Excess.deaths.bib

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@techreport{PAHO2019,
abstract = {Core Indicators 2019: Health Trends in the Americas is a milestone achievement. For a quarter of a century now PAHO has--thorugh this publication, provided the latest information on key health and health-related indicators for the Region of the Americas. The 2019 edition presents selected core indicators data in a visual, thought-provoking format to illustrate notable trends over the past 25 years. It also includes the traditional data tables on the 82 core indicators for the countries, territories, and subregions of the Americas, grouped into the following categories: demographic-socioeconomic, health status, risk factors, service coverage, and health systems. Information is also presented on 18 indicators of the Sustainable Development Goals (SDGs). For the complete set of core indicator data (200 indicators, including 26 SDGs), we encourage you to visit PAHO's online database.},
address = {Washington, D.C.},
author = {{Pan American Health Organization}},
doi = {10.37774/9789275121283},
file = {:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/paho.pdf:pdf},
institution = {PAHO},
title = {{Core Indicators 2019: Health Trends in the Americas}},
year = {2019}
}
@article{Gupta2016,
abstract = {Objective To analyse the design and operational status of India's civil registration and vital statistics system and facilitate the system's development into an accurate and reliable source of mortality data. Methods We assessed the national civil registration and vital statistics system's legal framework, administrative structure and design through document review. We did a cross-sectional study for the year 2013 at national level and in Punjab state to assess the quality of the system's mortality data through analyses of life tables and investigation of the completeness of death registration and the proportion of deaths assigned ill-defined causes. We interviewed registrars, medical officers and coders in Punjab state to assess their knowledge and practice. Findings Although we found the legal framework and system design to be appropriate, data collection was based on complex intersectoral collaborations at state and local level and the collected data were found to be of poor quality. The registration data were inadequate for a robust estimate of mortality at national level. A medically certified cause of death was only recorded for 965 992 (16.8{\%}) of the 5 735 082 deaths registered. Conclusion The data recorded by India's civil registration and vital statistics system in 2011 were incomplete. If improved, the system could be used to reliably estimate mortality. We recommend improving political support and intersectoral coordination, capacity building, computerization and state-level initiatives to ensure that every death is registered and that reliable causes of death are recorded – at least within an adequate sample of registration units within each state.},
author = {Gupta, Mamta and Rao, Chalapati and Lakshmi, P. V.M. and Prinja, Shankar and Kumar, Rajesh},
doi = {10.2471/BLT.15.153585},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gupta et al. - 2016 - Estimating mortality using data from civil registration a cross-sectional study in India.pdf:pdf},
issn = {15640604},
journal = {Bulletin of the World Health Organization},
month = {jan},
number = {1},
pages = {10--21},
pmid = {26769992},
publisher = {World Health Organization},
title = {{Estimating mortality using data from civil registration: a cross-sectional study in India}},
url = {/pmc/articles/PMC4709797/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709797/},
volume = {94},
year = {2016}
}
@article{Scortichini2020,
abstract = {BACKGROUND Italy was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group and period of the outbreak. METHODS The analysis was performed using a two-stage interrupted time-series design using daily mortality data for the period January 2015-May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis. RESULTS In the period 15 February-15 May 2020, we estimated an excess of 47 490 [95{\%} empirical confidence intervals (eCIs): 43 984 to 50 362] deaths in Italy, corresponding to an increase of 29.5{\%} (95{\%} eCI: 26.8 to 31.9{\%}) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced increases up to 800{\%} during the peak in late March. There were differences by sex, age and area both in the overall impact and in its temporal distribution. CONCLUSION This study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to the implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.},
author = {Scortichini, Matteo and {Schneider dos Santos}, Rochelle and {De' Donato}, Francesca and {De Sario}, Manuela and Michelozzi, Paola and Davoli, Marina and Masselot, Pierre and Sera, Francesco and Gasparrini, Antonio},
doi = {10.1093/ije/dyaa169},
issn = {0300-5771},
journal = {International Journal of Epidemiology},
month = {oct},
pmid = {33053172},
publisher = {Oxford University Press (OUP)},
title = {{Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time-series analysis}},
url = {/pmc/articles/PMC7665549/?report=abstract https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665549/},
year = {2020}
}
@article{Dahal2021,
abstract = {Background: Low testing rates and delays in reporting hinder the estimation of the mortality burden associated with the COVID-19 pandemic. During a public health emergency, estimating all cause excess deaths above an expected level of death can provide a more reliable picture of the mortality burden. Here, we aim to estimate the absolute and relative mortality impact of COVID-19 pandemic in Mexico. Methods: We obtained weekly mortality time series due to all causes for Mexico, and by gender, and geographic region from 2015 to 2020. We also compiled surveillance data on COVID-19 cases and deaths to assess the timing and intensity of the pandemic and assembled weekly series of the proportion of tweets about ‘death' from Mexico to assess the correlation between people's media interaction about ‘death' and the rise in pandemic deaths. We estimated all-cause excess mortality rates and mortality rate ratio increase over baseline by fitting Serfling regression models and forecasted the total excess deaths for Mexico for the first 4 weeks of 2021 using the generalized logistic growth model. Results: We estimated the all-cause excess mortality rate associated with the COVID-19 pandemic in Mexico in 2020 at 26.10 per 10,000 population, which corresponds to 333,538 excess deaths. Males had about 2-fold higher excess mortality rate (33.99) compared to females (18.53). Mexico City reported the highest excess death rate (63.54) and RR (2.09) compared to rest of the country (excess rate = 23.25, RR = 1.62). While COVID-19 deaths accounted for only 38.64{\%} of total excess deaths in Mexico, our forecast estimate that Mexico has accumulated a total of {\~{}} 61,610 [95{\%} PI: 60,003, 63,216] excess deaths in the first 4 weeks of 2021. Proportion of tweets was significantly correlated with the excess mortality ($\rho$ = 0.508 [95{\%} CI: 0.245, 0.701], p-value = 0.0004). Conclusion: The COVID-19 pandemic has heavily affected Mexico. The lab-confirmed COVID-19 deaths accounted for only 38.64{\%} of total all cause excess deaths (333,538) in Mexico in 2020. This reflects either the effect of low testing rates in Mexico, or the surge in number of deaths due to other causes during the pandemic. A model-based forecast indicates that an average of 61,610 excess deaths have occurred in January 2021.},
author = {Dahal, Sushma and Banda, Juan M. and Bento, Ana I. and Mizumoto, Kenji and Chowell, Gerardo},
doi = {10.1186/s12879-021-06122-7},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dahal et al. - 2021 - Characterizing all-cause excess mortality patterns during COVID-19 pandemic in Mexico.pdf:pdf},
issn = {14712334},
journal = {BMC Infectious Diseases},
keywords = {2020,All-cause mortality,COVID-19,Excess mortality,Mexico,Pandemic},
month = {dec},
number = {1},
pages = {1--10},
pmid = {33962563},
publisher = {BioMed Central Ltd},
title = {{Characterizing all-cause excess mortality patterns during COVID-19 pandemic in Mexico}},
url = {https://doi.org/10.1186/s12879-021-06122-7},
volume = {21},
year = {2021}
}
@article{Kung2020,
abstract = {In countries experiencing marked increases in all-cause mortality during the global pandemic, all-cause mortality more accurately estimated COVID-19 mortality and the onset of the pandemic than the reported COVID-19 mortality rates. ABSTRACT Background},
author = {Kung, Stacey and Doppen, Marjan and Black, Melissa and Braithwaite, Irene and Kearns, Cil{\'{e}}in and Weatherall, Mark and Beasley, Richard and Kearns, Nethmi},
doi = {10.1183/23120541.00766-2020},
issn = {2312-0541},
journal = {ERJ Open Research},
month = {dec},
pages = {00766--2020},
publisher = {European Respiratory Society},
title = {{Underestimation of COVID-19 mortality during the pandemic}},
url = {http://openres.ersjournals.com/lookup/doi/10.1183/23120541.00766-2020},
year = {2020}
}
@article{Iburg2020,
