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Background. Femicides are an increasing social problem worldwide. In this study, we aim to describe the trend of femicides in the prepandemic decade and characterize the femicide victims and their perpetrators. Methods. We assessed the trend of femicides in Peru during 2010–2019 and performed a cross-sectional study to analyze the femicides reported in 2019 using open data. Results. We analyzed 166 femicides reported in 24/25 regions of Peru in 2019 and calculated a yearly incidence of 1.01 femicides per 100,000 women. This incidence level represents an increase of 38% compared to the mean annual incidence from 2010 to 2018 (0.74 femicides per 100,000 women). Most femicides occurred in urban areas (64%), through strangling/asphyxiation (25.9%), stabbing (23%), and shooting (16%). Most victims were mothers (61%) 30 years old or over (51%). Most perpetrators have had a partner history with their victims (69%), 30 years old or over (62%), employed (57%), and consumed enablers (51%). Our regression analysis observed that the victim's age was associated with the perpetrator's age and partner history. Conclusions. Femicides are endemic in Peru, and the main characteristics of the victims and their perpetrators offer opportunities for tackling this social problem in Peru and similar low- to middle-income countries.

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.

Peru started its COVID-19 Immunization Program after recording the highest mortality worldwide during the first wave of the pandemic and halfway through its second wave. However, it became one of the fastest developing countries to cover 80% of its adult population with two COVID-19 vaccine doses. Overall, from the beginning of the Program in February 2021 up to March 2022, Peru delivered 66.2 million vaccine doses in a country with 32.8 million inhabitants. To do so, Peru successfully implemented a COVID-19 Immunization Program using an intersectoral and multidisciplinary approach. In addition, and more importantly, the Program included the participation of traditional and nontraditional health leaders. Peru's experience from previous immunization campaigns played a crucial role in its success. This experience allowed Peru to tackle challenges such as having over 80 original languages, a segmented and fragmented health system, and a vast territory with remote communities across coastal, highland, and jungle regions. This manuscript reports the findings of the policy evaluation of the Peru COVID-19 Immunization Program and collects the critical lessons learned during the Program’s first year of implementation.

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.