg/dL (95% CI 3.44-1.30, n = 43, p = 0.0098) in the control and telmisartan groups, respectively (all values expressed as mean ± SD). Kaplan-Meier analysis showed that telmisartan-treated patients had a lower median time-to-discharge (control=15 days; telmisartan=9 days). Death by day 30 was reduced in the telmisartan-treated group (control 22.54%, 16/71; telmisartan 4.29%, 3/70 participants; p = 0.0023). Composite ICU, mechanical ventilation or death was reduced by telmisartan treatment at days 15 and 30. No adverse events were reported. Interpretation: Our study suggests that the ARB telmisartan, a widely used antihypertensive drug, is safe and could reduce morbidity and mortality in hospitalized patients infected with SARS -CoV-2 by anti-inflammatory effects. Further studies employing telmisartan are needed for confirmation of our results and to define its true therapeutic value as a tool against Covid-19.

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COVID-19 pandemic demands a swift response to find therapeutic tools that effectively reduce morbidity and mortality. Despite initial fears, evidence from retrospective observational studies supports the inhibition of the renin-angiotensin system as an emerging pathway to delay or moderate angiotensin II-driven lung inflammation. This has triggered several prospective clinical trials. In this commentary we provide an overview and analysis of current ongoing clinical trials aimed at evaluating the therapeutic efficacy of angiotensin receptor blocker (ARB) use in COVID-19. The relevance of the results of these trials will have to be interpreted depending on the stage and severity of the disease and in light of the start time of their prescription related to the time of diagnosis of COVID-19 as well as the administered doses.

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Abstract Cardiovascular diseases are the number one cause of death globally, but their optimal prevention remains a challenge. A high-risk approach can only have a limited effect at a population level, while population-based strategies can improve and extend the coverage of a high-risk approach. However, one main problem for promoting cardiovascular diseases prevention public policies is the difficulty to foresee population health benefits of a single policy. Computer simulation models can assist with this problem, due to their ability to estimate intervention effects over different periods, and by scaling up the evidence to a broader, more diverse population. Their applicability to countries with different social, political and economic contexts can assist in the design of public policies. There are several models that assess health and economics scenarios, but regardless which model is chosen, when adequately used, they can provide reasonable estimations of health policies' impact. There is a growing consensus amongst the public health communities about the powerful role of population-level policies. They are more effective, cost saving and more equitable when compared with individual-level interventions. Policy makers and the public health community need to make further progress in changing the focus of prevention, from individuals to populations.

Resumen Las enfermedades cardiovasculares son la principal causa de muerte en el mundo, pero su prevención óptima sigue siendo un desafío. El enfoque prioritario a escala individual en pacientes de alto riesgo solo puede tener un efecto limitado a nivel colectivo, mientras que las estrategias de alcance poblacional pueden mejorar y ampliar la cobertura de estos enfoques de alto riesgo. Sin embargo, uno de los principales problemas para promover políticas públicas de prevención de enfermedades cardiovasculares es la dificultad para prever los beneficios que una política única puede tener en salud. Los modelos de simulación por computadora pueden ayudar con este problema, dada su capacidad para estimar los efectos de una intervención en diferentes períodos, ampliando la evidencia a una población más extensa y diversa. Adicionalmente, su aplicabilidad a países con diferentes contextos sociales, políticos y económicos puede asistir en el diseño de políticas públicas. Existen varios modelos que evalúan escenarios tanto de salud como de economía, pero independientemente de qué modelo se elija, usados adecuadamente pueden proporcionar estimaciones razonables del impacto de las políticas de salud. Existe un consenso creciente en el ámbito de la salud pública sobre el importante rol de las políticas poblacionales. Son más efectivas, económicas, y equitativas en comparación con las intervenciones a nivel individual. En la formulación de políticas públicas en general, y de salud pública en particular, se debe avanzar en cambiar el enfoque de la prevención desde las personas a las comunidades.

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