abstract = {Background and objective Many countries have used the new ANACONDA (Analysis of Causes of National Death for Action) tool to assess the quality of their cause of death data (COD), but no cross-country analysis has been done to verify how different or similar patterns of diagnostic errors and data quality are in countries or how they are related to the local cultural or epidemiological environment or to levels of development. Our objective is to measure whether the usability of COD data and the patterns of unusable codes are related to a country's level of socio-economic development. Methods We have assessed the quality of 20 national COD datasets from the WHO Mortality Database by assessing their completeness of COD reporting and the extent, pattern and severity of garbage codes, i.e. codes that provide little or no information about the true underlying COD. Garbage codes were classified into four groups based on the severity of the error in the code. The Vital Statistics Performance Index for Quality (VSPI(Q)) was used to measure the overall quality of each country's mortality surveillance system. Findings The proportion of ‘garbage codes' varied from 7 to 66{\%} across the 20 countries. Countries with a high SDI generally had a lower proportion of high impact (i.e. more severe) garbage codes than countries with low SDI. While the magnitude and pattern of garbage codes differed among countries, the specific codes commonly used did not. Conclusions There is an inverse relationship between a country's socio-demographic development and the overall quality of its cause of death data, but with important exceptions. In particular, some low SDI countries have vital statistics systems that are as reliable as more developed countries. However, in low-income countries, where most people die at home, the proportion of unusable codes often exceeds 50{\%}, implying that half of all cause-specific mortality data collected is of little or no use in guiding public policy. Moreover, the cause of death pattern identified from the data is likely to seriously under-represent the true extent of the leading causes of death in the population, with very significant consequences for health priority setting. Garbage codes are prevalent at all ages, contrary to expectations. Further research into effective strategies deployed in these countries to improve data quality can inform efforts elsewhere to improve COD reporting systems.},
author = {Iburg, Kim Moesgaard and Mikkelsen, Lene and Adair, Tim and Lopez, Alan D.},
doi = {10.1371/journal.pone.0237539},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Iburg et al. - 2020 - Are cause of death data fit for purpose evidence from 20 countries at different levels of socio-economic developme.pdf:pdf},
isbn = {1111111111},
issn = {19326203},
journal = {PLoS ONE},
number = {8 August 2020},
pages = {1--18},
pmid = {32834006},
title = {{Are cause of death data fit for purpose? evidence from 20 countries at different levels of socio-economic development}},
url = {http://dx.doi.org/10.1371/journal.pone.0237539},
volume = {15},
year = {2020}
}
@techreport{Gasparrini2014,
abstract = {Background: Measures of attributable risk are an integral part of epidemiological analyses, particularly when aimed at the planning and evaluation of public health interventions. However, the current definition of such measures does not consider any temporal relationships between exposure and risk. In this contribution, we propose extended definitions of attributable risk within the framework of distributed lag non-linear models, an approach recently proposed for modelling delayed associations in either linear or non-linear exposure-response associations. Methods: We classify versions of attributable number and fraction expressed using either a forward or backward perspective. The former specifies the future burden due to a given exposure event, while the latter summarizes the current burden due to the set of exposure events experienced in the past. In addition, we illustrate how the components related to sub-ranges of the exposure can be separated. Results: We apply these methods for estimating the mortality risk attributable to outdoor temperature in two cities, London and Rome, using time series data for the periods 1993-2006 and 1992-2010, respectively. The analysis provides estimates of the overall mortality burden attributable to temperature, and then computes the components attributable to cold and heat and then mild and extreme temperatures. Conclusions: These extended definitions of attributable risk account for the additional temporal dimension which characterizes exposure-response associations, providing more appropriate attributable measures in the presence of dependencies characterized by potentially complex temporal patterns.},
author = {Gasparrini, Antonio and Leone, Michela},
booktitle = {BMC Medical Research Methodology},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gasparrini, Leone - 2014 - Attributable risk from distributed lag models(2).pdf:pdf},
keywords = {Attributable fraction,Attributable risk,Distributed lag models},
pages = {55},
title = {{Attributable risk from distributed lag models}},
url = {http://www.biomedcentral.com/1471-2288/14/55},
volume = {14},
year = {2014}
}
@misc{McLaughlin2019,
abstract = {Accurate information on mortality and causes of death is essential for program and policy development and monitoring in health, and other sectors. The most reliable source for death data is a well-functioning Civil Registration and Vital Statistics (CRVS) system; however, the CRVS systems are underdeveloped in Latin America, with varying levels of incompleteness between and within the countries 1,2. The Bloomberg Data For Health Initiative (D4H) identified four Latin American countries with functional CRVS systems, which lacked the capacity for providing high-quality mortality information with accurate causes of death for different reasons, including the inability to capture community deaths. These countries, Brazil, Peru, Ecuador and Colombia, joined the Initiative at various times. Their governments had a common desire to strengthen their CRVS systems by using evidence-based interventions to enable the timely flow of high-quality mortality information for the development of health policies and practices. A key outcome for D4H was that technical interventions should be collaboratively driven and ultimately sustainable by government partners. Brazil has a relatively high Vital Statistics Performance Index (VSPI) 1 and high completeness rate of death registration. However, deaths with an ill-defined underlying cause, or coded to non-specific causes (unusable or garbage codes), represented 33{\%} of all deaths in 2013 3. This high burden of garbage codes reflected the percentage of deaths occurring outside health facilities, the nature of the geography in some parts of the country that isolated rural populations from physicians who could certify cause of death, and poor medical certification practices in some areas. To address these},
author = {McLaughlin, Deirdre and Lopez, Alan D.},
booktitle = {Revista Brasileira de Epidemiologia},
doi = {10.1590/1980-549720190016.supl.3},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/McLaughlin, Lopez - 2019 - Strengthening mortality data for health policy and planning The bloomberg data for health initiative in Latin.pdf:pdf},
issn = {1415790X},
number = {3},
pmid = {31826125},
publisher = {Associa{\c{c}}{\~{a}}o Brasileira de P{\'{o}}s Gradua{\c{c}}{\~{a}}o em Sa{\'{u}}de Coletiva},
title = {{Strengthening mortality data for health policy and planning: The bloomberg data for health initiative in Latin America}},
url = {http://www.scielo.br/scielo.php?script=sci{\_}arttext{\&}pid=S1415-790X2019000400802{\&}lng=en{\&}nrm=iso{\&}tlng=en http://www.scielo.br/scielo.php?script=sci{\_}abstract{\&}pid=S1415-790X2019000400802{\&}lng=en{\&}nrm=iso{\&}tlng=en},
volume = {22},
year = {2019}
}
@misc{FT2020,
author = {{Financial Times}},
title = {{Coronavirus tracker: the latest figures as countries fight Covid-19 resurgence}},
url = {https://www.ft.com/content/a2901ce8-5eb7-4633-b89c-cbdf5b386938},
urldate = {2020-12-03},
year = {2020}
}
@article{Banerjee2020,
abstract = {Background: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. Methods: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1{\textperiodcentered}5, 2{\textperiodcentered}0, and 3{\textperiodcentered}0 at differing infection rate scenarios, including full suppression (0{\textperiodcentered}001{\%}), partial suppression (1{\%}), mitigation (10{\%}), and do nothing (80{\%}). We also developed an online, public, prototype risk calculator for excess death estimation. Findings: We included 3 862 012 individuals (1 957 935 [50{\textperiodcentered}7{\%}] women and 1 904 077 [49{\textperiodcentered}3{\%}] men). We estimated that more than 20{\%} of the study population are in the high-risk category, of whom 13{\textperiodcentered}7{\%} were older than 70 years and 6{\textperiodcentered}3{\%} were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4{\textperiodcentered}46{\%} (95{\%} CI 4{\textperiodcentered}41–4{\textperiodcentered}51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1{\textperiodcentered}5, four with an RR of 2{\textperiodcentered}0, and seven with an RR of 3{\textperiodcentered}0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1{\textperiodcentered}5, 36 749 with an RR of 2{\textperiodcentered}0, and 73 498 with an RR of 3{\textperiodcentered}0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1{\textperiodcentered}5, 293 991 with an RR of 2{\textperiodcentered}0, and 587 982 with an RR of 3{\textperiodcentered}0. Interpretation: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. Funding: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.},
author = {Banerjee, Amitava and Pasea, Laura and Harris, Steve and Gonzalez-Izquierdo, Arturo and Torralbo, Ana and Shallcross, Laura and Noursadeghi, Mahdad and Pillay, Deenan and Sebire, Neil and Holmes, Chris and Pagel, Christina and Wong, Wai Keong and Langenberg, Claudia and Williams, Bryan and Denaxas, Spiros and Hemingway, Harry},
doi = {10.1016/S0140-6736(20)30854-0},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Banerjee et al. - 2020 - Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and.pdf:pdf},
issn = {1474547X},
