Covid-19 en el Perú: respuestas estatales y sociales

Lossio, Jorge

doi:10.1590/s0104-59702021005000001

Cited by 22 publications

(15 citation statements)

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“…These results suggest that vaccination campaigns against the disease should focus on rural areas, considering the impact that the pandemic has had on this population. Peru has been one of the countries most affected by the pandemic for reasons including problems due to its fragmented and centralized health system and in decision-making regarding the treatment of infected patients [ 31 , 32 ]. Although there are no studies that verify the distribution of infected patients according to whether they live in a rural or urban area, some studies have shown while the jungle region of Peru did not necessarily have the highest number of infected [ 13 ], it had the highest mortality from COVID-19 [ 33 ], and some of its departments had the highest percentages of excess mortality in our country [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Factors Associated with Not Receiving a Booster Dose of COVID-19 Vaccine in Peru

et al. 2022

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To determine the factors associated with not receiving the booster dose for COVID-19 in Peru, a cross-sectional study by secondary analysis of a University of Maryland and Facebook survey database assessing the global impact of COVID-19 was conducted. Data of Peruvian users of this social network over 18 years of age who answered the survey between 13 February 2022 and 14 April 2022 were analyzed. We evaluated the association between sociodemographic characteristics, comorbidities, and history of COVID-19 with having received a booster dose for COVID-19. Crude (cPR) and adjusted (aPR) prevalence ratios with their respective 95% confidence intervals (95%CI) were calculated. A sample of 20,814 adults, 21.5% of whom reported not receiving the booster dose, was analyzed. People under 75 years of age had a higher prevalence of not having received the booster dose. Likewise, having a university education (aPR = 1.03; 95%CI: 1.02–1.05), secondary, or pre-university education (aPR = 1.07; 95%CI: 1.05–1.09), or having a primary level or less (aPR = 1.11; 95%CI: 1.05–1.18), were associated with a higher prevalence of not receiving the booster, compared to individuals with a postgraduate education. Being employed (aPR = 1.01; 95%CI: 1.00–1.02), having had COVID-19 (aPR = 1.03; 95%CI: 1.01–1.04) and living in a town (aPR = 1.05; 95%CI: 1.02–1.07) or in a rural area (aPR = 1.06; 95%CI: 1.03–1.10), compared to living in the city, had a similar association. On the contrary, the female gender was associated with a lower prevalence of not receiving the booster (aPR = 0.97; 95%CI: 0.96–0.99). Sociodemographic characteristics and a history of having had COVID-19 were associated with the probability of not having received the booster dose for COVID-19 in the Peruvian population.

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Section: Discussionmentioning

confidence: 99%

Factors Associated with Not Receiving a Booster Dose of COVID-19 Vaccine in Peru

et al. 2022

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“…From a social perspective, the inequalities and inequities that increased mortality before the COVID-19 pandemic were mitigated during the rst days of the COVID-19 pandemic 13 . At that time, the world faced a deathly disease without knowing much about its pathogenicity or epidemiology, and more importantly, without any effective treatment to prevent COVID-19 deaths 14 .…”

Section: Discussionmentioning

confidence: 99%

Mortality from all causes before and during the COVID-19 pandemic in Peru: The role of sociodemographic factors and accessibility

Quispe

Vargas

Morales-García

et al. 2022

Preprint

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Peru suffered the highest mortality rates worldwide during the COVID-19 pandemic. In this study, we assessed the Peruvian districts' all causes of mortality-associated sociodemographic factors before and during the COVID-19 pandemic using mixed-effects Poisson regression models. During the pre-pandemic and the first four COVID-19 waves, the Peruvian districts reported mean weekly mortality of 22.3 (standard deviation 40.4), 29.2 (38.7), 32.5 (47.2), 26.8 (38.9), and 24.4 (38.0), respectively. We observed that before the COVID-19 pandemic, the districts' weekly deaths were associated with the human development index ((HDI) adjusted incidence rate ratio (aIRR) 0.11 (95% confidence interval 0.11–0.12)), accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99 (0.99–0.99)). However, during each of the first four COVID-19 waves, the magnitude of association between the districts' weekly deaths and HDI decreased (first, aIRR 0.61 (0.58−0.64); second, aIRR 0.54 (0.52−0.57); third, aIRR 0.20 (0.19−0.22); fourth, aIRR 0.17 (0.15−0.19)), but the association with accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99(0.99–0.99)) remain constant. Before and during the COVID-19 pandemic, a solid association existed between all-cause mortality and the district's sociodemographics, increasing with lower HDI, accessibility, poverty, and anemia rates.

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“…Peru became one of the first Latin American countries to implement quarantine and other restrictive measures; however, the spread of the index virus did not stop across Peru 4 . One year later, Peru accumulated over 210,000 deaths due to COVID-19, leading the list of countries with the highest mortality rates worldwide in the first and second waves 5 .…”

Section: Introductionmentioning

confidence: 99%

Regional clustering and waves patterns due to COVID-19 by the index virus and the lambda/gamma, and delta/omicron SARS-CoV-2 variants in Peru

et al. 2022

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Background: Coronavirus disease 2019 (COVID-19) impact varies substantially due to various factors, so it is critical to characterize its main differences to inform decision-makers about where to focus their interventions and differentiate mitigation strategies. Up to this date, little is known about the patterns and regional clustering of COVID-19 waves worldwide. Methods: We assessed the patterns and regional clustering of COVID-19 waves in Peru by using the weekly mortality rates for each of the 25 regions as an outcome of interest. We obtained the death counts from the National Informatics System of Deaths and population estimates from the National Registry of Identification and Civil Status. In addition, we characterized each wave according to its duration, peak, and mortality rates by age group and gender. Additionally, we used polynomial regression models to compare them graphically and performed a cluster analysis to identify regional patterns. Results: We estimated the average mortality rate at the first, second, and third wave at 13.01, 14.12, and 9.82 per 100,000 inhabitants, respectively, with higher mortality rates among elders and men. The patterns of each wave varied substantially in terms of duration, peak, impact, and wave shapes. Based on our clustering analysis, during the first wave caused by the index virus, the 25 regions of Peru presented six different wave patterns. However, the regions were clustered in two different wave patterns during the second and third, caused by alpha/lambda/delta and omicron. Conclusions: The propagation of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) variants behaved in Peru with varying wave patterns and regional clustering. During the COVID-19 pandemic, the weekly mortality rates followed different spatiotemporal patterns with solid clustering, which might help project the impact of future waves of COVID-19.

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Covid-19 en el Perú: respuestas estatales y sociales

Cited by 22 publications

References 0 publications

Factors Associated with Not Receiving a Booster Dose of COVID-19 Vaccine in Peru

Factors Associated with Not Receiving a Booster Dose of COVID-19 Vaccine in Peru

Mortality from all causes before and during the COVID-19 pandemic in Peru: The role of sociodemographic factors and accessibility

Regional clustering and waves patterns due to COVID-19 by the index virus and the lambda/gamma, and delta/omicron SARS-CoV-2 variants in Peru

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