journal = {The Lancet},
month = {may},
number = {10238},
pages = {1715--1725},
pmid = {32405103},
publisher = {Lancet Publishing Group},
title = {{Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study}},
url = {www.caliberresearch.},
volume = {395},
year = {2020}
}
@article{Currie2004,
abstract = {The prediction of future mortality rates is a problem of fundamental importance for the insurance and pensions industry. We show how the method of P-splines can be extended to the smoothing and forecasting of two-dimensional mortality tables. We use a penalized generalized linear model with Poisson errors and show how to construct regression and penalty matrices appropriate for two-dimensional modelling. An important feature of our method is that forecasting is a natural consequence of the smoothing process. We illustrate our methods with two data sets provided by the Continuous Mortality Investigation Bureau, a central body for the collection and processing of UK insurance and pensions data.},
author = {Currie, Iain D and Durban, Maria and Eilers, Paul Hc},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Currie, Durban, Eilers - 2004 - Smoothing and forecasting mortality rates(2).pdf:pdf},
journal = {Statistical  Modelling},
keywords = {P-splines,forecasting,mortality,overdispersion,two dimensions},
number = {4},
pages = {279--298},
title = {{Smoothing and forecasting mortality rates}},
volume = {4},
year = {2004}
}
@article{Vestegaard2020,
abstract = {{\textless}p{\textgreater}A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected  ≥ 65 year olds (91{\%} of all excess deaths), but also 45–64 (8{\%}) and 15–44 year olds (1{\%}). No excess mortality was observed in 0–14 year olds.{\textless}/p{\textgreater}},
author = {Vestergaard, Lasse S and Nielsen, Jens and Richter, Lukas and Schmid, Daniela and Bustos, Natalia and Braeye, Toon and Denissov, Gleb and Veideman, Tatjana and Luomala, Oskari and M{\"{o}}tt{\"{o}}nen, Teemu and Fouillet, Anne and Caserio-Sch{\"{o}}nemann, C{\'{e}}line and an der Heiden, Matthias and Uphoff, Helmut and Lytras, Theodore and Gkolfinopoulou, Kassiani and Paldy, Anna and Domegan, Lisa and O'Donnell, Joan and de' Donato, Francesca and Noccioli, Fiammetta and Hoffmann, Patrick and Velez, Telma and England, Kathleen and van Asten, Liselotte and White, Richard A and T{\o}nnessen, Ragnhild and da Silva, Susana P and Rodrigues, Ana P and Larrauri, Amparo and Delgado-Sanz, Concepci{\'{o}}n and Farah, Ahmed and Galanis, Ilias and Junker, Christoph and Perisa, Damir and Sinnathamby, Mary and Andrews, Nick and O'Doherty, Mark and Marquess, Diogo FP and Kennedy, Sharon and Olsen, Sonja J and Pebody, Richard and Krause, Tyra G and M{\o}lbak, K{\aa}re},
doi = {10.2807/1560-7917.ES.2020.25.26.2001214},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vestergaard et al. - 2020 - Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the(2).pdf:pdf},
issn = {1560-7917},
journal = {Eurosurveillance},
month = {jul},
number = {26},
pages = {2001214},
publisher = {European Centre for Disease Prevention and Control},
title = {{Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the EuroMOMO network, March to April 2020}},
url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.26.2001214},
volume = {25},
year = {2020}
}
@article{Dyer2020,
author = {Dyer, Owen},
doi = {10.3201/eid2203.150977},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dyer - 2020 - Covid-19 Excess deaths point to hidden toll in South Africa as cases surge.pdf:pdf},
journal = {BMJ},
title = {{Covid-19: Excess deaths point to hidden toll in South Africa as cases surge}},
url = {http://dx.doi.org/10.1136/bmj.m3038},
year = {2020}
}
@article{Davidson1993,
author = {Davidson, Russell and MacKinnon, James G},
journal = {OUP Catalogue},
publisher = {Oxford University Press},
title = {{Estimation and inference in econometrics}},
year = {1993}
}
@misc{Joy2020,
abstract = {Correspondence www.thelancet.com/infection Published online August 4, 2020 https://doi.org/10.1016/S1473-3099(20)30632-0 1 We conclude that in about a third of the year, the excess risk amounted to three-quarters of the deaths we might have anticipated in the whole of the previous year. Sharing this estimate might be of help in modelling for future waves of infection and quantifying the impact of future mitigation strategies. SdeL is director of the Oxford RCGP RSC and has attended advisory boards for Sanofi and Seqirus. All other authors declare no competing interests.},
author = {Joy, Mark and Hobbs, F. D.Richard and McGagh, Dylan and Akinyemi, Oluwafunmi and de Lusignan, Simon},
booktitle = {The Lancet Infectious Diseases},
doi = {10.1016/S1473-3099(20)30632-0},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Joy et al. - 2020 - Excess mortality from COVID-19 in an English sentinel network population(2).pdf:pdf},
issn = {14744457},
number = {0},
pmid = {32763192},
publisher = {Lancet Publishing Group},
title = {{Excess mortality from COVID-19 in an English sentinel network population}},
url = {https://doi.org/10.1016/S1473-3099},
volume = {0},
year = {2020}
}
@misc{Mikkelsen2015,
abstract = {Increasing demand for better quality data and more investment to strengthen civil registration and vital statistics (CRVS) systems will require increased emphasis on objective, comparable, cost-effective monitoring and assessment methods to measure progress. We apply a composite index (the vital statistics performance index [VSPI]) to assess the performance of CRVS systems in 148 countries or territories during 1980-2012 and classify them into five distinct performance categories, ranging from rudimentary (with scores close to zero) to satisfactory (with scores close to one), with a mean VSPI score since 2005 of 0{\textperiodcentered}61 (SD 0{\textperiodcentered}31). As expected, the best performing systems were mostly in the European region, the Americas, and Australasia, with only two countries from east Asia and Latin America. Most low-scoring countries were in the African or Asian regions. Globally, only modest progress has been made since 2000, with the percentage of deaths registered increasing from 36{\%} to 38{\%}, and the percentage of children aged under 5 years whose birth has been registered increasing from 58{\%} to 65{\%}. However, several individual countries have made substantial improvements to their CRVS systems in the past 30 years by capturing more deaths and improving accuracy of cause-of-death information. Future monitoring of the effects of CRVS strengthening will greatly benefit from application of a metric like the VSPI, which is objective, costless to compute, and able to identify components of the system that make the largest contributions to good or poor performance.},
author = {Mikkelsen, Lene and Phillips, David E. and Abouzahr, Carla and Setel, Philip W. and {De Savigny}, Don and Lozano, Rafael and Lopez, Alan D.},
booktitle = {The Lancet},
doi = {10.1016/S0140-6736(15)60171-4},
issn = {1474547X},
month = {oct},
number = {10001},
pages = {1395--1406},
pmid = {25971218},
publisher = {Lancet Publishing Group},
title = {{A global assessment of civil registration and vital statistics systems: Monitoring data quality and progress}},
volume = {386},
year = {2015}
}
@article{Brown2020,
abstract = {National predictions of the course of COVID mortality can be used to plan for effective healthcare responses as well as to support COVID policymaking. We developed the Global COVID Assessment of Mortality (GCAM), a statistical model with continually improving precision that combines actual mortality counts with Bayesian inference, to predict COVID trends, currently until December 1, 2020. In Colombia, the GCAM analysis found the peak of COVID mortality around August 12 and an expected total of COVID deaths of 24,000-31,000, or 48{\%}-92{\%} over the total through August 21. In Peru, a first mortality peak occurred around May 24, and given the current trajectory, a second peak is predicted around September 6. Peru can expect 29,000-43,000 COVID deaths, representing an increase of 7{\%}-55{\%} over COVID deaths through August 21. GCAM projections are also used to estimate medical surge capacity needs. To gauge the reliability of COVID mortality forecasts, we compared all-cause mortality from January through June 2020 with average all-cause mortality in previous years in Colombia and Peru, and found that the excesses were consistent with GCAM forecast, most notably a doubling of overall mortality from May 25-June 7th of weeks in Peru. The GCAM results predict that as a percentage of all adult deaths in previous years, Colombia can expect about 13{\%} excess from COVID deaths, whereas Peru can expect 34{\%} excess. Comparisons of GCAM analyses of several other countries with Colombia and Peru demonstrate the extreme variability that characterizes COVID mortality around the world, emphasizing the need for country-specific analyses and ongoing monitoring as more mortality data become available.

{\#}{\#}{\#} Competing Interest Statement

The authors have declared no competing interest.

{\#}{\#}{\#} Funding Statement

This study was partly supported by the World Bank, the Canadian Institutes of Health Research Foundation grant (FDN 154277) and Emergency COVID Research Grant, the Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-06856) and the Connaught Global Challenge program of the University of Toronto. Prabhat Jha is a Canada Research Chair and Dalla Lana Chair of Global Health at the University of Toronto. The funders had no role in the study design, conduct, and reporting.

{\#}{\#}{\#} Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Approval from the IRB was not required as all data are publicly available and no individual patient data were used.

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data used in the GCAM model were taken from publicly available sources (as cited). The GCAM statistical forecasts are updated regularly and made available at {\textless}http://www.cghr.org/covid{\textgreater}.},
author = {Brown, Patrick E and Greenwald, Zo{\"{e}} R and Salinas, Luis Ernesto and Martens, Gabriel Aguirre and Newcombe, Leslie and Gelband, Hellen and Veillard, Jeremy and Jha, Prabhat},
doi = {10.1101/2020.08.24.20181016},
journal = {medRxiv},
month = {nov},
pages = {2020.08.24.20181016},
publisher = {Cold Spring Harbor Laboratory Press},
title = {{Mortality from COVID in Colombia and Peru: Analyses of Mortality Data and Statistical Forecasts}},
url = {https://doi.org/10.1101/2020.08.24.20181016},
year = {2020}
}
@book{Hyndman2019,
address = {Melborne, Australia},
author = {Hyndman, R and Athanasopoulos, G},
edition = {3rd},
title = {{Forecasting: principles and practice}},
year = {2019}
}
@article{Jha2014,
abstract = {Background: Most of the 48 million annual deaths in low- and middle-income countries (LMICs) occur without medical attention at the time of death so that the causes of death (COD) are largely unknown. A review of low-cost methods of obtaining nationally representative COD data is timely.Discussion: Despite clear historic evidence of their usefulness, most LMICs lack reliable nationally representative COD data. Indirect methods to estimate COD for most countries are inadequate, mainly because they currently rely on an average ratio of 1 nationally representative COD to every 850 estimated deaths in order to measure the cause of 25 million deaths across 110 LMICs. Direct measurement of COD is far more reliable and relevant for country priorities. Five feasible methods to expand COD data are: sample registration systems (which form the basis for the ongoing Million Death Study in India; MDS); strengthening the INDEPTH network of 42 demographic surveillance sites; adding retrospective COD surveys to the demographic household and health surveys in 90 countries; post-census retrospective mortality surveys; and for smaller countries, systematic assembly of health records. Lessons learned from the MDS, especially on low-cost, high-quality methods of verbal autopsy, paired with emerging use of electronic data capture and other innovations, can make COD systems low-cost and relevant for a wide range of childhood and adult conditions.Summary: Low-cost systems to obtain and report CODs are possible. If implemented widely, COD systems could identify disease control priorities, help detect emerging epidemics, enable evaluation of disease control programs, advance indirect methods, and improve the accountability for expenditures of disease control programs. {\textcopyright} 2014 Jha; licensee BioMed Central Ltd.},
author = {Jha, Prabhat},
doi = {10.1186/1741-7015-12-19},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jha - 2014 - Reliable direct measurement of causes of death in low- and middle-income countries.pdf:pdf},
issn = {17417015},
journal = {BMC Medicine},
keywords = {Cause of death statistics,Causes of death,Mortality,Sample registration system,Verbal autopsy,Vital statistics},
month = {feb},
number = {1},
pages = {19},
pmid = {24495839},
publisher = {BioMed Central},
title = {{Reliable direct measurement of causes of death in low- and middle-income countries}},
url = {http://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-12-19},
volume = {12},
year = {2014}
}
@article{Hernandez2020,
author = {Hern{\'{a}}ndez-V{\'{a}}squez, Akram and Gamboa-Unsihuay, Jes{\'{u}}s Eduardo and Vargas-Fern{\'{a}}ndez, Rodrigo and Aza{\~{n}}edo, Diego},
doi = {10.5867/medwave.2020.08.8032},
issn = {07176384},
journal = {Medwave},
keywords = {COVID-19,Peru,mortality,social determinants of health},
month = {sep},
number = {8},
pages = {e8031},
pmid = {33017383},
publisher = {NLM (Medline)},
title = {{Exceso de mortalidad en Lima Metropolitana durante la pandemia de COVID-19: comparaci{\'{o}}n a nivel distrital}},
url = {https://pubmed.ncbi.nlm.nih.gov/33017383/},
volume = {20},
year = {2020}
}
@techreport{Inei2019,
abstract = {La informaci{\'{o}}n contenida en este documento puede ser reproducida total o parcialmente, siempre y cuando se mencione la fuente de origen: Instituto Nacional de Estad{\'{i}}stica e Inform{\'{a}}tica.},
author = {INEI},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/INEI - 2019 - PER{\'{U}} Estimaciones y Proyecciones de la Poblaci{\'{o}}n Nacional, 1950-2070.pdf:pdf},
title = {{PER{\'{U}}: Estimaciones y Proyecciones de la Poblaci{\'{o}}n Nacional, 1950-2070}},
year = {2019}
}
@article{Weinberger2020,
abstract = {Importance: Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19. Objective: To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020. Design, Setting, and Population: This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020. Main Outcomes and Measures: Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data. Results: There were approximately 781000 total deaths in the United States from March 1 to May 30, 2020, representing 122300 (95{\%} prediction interval, 116800-127000) more deaths than would typically be expected at that time of year. There were 95235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28{\%} higher than the official tally of COVID-19-reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths. Conclusions and Relevance: Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states..},
author = {Weinberger, Daniel M. and Chen, Jenny and Cohen, Ted and Crawford, Forrest W. and Mostashari, Farzad and Olson, Don and Pitzer, Virginia E. and Reich, Nicholas G. and Russi, Marcus and Simonsen, Lone and Watkins, Anne and Viboud, Cecile},
doi = {10.1001/jamainternmed.2020.3391},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Weinberger et al. - 2020 - Estimation of Excess Deaths Associated with the COVID-19 Pandemic in the United States, March to May 2020.pdf:pdf},
issn = {21686114},
journal = {JAMA Internal Medicine},
keywords = {The JAMA Network},
number = {May},
pages = {E1--E9},
pmid = {32609310},
title = {{Estimation of Excess Deaths Associated with the COVID-19 Pandemic in the United States, March to May 2020}},
volume = {06520},
year = {2020}
}
@article{Li2020,
author = {Li, Han and Hyndman, Rob},
doi = {10.2139/ssrn.3550683},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Hyndman - 2020 - Assessing Longevity Inequality in the U.S. What Can Be Said About the Future.pdf:pdf},
journal = {SSRN Electronic Journal},
keywords = {and business analytics,australia,department of actuarial studies,email address,forecast reconciliation,heterogeneity,longevity,macquarie university,mortality modeling},
pages = {1--18},
title = {{Assessing Longevity Inequality in the U.S.: What Can Be Said About the Future?}},
year = {2020}
}
@article{Rossen2020,
abstract = {As of October 15, 216,025 deaths from coronavirus disease 2019 (COVID-19) have been reported in the United States*; however, this number might underestimate the total impact of the pandemic on mortality. Measures of excess deaths have been used to estimate the impact of public health pandemics or disasters, particularly when there are questions about underascertainment of deaths directly attributable to a given event or cause (1-6).† Excess deaths are defined as the number of persons who have died from all causes, in excess of the expected number of deaths for a given place and time. This report describes trends and demographic patterns in excess deaths during January 26-October 3, 2020. Expected numbers of deaths were estimated using overdispersed Poisson regression models with spline terms to account for seasonal patterns, using provisional mortality data from CDC's National Vital Statistics System (NVSS) (7). Weekly numbers of deaths by age group and race/ethnicity were assessed to examine the difference between the weekly number of deaths occurring in 2020 and the average number occurring in the same week during 2015-2019 and the percentage change in 2020. Overall, an estimated 299,028 excess deaths have occurred in the United States from late January through October 3, 2020, with two thirds of these attributed to COVID-19. The largest percentage increases were seen among adults aged 25-44 years and among Hispanic or Latino (Hispanic) persons. These results provide information about the degree to which COVID-19 deaths might be underascertained and inform efforts to prevent mortality directly or indirectly associated with the COVID-19 pandemic, such as efforts to minimize disruptions to health care.},
author = {Rossen, Lauren M. and Branum, Amy M. and Ahmad, Farida B. and Sutton, Paul and Anderson, Robert N.},
doi = {10.15585/mmwr.mm6942e2},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Rossen et al. - 2020 - Excess Deaths Associated with COVID-19, by Age and Race and Ethnicity — United States, January 26–October 3, 2(2).pdf:pdf},
issn = {0149-2195},
journal = {MMWR. Morbidity and Mortality Weekly Report},
keywords = {COVID-19,COVID-19 Mortality United States,Cause of death,Coronavirus [CoV],Data {\&} Statistics,Death Rates,Excess Deaths Among Adults,Excess Deaths Among Hispanic Or Latino Persons,Excess Deaths Associated With COVID-19,Excess Deaths In The U.S.,MMWR,Morbidity {\&} Mortality Weekly Report,Race and Ethnicity},
month = {oct},
number = {42},
pages = {1522--1527},
publisher = {Centers for Disease Control MMWR Office},
title = {{Excess Deaths Associated with COVID-19, by Age and Race and Ethnicity — United States, January 26–October 3, 2020}},
url = {http://www.cdc.gov/mmwr/volumes/69/wr/mm6942e2.htm?s{\_}cid=mm6942e2{\_}w},
volume = {69},
year = {2020}
}
@misc{Setel2020,
author = {Setel, Philip and Abouzahr, Carla and Atuheire, Emily B. and Bratschi, Martin and Cercone, Emily and Chinganya, Oliver and Clapham, Benjamin and Clark, Samuel J. and Congdon, Carlie and de Savigny, Don and Karpati, Adam and Nichols, Erin and Jakob, Robert and Mwanza, James and Muhwava, William and Nahmias, Petra and Ortiz, Elizabeth M. and Tshangela, Akhona},
booktitle = {Bulletin of the World Health Organization},
doi = {10.2471/BLT.20.263194},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Setel et al. - 2020 - Mortality surveillance during the COVID-19 pandemic.pdf:pdf},
issn = {15640604},
keywords = {Akhona Tshangela,Betacoronavirus,COVID-19,Carla AbouZahr,Coronavirus Infections / epidemiology*,Coronavirus Infections / mortality,Editorial,Humans,MEDLINE,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,PMC7265935,Pandemics*,Philip Setel,Pneumonia,Population Surveillance / methods*,PubMed Abstract,SARS-CoV-2,Viral / epidemiology*,Viral / mortality,doi:10.2471/BLT.20.263194,pmid:32514207},
month = {jun},
number = {6},
pages = {374},
pmid = {32514207},
publisher = {World Health Organization},
title = {{Mortality surveillance during the COVID-19 pandemic}},
url = {https://pubmed.ncbi.nlm.nih.gov/32514207/ https://pubmed.ncbi.nlm.nih.gov/32514207/?dopt=Abstract},
volume = {98},
year = {2020}
}
@article{Douglas2020,
abstract = {Countries worldwide have implemented strict controls on movement in response to the covid-19 pandemic. The aim is to cut transmission by reducing close contact (box 1), but the measures have profound consequences. Several sectors are seeing steep reductions in business, and there has been panic buying in shops. Social, economic, and health consequences are inevitable. Box 1: Social distancing measures • Advising the whole population to self-isolate at home if they or their family have symptoms • Bans on social gatherings (including mass gatherings) • Stopping flights and public transport • Closure of "non-essential" workplaces (beyond the health and social care sector, utilities, and the food chain) with continued working from home for those that can • Closure of schools, colleges, and universities • Prohibition of all "non-essential" population movement • Limiting contact for special populations (eg, care homes, prisons) The health benefits of social distancing measures are obvious, with a slower spread of infection reducing the risk that health services will be overwhelmed. But they may also prolong the pandemic and the restrictions adopted to mitigate it. 1 Policy makers need to balance these considerations while paying attention to broader effects on health and health equity.},
author = {Douglas, Margaret and Katikireddi, Srinivasa Vittal and Taulbut, Martin and McKee, Martin and McCartney, Gerry},
doi = {10.1136/bmj.m1557},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Douglas et al. - 2020 - Mitigating the wider health effects of covid-19 pandemic response.pdf:pdf},
issn = {17561833},
journal = {The BMJ},
month = {apr},
pmid = {32341002},
publisher = {BMJ Publishing Group},
title = {{Mitigating the wider health effects of covid-19 pandemic response}},
url = {http://www.heal},
volume = {369},
year = {2020}
}
@article{Cuellar2021,
abstract = {Background In early 2020, Ecuador reported one of the highest surges of per capita deaths across the globe. Methods We collected a comprehensive dataset containing individual death records between 2015 and 2020 from the Ecuadorian National Institute of Statistics and Census and the Ecuadorian Ministry of Government. We computed the number of excess deaths across time, geographical locations and demographic groups using Poisson regression methods. Results Between January 1 st and September 23 rd , 2020, the number of excess deaths in Ecuador is 36,402 (95{\%} CI: 35,762-36,827) or 208 per 10 5 population, which is 171{\%} of the expected deaths in that period in a typical year. Only 20{\%} of the excess deaths are attributable to confirmed COVID-19 deaths. Strikingly, in provinces that were most affected by COVID-19, such as Guayas and Santa Elena, the all-cause deaths are more than double the expected number of deaths that would have occurred in a normal year. The extent of excess deaths in men is higher than in women, and the number of excess deaths increases with age. Indigenous populations had the highest level of excess deaths among all ethnic groups. Conclusions Overall, the exceptionally high level of excess deaths in Ecuador highlights the enormous burden and heterogeneous impact of COVID-19 on mortality especially in older age groups and indigenous populations in Ecuador that was not fully revealed by COVID-19 death counts. Together with the limited testing in Ecuador, our results suggest that the majority of the excess deaths were likely to be undocumented COVID-19 deaths.},
author = {Cu{\'{e}}llar, Leticia and Torres, Irene and Romero-Severson, Ethan and Mahesh, Riya and Ortega, Nathaniel and Pungitore, Sarah and Hengartner, Nicolas and Ke, Ruian},
doi = {10.1101/2021.02.25.21252481},
journal = {medRxiv : the preprint server for health sciences},
month = {mar},
pmid = {33688690},
publisher = {Cold Spring Harbor Laboratory Preprints},
title = {{Excess deaths reveal the true spatial, temporal, and demographic impact of COVID-19 on mortality in Ecuador.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33688690 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC7941667},
year = {2021}
}
@article{Queiroz2017,
abstract = {Objective: Assess the completeness of the DataSUS SIM death-count registry, by sex and Brazilian state, and estimate the probability of adult mortality (45q15), by sex and state, from 1980 to 2010. Methods: The study was based on mortality data obtained in the DataSUS Mortality Information System, from 1980 to 2010, and on population data from the 1980, 1991, 2000, and 2010 demographic censuses. The quality assessment of the registry data was conducted using traditional demographic and death distribution methods, and death probabilities were calculated using life-table concepts. Results: The results show a considerable improvement in the completeness of the death-count coverage in Brazil since 1980. In the southeast and south, we observed the complete coverage of the adult mortality registry, which did not occur in the previous decade. In the northeast and north, there were still places with a low coverage from 2000 to 2010, although there was a clear improvement in the quality of data. For all Brazilian states, there was a decline in the probability of adult mortality; we observed, however, that the death probability for males is much higher than that for females throughout the whole analysis period. Conclusion: The observed improvements seem to be related to investments in the public health care system and administrative procedures to improve the recording of vital events.},
author = {Queiroz, Bernardo Lanza and {De Araujo FreireI}, Fl{\'{a}}vio Henrique Miranda and Gonzaga, Marcos Roberto and {De Lima}, Everton Emanuel Campos},
doi = {10.1590/1980-5497201700050003},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Queiroz et al. - 2017 - Estimativas do grau de cobertura e da mortalidade adulta (45q15) para as unidades da federa{\c{c}}{\~{a}}o no Brasil entre 1.pdf:pdf},
issn = {1415790X},
journal = {Revista Brasileira de Epidemiologia},
keywords = {Brazil,Demography,Mortality,Underregistration},
number = {Suppl 01},
pages = {21--33},
pmid = {28658370},
publisher = {Assocaicao Brasileira de Pos, Gradacao em Saude Coletiva},
title = {{Estimativas do grau de cobertura e da mortalidade adulta (45q15) para as unidades da federa{\c{c}}{\~{a}}o no Brasil entre 1980 e 2010}},
url = {https://pubmed.ncbi.nlm.nih.gov/28658370/},
volume = {20},
year = {2017}
}
@article{Reyes2021,
abstract = {Background Worldwide, Peru has one of the highest infection fatality rates of COVID-19, and its capital city, Lima, accumulates roughly 50{\%} of diagnosed cases. Despite surveillance efforts to assess the extent of the pandemic, reported cases and deaths only capture a fraction of its impact due to COVID-19's broad clinical spectrum. This study aimed to estimate the seroprevalence of SARS-CoV-2 in Lima, stratified by age, sex, region, socioeconomic status (SES), overcrowding, and symptoms. Methods We conducted a multi-stage, population-based serosurvey in Lima, between June 28th and July 9th, 2020, after 115 days of the index case and after the first peak cases. We collected whole blood samples by finger-prick and applied a structured questionnaire. A point-of-care rapid serological test assessed IgM and IgG antibodies against SARS-CoV-2. Seroprevalence estimates were adjusted by sampling weights and test performance. Additionally, we performed RT-PCR molecular assays to seronegatives and estimated the infection prevalence. Findings We enrolled 3212 participants from 797 households and 241 sample clusters from Lima in the analysis. The SARS-CoV-2 seroprevalence was 20{\textperiodcentered}8{\%} (95{\%}CI 17{\textperiodcentered}2-23{\textperiodcentered}5), and the prevalence was 25{\textperiodcentered}2{\%} (95{\%}CI 22{\textperiodcentered}5-28{\textperiodcentered}2). Seroprevalence was equally distributed by sex (aPR=0{\textperiodcentered}96 [95{\%}CI 0{\textperiodcentered}85-1{\textperiodcentered}09, p = 0{\textperiodcentered}547]) and across all age groups, including ≥60 versus ≤11 years old (aPR=0{\textperiodcentered}96 [95{\%}CI 0{\textperiodcentered}73-1{\textperiodcentered}27, p = 0{\textperiodcentered}783]). A gradual decrease in SES was associated with higher seroprevalence (aPR=3{\textperiodcentered}41 [95{\%}CI 1{\textperiodcentered}90-6{\textperiodcentered}12, p{\textless}0{\textperiodcentered}001] in low SES). Also, a gradual increase in the overcrowding index was associated with higher seroprevalence (aPR=1{\textperiodcentered}99 [95{\%}CI 1{\textperiodcentered}41-2{\textperiodcentered}81, p{\textless}0{\textperiodcentered}001] in the fourth quartile). Seroprevalence was also associated with contact with a suspected or confirmed COVID-19 case, whether a household member (48{\textperiodcentered}9{\%}, aPR=2{\textperiodcentered}67 [95{\%}CI 2{\textperiodcentered}06-3{\textperiodcentered}47, p{\textless}0{\textperiodcentered}001]), other family members (27{\textperiodcentered}3{\%}, aPR=1{\textperiodcentered}66 [95{\%}CI 1{\textperiodcentered}15-2{\textperiodcentered}40, p = 0{\textperiodcentered}008]) or a workmate (34{\textperiodcentered}1{\%}, aPR=2{\textperiodcentered}26 [95{\%}CI 1{\textperiodcentered}53-3{\textperiodcentered}35, p{\textless}0{\textperiodcentered}001]). More than half of seropositive participants reported never having had symptoms (56{\textperiodcentered}1{\%}, 95{\%} CI 49{\textperiodcentered}7-62{\textperiodcentered}3). Interpretation This first estimate of SARS-CoV-2 seroprevalence in Lima shows an intense transmission scenario, despite the government's numerous interventions early established. Susceptibles across age groups show that physical distancing interventions must not be relaxed. SES and overcrowding households are associated with seroprevalence. This study highlights the importance of considering the existing social inequalities for implementing the response to control transmission in low- and middle-income countries.},
author = {Reyes-Vega, Mary F. and Soto-Cabezas, M.Gabriela and C{\'{a}}rdenas, Fany and Martel, Kevin S. and Valle, Andree and Valverde, Juan and Vidal-Anzardo, Margot and Falc{\'{o}}n, Mar{\'{i}}a Elena and Munayco, C{\'{e}}sar V.},
doi = {10.1016/j.eclinm.2021.100801},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Reyes-Vega et al. - 2021 - SARS-CoV-2 prevalence associated to low socioeconomic status and overcrowding in an LMIC megacity A populatio.pdf:pdf},
issn = {25895370},
journal = {EClinicalMedicine},
month = {apr},
pages = {100801},
pmid = {33817611},
publisher = {Elsevier BV},
title = {{SARS-CoV-2 prevalence associated to low socioeconomic status and overcrowding in an LMIC megacity: A population-based seroepidemiological survey in Lima, Peru}},
volume = {34},
year = {2021}
}
@article{Dowd2020,
abstract = {Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.},
author = {Dowd, Jennifer Beam and Andriano, Liliana and Brazel, David M. and Rotondi, Valentina and Block, Per and Ding, Xuejie and Liu, Yan and Mills, Melinda C.},
doi = {10.1073/pnas.2004911117},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dowd et al. - 2020 - Demographic science aids in understanding the spread and fatality rates of COVID-19.pdf:pdf},
issn = {10916490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Age structure,COVID-19,Demography,Mortality},
number = {18},
pages = {9696--9698},
pmid = {32300018},
title = {{Demographic science aids in understanding the spread and fatality rates of COVID-19}},
volume = {117},
year = {2020}
}
@article{Beaney2020,
author = {Beaney, Thomas and Clarke, Jonathan M and Jain, Vageesh and {Kataria Golestaneh}, Amelia and Lyons, Gemma and Salman, David and Majeed, Azeem},
doi = {10.1177/0141076820956802},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Beaney et al. - Unknown - Excess mortality the gold standard in measuring the impact of COVID-19 worldwide.pdf:pdf},
title = {{Excess mortality: the gold standard in measuring the impact of COVID-19 worldwide?}}
}
@article{Kontis2020,
abstract = {The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95{\%} credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: {\~{}}100 excess deaths per 100,000 people, equivalent to a 37{\%} (30–44{\%}) relative increase in England and Wales and 38{\%} (31–45{\%}) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5{\%} or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.},
author = {Kontis, Vasilis and Bennett, James E. and Rashid, Theo and Parks, Robbie M. and Pearson-Stuttard, Jonathan and Guillot, Michel and Asaria, Perviz and Zhou, Bin and Battaglini, Marco and Corsetti, Gianni and McKee, Martin and {Di Cesare}, Mariachiara and Mathers, Colin D. and Ezzati, Majid},
doi = {10.1038/s41591-020-1112-0},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kontis et al. - 2020 - Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortal.pdf:pdf},
issn = {1546170X},
journal = {Nature Medicine},
keywords = {Policy,Public health},
month = {dec},
number = {12},
pages = {1919--1928},
pmid = {33057181},
publisher = {Nature Research},
title = {{Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries}},
url = {https://doi.org/10.1038/s41591-020-1112-0},
volume = {26},
year = {2020}
}
@article{Yanez2020,
author = {Ya{\~{n}}ez, Jaime and Alvarez-Risco, Aldo and Delgado-Zegarra, Alvaro},
journal = {BMJ},
number = {m2518},
title = {{Does Peru really have that high number of COVID-19 confirmed cases? The deception of combining RT-PCR and rapid test results | The BMJ}},
url = {https://www.bmj.com/content/369/bmj.m2518/rr-4},
volume = {369},
year = {2020}
}
@article{Lloyd2020,
abstract = {{\textless}p{\textgreater}For all health conditions, reliable age-disaggregated data are vital for both epidemiological analysis and monitoring the relative prioritization of different age groups in policy responses. This is especially essential in the case of Coronavirus Disease-2019 (COVID-19), given the strong association between age and case fatality. This paper assesses the availability and quality of age-based data on reported COVID-19 cases and deaths for low and middle-income countries. It finds that the availability of reliable data which permit specific analyses of older people is largely absent. The paper explores the potential of excess mortality estimates as an alternative metric of the pandemic's effects on older populations. Notwithstanding some technical challenges, this may offer a better approach, especially in countries where cause of death data is unreliable.{\textless}/p{\textgreater}},
author = {Lloyd-Sherlock, Peter and Sempe, Lucas and McKee, Martin and Guntupalli, Aravinda},
doi = {10.1093/geront/gnaa153},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lloyd-Sherlock et al. - 2020 - Problems of Data Availability and Quality for COVID-19 and Older People in Low- and Middle-Income Coun(3).pdf:pdf},
issn = {0016-9013},
journal = {The Gerontologist},
keywords = {Ageism,COVID-19,Data,Low-and middle-income countries},
month = {oct},
pages = {1--4},
publisher = {Oxford University Press (OUP)},
title = {{Problems of Data Availability and Quality for COVID-19 and Older People in Low- and Middle-Income Countries}},
url = {https://academic.oup.com/gerontologist/advance-article/doi/10.1093/geront/gnaa153/5918111},
volume = {XX},
year = {2020}
}
@article{Rivera2020,
abstract = {Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia, and influenza mortality during the COVID-19 pandemic using the September 11, 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from September 27, 2015 to May 9, 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania, and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95{\%} confidence interval (CI) (100013, 127501) vs. 78834 COVID-19 deaths) and 9 states: California (excess mortality 95{\%} CI (3338, 6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95{\%} CI (3095, 3952) vs. 2932 COVID-19 deaths); Illinois (95{\%} CI (4646, 6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95{\%} CI (2341, 3183) vs. 2267 COVID-19 deaths); Massachusetts (95{\%} CI (5562, 7201) vs. 5050 COVID-19 deaths); New Jersey (95{\%} CI (13170, 16058) vs. 10465 COVID-19 deaths); New York (95{\%} CI (32538, 39960) vs. 26584 COVID-19 deaths); and Pennsylvania (95{\%} CI (5125, 6560) vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.},
author = {Rivera, R. and Rosenbaum, J. E. and Quispe, W.},
doi = {10.1017/S0950268820002617},
issn = {14694409},
journal = {Epidemiology and Infection},
pmid = {33115546},
publisher = {Cambridge University Press},
title = {{Excess mortality in the united states during the first three months of the COVID-19 pandemic}},
year = {2020}
}
@article{Miki2018,
abstract = {Background: Mortality statistics derived from cause of death data are an important source of information for population health monitoring, priority setting and planning. In Per{\'{u}}, almost all death certificates are issued by doctors because it is a legal requirement. However, the quality of cause of death data is poor. In August 2016, the Ministry of Health of Per{\'{u}} decided to make two specific interventions to improve cause of death data: to introduce an online death certification system and to train doctors in standard death certification practices. Methods: The study comprised a random sample of 300 pre-intervention death certificates, 900 death certificates that were part of the online intervention, and 900 death certificates that were part of both the online and training interventions. All the deaths had occurred between January and September 2017. We used the Assessing the quality of death certification tool from the University of Melbourne for the assessment. We examined the frequency of common errors in death certificates, the frequency of any error and the average error score for each category of: age group, sex, doctor's seniority, doctor's speciality, level of health facility and broad cause of death. Results: The average error score declined by 38{\%} due to the online intervention and by a further 26{\%} due to the training intervention. Improved certification practices remained after controlling for potentially confounding factors. Main improvements were reductions in the absence of a time interval (66{\%} of certificates), incorrect sequence of causes (22{\%}), and ill-defined conditions (13{\%}). Conclusions: This study demonstrates how the two interventions introduced by the Ministry of Health in Per{\'{u}} improved the correctness of death certificates. The study also provides evidence on necessary changes to the training program to address the poor certification practices that have remained after implementation of the online system.},
author = {Miki, Janet and Rampatige, Rasika and Richards, Nicola and Adair, Tim and Cortez-Escalante, Juan and Vargas-Herrera, Javier},
doi = {10.1186/s12889-018-6264-1},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Miki et al. - 2018 - Saving lives through certifying deaths Assessing the impact of two interventions to improve cause of death data in.pdf:pdf},
issn = {14712458},
journal = {BMC Public Health},
keywords = {Cause of death,Certification,Intervention,Mortality,Online,Per{\'{u}},Quality,Training},
number = {1},
pages = {1--11},
pmid = {30509233},
publisher = {BMC Public Health},
title = {{Saving lives through certifying deaths: Assessing the impact of two interventions to improve cause of death data in Per{\'{u}}}},
volume = {18},
year = {2018}
}
@article{Steenland2006,
abstract = {There are a number of measures that quantify the public health burden due to specific risk factors for specific diseases. Although these measures are of importance for policymakers, epidemiologists do not often calculate them or may be unfamiliar with some of the issues involved when they do. The primary measure of interest is the attributable fraction (AF), representing the fraction of cases or deaths from a specific disease that would not have occurred in the absence of exposure to a specific risk factor either in the exposed population or the population as a whole. AFs can be multiplied by the total number of cases of a given disease to obtain a "body count"-the absolute number of preventable cases due to a specific risk factor. Two other measures of public health burden, used in conjunction with AFs, are attributable years-of-life-lost and attributable disability-adjusted life-years. We provide an overview of the AF and related measures and discuss some of the specific issues involved in calculating AFs. These issues include calculating the variance of AFs (such as Monte Carlo sensitivity methods), biases arising from some formulas for the AF, sources of data for calculating AFs, dependence of AFs on basic decisions about what exposure-disease associations are causal, and extrapolation from the source population to the target population. Copyright {\textcopyright} 2006 by Lippincott Williams {\&} Wilkins.},
author = {Steenland, Kyle and Armstrong, Ben},
doi = {10.1097/01.ede.0000229155.05644.43},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Steenland, Armstrong - 2006 - An overview of methods for calculating the burden of disease due to specific risk factors.pdf:pdf},
issn = {10443983},
journal = {Epidemiology},
number = {5},
pages = {512--519},
pmid = {16804473},
title = {{An overview of methods for calculating the burden of disease due to specific risk factors}},
volume = {17},
year = {2006}
}
@misc{Taylor2021,
abstract = {Peru was praised for imposing swift pandemic measures early on, so why are its excess deaths so high, asks Luke Taylor In March 2020, as the financial pain caused by Peru's national lockdown began to bite, thousands of families fled the capital of Lima for their rural hometowns. With small children and possessions strapped to their backs they trekked up to hundreds of miles through the Andes to return to their families for shelter and support. By April around 170 000 Peruvians had requested support from their local government to return home to the countryside. Many had lost their jobs after businesses were closed and residents were ordered to stay at home on 16 March.1 Some of those who decided it was better to walk home than stay in the capital were repelled by police, who fired teargas in an attempt to stop them.2 Peru's government had acted swiftly, imposing a lockdown just 10 days after its first case was reported. It was hoped that the pain suffered by its citizens would be short term and eventually justified by a victory over the virus. Instead, the mass migration of Peru's families has become symbolic of its failure to understand its people. “Why did the Ministry of Health not hire buses to get people to certain destinations and pay them to quarantine for two weeks?” asks Valeria Paz-Soldan, who runs public health surveillance programmes in Peru for the US Tulane University. “There was so little consideration for the reality for people who were stuck in the wrong place at the wrong time.” A year on, Peru {\ldots}},
author = {Taylor, Luke},
booktitle = {The BMJ},
doi = {10.1136/bmj.n611},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Taylor - 2021 - Covid-19 Why Peru suffers from one of the highest excess death rates in the world.pdf:pdf},
issn = {17561833},
month = {mar},
pmid = {33687923},
publisher = {BMJ Publishing Group},
title = {{Covid-19: Why Peru suffers from one of the highest excess death rates in the world}},
url = {http://dx.doi.org/10.1136/bmj.n611},
volume = {372},
year = {2021}
}
@article{Tadbiri2020,
author = {Tadbiri, Hooman and Moradi-Lakeh, Maziar and Naghavi, Mohsen},
doi = {10.34171/MJIRI.34.80},
issn = {1016-1430},
journal = {Medical journal of the Islamic Republic of Iran},
keywords = {Hooman Tadbiri,MEDLINE,Maziar Moradi-Lakeh,Mohsen Naghavi,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,PMC7711045,PubMed Abstract,doi:10.34171/mjiri.34.80,pmid:33306040},
pmid = {33306040},
publisher = {Med J Islam Repub Iran},
title = {{All-cause excess mortality and COVID-19-related deaths in Iran}},
url = {https://pubmed.ncbi.nlm.nih.gov/33306040/},
volume = {34},
year = {2020}
}
@techreport{INEIpob2020,
abstract = {Todos los a{\~{n}}os el d{\'{i}}a 11 de julio se conmemora el “D{\'{i}}a Mundial de Poblaci{\'{o}}n”, para reflexionar y sensibilizar acerca de la din{\'{a}}mica demogr{\'{a}}fica y los problemas que dificultan el logro del bienestar de los diferentes grupos de poblaci{\'{o}}n. En la coyuntura actual analizar los efectos de la pandemia del COVID-19 a{\'{u}}n es prematuro, sin embargo, se aborda los primeros impactos visibles. Al 30 de junio del a{\~{n}}o 2020, en el Per{\'{u}} somos 32 millones 625 mil 948 personas.},
address = {Lima},
author = {INEI},
booktitle = {Estado de la Poblaci{\'{o}}n Peruana 2020},
file = {:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/pob2020inei.pdf:pdf},
institution = {INEI},
pages = {1--50},
title = {{Estado de la poblaci{\'{o}}n peruana 2020}},
url = {https://www.inei.gob.pe/media/MenuRecursivo/publicaciones{\_}digitales/Est/Lib1743/Libro.pdf},
year = {2020}
}
@article{Pollard2018,
abstract = {Introduction: Over 80{\%} of rural households in Peru use solid fuels as their primary source of domestic energy, which contributes to several health problems. In 2016, 6.7 million Peruvians were living in rural areas. The Fondo de Inclusi{\'{o}}n Social Energ{\'{e}}tico (FISE) LPG Promotion Program, which began in 2012 and is housed under the Ministry of Energy and Mining, is a government-sponsored initiative aimed at reducing use of solid fuels by increasing access to clean fuel for cooking to poor Peruvian households. Methods: We conducted a mixed methods study incorporating data from publicly available records and reports, a community survey of 375 households in Puno (the province with the largest number of FISE beneficiary households), and in-depth interviews with community members and key stakeholders. We used the Reach, Effectiveness – Adoption, Implementation, Maintenance (RE-AIM) framework to guide our data collection and analysis efforts. In a sample of 95 households, we also measured 48-hour area concentrations and personal exposures to fine particulate matter (PM2.5). Results: The FISE LPG promotion program has achieved high geographical reach; the program is currently serving households in 100{\%} of districts in Peru. Households with access to electricity may be participating at a higher level than households without electricity because the program is implemented primarily by electricity distributors. In a sample of 95 households, FISE beneficiaries experienced a reduction in kitchen concentrations of PM2.5; however, there were no differences in personal exposures, and both kitchen and personal exposures were above the WHO intermediate target for indoor air quality. Among the 375 households surveyed, stove stacking with biomass fuels was reported in {\textgreater}95{\%} of both beneficiary and non-beneficiary households, with fewer than 5{\%} reporting exclusive use. In-depth interviews suggest that the complexity of the enrollment process and access to LPG distribution points may be key barriers to participating in FISE. Conclusion: The FISE LPG Program has achieved high reach and its targeted subsidy and surcharge-based financing structure represent a potentially feasible and sustainable model for other government programs. However, the prevalence of stove stacking among FISE beneficiaries remains high. There is a need for improved communication channels between program implementers and beneficiaries. FISE should also consider expanding the mobile LPG network and community delivery service to reduce physical barriers and indirect costs of LPG acquisition. Finally, increasing the value of LPG vouchers to completely cover one or two tanks a month, or alternatively, introducing behavior change strategies to reduce monthly LPG usage, may facilitate the transition to exclusive LPG use.},
author = {Pollard, Suzanne L. and Williams, Kendra N. and O'Brien, Carolyn J. and Winiker, Abigail and Puzzolo, Elisa and Kephart, Josiah L. and Fandi{\~{n}}o-Del-Rio, Magdalena and Tarazona-Meza, Carla and Grigsby, Matthew R. and Chiang, Maril{\'{u}} and Checkley, William},
doi = {10.1016/j.esd.2018.06.001},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pollard et al. - 2018 - An evaluation of the Fondo de Inclusi{\'{o}}n Social Energ{\'{e}}tico program to promote access to liquefied petroleum gas i.pdf:pdf},
issn = {23524669},
journal = {Energy for Sustainable Development},
keywords = {Adoption,Biomass fuels,Clean cooking,LPG,Program evaluation,RE-AIM},
month = {oct},
pages = {82--93},
publisher = {Elsevier B.V.},
title = {{An evaluation of the Fondo de Inclusi{\'{o}}n Social Energ{\'{e}}tico program to promote access to liquefied petroleum gas in Peru}},
volume = {46},
year = {2018}
}
@article{Freitas2020,
abstract = {INTRODUCTION: In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak a pandemic. In Brazil, 110 thousand cases and 5,901 deaths were confirmed by the end of April 2020. The scarcity of laboratory resources, the overload on the service network, and the broad clinical spectrum of the disease make it difficult to document all the deaths due to COVID-19. The aim of this study was to assess the mortality rate in Brazilian capitals with a high incidence of COVID-19. METHODS: We assessed the weekly mortality between epidemiological week 1 and 16 in 2020 and the corresponding period in 2019. We estimated the expected mortality at 95{\%} confidence interval by projecting the mortality in 2019 to the population in 2020, using data from the National Association of Civil Registrars (ARPEN-Brasil). RESULTS: In the five capitals with the highest incidence of COVID-19, we identified excess deaths during the pandemic. The age group above 60 years was severely affected, while 31{\%} of the excess deaths occurred in the age group of 20-59 years. There was a strong correlation (r = 0.94) between excess deaths and the number of deaths confirmed by epidemiological monitoring. The epidemiological surveillance captured only 52{\%} of all mortality associated with the COVID-19 pandemic in the cities examined. CONCLUSIONS: Considering the simplicity of the method and its low cost, we believe that the assessment of excess mortality associated with the COVID-19 pandemic should be used as a complementary tool for regular epidemiological surveillance.},
author = {Freitas, Andr{\'{e}} Ricardo Ribas and de Medeiros, Nicole Montenegro and Frutuoso, Livia Carla Vinhal and Beckedorff, Otto Albuquerque and de Martin, Lucas Mariscal Alves and Coelho, Marcela Montenegro de Medeiros and de Freitas, Giovanna Gimenez Souza and Lemos, Daniele Rocha Queir{\'{o}}z and Cavalcanti, Luciano Pamplona de G{\'{o}}es},
doi = {10.1590/0037-8682-0558-2020},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Freitas et al. - 2020 - Tracking excess deaths associated with the COVID-19 epidemic as an epidemiological surveillance strategy-prelimi.pdf:pdf},
issn = {16789849},
journal = {Revista da Sociedade Brasileira de Medicina Tropical},
keywords = {Epidemiological surveillance,Excess mortality COVID-19,Intelligence tool,Pandemic,Respiratory virus},
pages = {e20200558},
pmid = {33174964},
publisher = {NLM (Medline)},
title = {{Tracking excess deaths associated with the COVID-19 epidemic as an epidemiological surveillance strategy-preliminary results of the evaluation of six Brazilian capitals}},
url = {www.scielo.br/rsbmtIwww.rsbmt.org.br},
volume = {53},
year = {2020}
}
@article{Leon2020,
author = {Leon, David A and Shkolnikov, Vladimir M and Smeeth, Liam and Magnus, Per and Pechholdov{\'{a}}, Mark{\'{e}}ta and Jarvis, Christopher I},
issn = {0140-6736},
journal = {The Lancet},
number = {10234},
pages = {e81},
publisher = {Elsevier},
title = {{COVID-19: a need for real-time monitoring of weekly excess deaths}},
volume = {395},
year = {2020}
}
@article{Cobos2020,
abstract = {Background: Despite attempts to apply standard methods proven to work in high-income nations, nearly all civil registration and vital statistics (CRVS) systems in low- and middle-income countries are failing to achieve adequate levels of registration completeness or produce the high-quality vital statistics needed to support better health outcomes and monitor progress towards the 2030 Sustainable Development Goals. This suggests that, rather than simple technical issues, these countries are facing additional or different systemic challenges, including duplication of roles and responsibilities, inefficient methods of data collection, and a reluctance to change. Applying process management: Process management is a valuable tool that strengthens the production of vital statistics by providing a visualisation of data flow from start to finish. It helps identify gaps and bottlenecks in the process, allowing stakeholders to work collaboratively to find solutions and target interventions. As part of the Bloomberg Philanthropies Data for Health Initiative at the University of Melbourne, 16 countries were supported in mapping the varied processes required in registering a birth or death. Comparative analysis exposed several limitations in the design of CRVS systems that hinder their performance - from 'passive' systems, to overly complex and fragmented system design, through to poor collaboration and duplication of efforts. Conclusions: The experiences from Myanmar, Papua New Guinea and Rwanda reported in this paper illustrate the benefits of process management to improve CRVS. While these three countries are at different stages of system development, each uniquely benefited. Process management is a useful tool for all CRVS systems, from the most rudimentary to the most developed. It can strengthen CRVS systems and improve the quality and completeness of vital statistics, resulting in more robust, reliable and timely vital statistics for health planning and better monitoring of the 2030 Sustainable Development Goal agenda.},
author = {{Cobos Mu{\~{n}}oz}, Daniel and {De Savigny}, Don and Sorchik, Renee and Bo, Khin Sandar and Hart, John and Kwa, Viola and Ngomituje, Xavier and Richards, Nicola and Lopez, Alan D.},
doi = {10.1186/s12916-020-01522-z},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Cobos Mu{\~{n}}oz et al. - 2020 - Better data for better outcomes The importance of process mapping and management in CRVS systems(2).pdf:pdf},
issn = {17417015},
journal = {BMC Medicine},
keywords = {Cause of death,Civil registration and vital statistics,Mortality,Myanmar,Papua New Guinea,Process management,Process mapping,Rwanda,Sustainable development goals},
month = {mar},
number = {1},
pages = {67},
pmid = {32146901},
publisher = {BioMed Central Ltd.},
title = {{Better data for better outcomes: The importance of process mapping and management in CRVS systems}},
url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01522-z},
volume = {18},
year = {2020}
}
@misc{INEI2009,
author = {INEI},
file = {:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/Proyecciones{\_}poblacionales{\_}INEI.pdf:pdf},
publisher = {INEI},
title = {{PER{\'{U}}: Estimaciones y Proyecciones de Edad 1995-2025. Bolet{\'{i}}n de An{\'{a}}lisis Demogr{\'{a}}fico N{\textordmasculine} 37}},
url = {http://www.hsr.gob.pe/epidemiologia/pdf/interes{\_}5.pdf},
year = {2009}
}
@article{Bando2020,
author = {Bando, Rosangela and Galiani, Sebastian and Gertler, Paul},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bando, Galiani, Gertler - 2020 - The Effects of Noncontributory Pensions on Material and Subjective Well-Being.pdf:pdf},
journal = {Economic Development and cultural change},
number = {4},
title = {{The Effects of Noncontributory Pensions on Material and Subjective Well-Being}},
volume = {68},
year = {2020}
}
@article{Alvarez2021,
author = {{\'{A}}lvarez-Antonio, Carlos and Meza-S{\'{a}}nchez, Graciela and Calampa, Carlos and Casanova, Wilma and Carey, Cristiam and Alava, Freddy and Rodr{\'{i}}guez-Ferrucci, Hugo and Quispe, Antonio M},
doi = {10.1016/S2214-109X(21)00173-X},
issn = {2214109X},
journal = {The Lancet Global Health},
keywords = {Antonio M Quispe,Carlos {\'{A}}lvarez-Antonio,Graciela Meza-S{\'{a}}nchez,MEDLINE,NCBI,NIH,NLM,National Center for Biotechnology Information,National Institutes of Health,National Library of Medicine,PMC8133769,PubMed Abstract,doi:10.1016/S2214-109X(21)00173-X,pmid:34022148},
month = {may},
publisher = {Lancet Glob Health},
title = {{Seroprevalence of anti-SARS-CoV-2 antibodies in Iquitos, Peru in July and August, 2020: a population-based study}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2214109X2100173X},
year = {2021}
}
@techreport{Curtin1995,
author = {Curtin, Lester R and Klein, Richard J},
institution = {US Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics},
number = {6},
title = {{Direct standardization (age-adjusted death rates)}},
year = {1995}
}
@misc{NYTIMES2020,
author = {Wu, Jin and McCann, Allison and Katz, Josh and Peltier, Elian and {Deep Singh}, Karan},
title = {{412,000 Missing Deaths: Tracking the True Toll of the Coronavirus Outbreak - The New York Times}},
url = {https://www.nytimes.com/interactive/2020/04/21/world/coronavirus-missing-deaths.html},
urldate = {2020-12-03},
year = {2020}
}
@misc{RobertBCleveland1990,
author = {{Robert B Cleveland} and {William S. Cleveland} and {Jean E. McRae} and {Irma Terpenning}},
booktitle = {Journal of Official Statistics},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Robert B Cleveland et al. - 1990 - STL A Seasonal-Trend decomposition Procedure Based on Loess.pdf:pdf},
number = {1},
pages = {3--73},
title = {{STL: A Seasonal-Trend decomposition Procedure Based on Loess}},
url = {http://www.nniiem.ru/file/news/2016/stl-statistical-model.pdf},
volume = {6},
year = {1990}
}
@article{Benitez2020,
abstract = {Background: COVID-19 reached Latin-American countries slightly later than European countries, around February/March, allowing some emergency preparedness response in countries characterized by low health system capacities and socioeconomic disparities. Objective: This paper focuses on the first months of the pandemic in five Latin American countries: Brazil, Chile, Colombia, Ecuador and Peru. It analyses how the pre-pandemic context, and the government's responses to contain and mitigate the spread together with economic measures have affected the COVID-19 health outcomes. Methods: Extensive qualitative document analysis was conducted focused on publicly-available epidemiological data and federal and state/regional policy documents since the beginning of the pandemic. Results: The countries were quick to implement stringent COVID-19 measures and incrementally scaled up their health systems capacity, although tracing and tracking have been poor. All five countries have experienced a large number of cases and deaths due to COVID-19. The analysis on the excess deaths also shows that the impact in deaths is far higher than the official numbers reported to date for some countries. Conclusion: Despite the introduction of stringent measures of containment and mitigation, and the scale up of health system capacities, pre-pandemic conditions that characterize these countries (high informal employment, and social inequalities) have undermined the effectiveness of the countries' responses to the pandemic. The economic support measures put in place were found to be too timid for some countries and introduced too late in most of them. Additionally, the lack of a comprehensive strategy for testing and tracking has also contributed to the failure to contain the spread of the virus.},
author = {Ben{\'{i}}tez, Mar{\'{i}}a Alejandra and Velasco, Carolina and Sequeira, Ana Rita and Henr{\'{i}}quez, Josefa and Menezes, Flavio M. and Paolucci, Francesco},
doi = {10.1016/j.hlpt.2020.08.014},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ben{\'{i}}tez et al. - 2020 - Responses to COVID-19 in five Latin American countries.pdf:pdf},
issn = {22118845},
journal = {Health Policy and Technology},
keywords = {COVID-19,Chile,Colombia,Deaths,Ecuador,Effects: Brazil,Health,Latin America,Measures,Pandemic,Peru,Response},
month = {dec},
number = {4},
pages = {525--559},
pmid = {32874863},
publisher = {Elsevier B.V.},
title = {{Responses to COVID-19 in five Latin American countries}},
volume = {9},
year = {2020}
}
@techreport{INEI2019M,
author = {Inei},
file = {:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/ineimortalidad2019.pdf:pdf},
institution = {inei},
title = {{Peru: Natalidad, Mortalidad y Nuncialidad 2019 (Departamento, provincia y distrito)}},
year = {2020}
}
@article{Griffin2020,
abstract = {A month after the “urgent” review promised by England's health secretary, Public Health England (PHE) has announced new reporting definitions that bring England into line with Wales, Scotland, and Northern Ireland in how it records deaths from covid-19. However, experts warn that some deaths will still be missed by this new approach.

PHE's analysis showed that 88{\%} of deaths from covid-19 in England occurred within 28 days of a positive test result, while 96{\%} occurred within 60 days or had covid-19 on the death certificate. As a result, rather than counting anyone who had ever tested positive as a covid associated death, PHE will now use two definitions of death with covid-19 in England.

The first definition is death within 28 days of the first covid positive swab date. The second is death of someone with a laboratory confirmed positive covid-19 test who either died within 60 days of {\ldots}},
author = {Griffin, Shaun},
doi = {10.1136/bmj.m3220},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Griffin - 2020 - Covid-19 England comes into line with rest of UK on recording deaths.pdf:pdf},
issn = {17561833},
journal = {BMJ (Clinical research ed.)},
month = {aug},
pages = {m3220},
pmid = {32816745},
publisher = {NLM (Medline)},
title = {{Covid-19: England comes into line with rest of UK on recording deaths}},
url = {http://dx.doi.org/10.1136/bmj.m3220},
volume = {370},
year = {2020}
}
@misc{Roser2020,
author = {Roser, Max and Ritchie, Hannah and Ortiz-Ospina, Esteban and Hasell, Joe},
title = {{Excess mortality during the Coronavirus pandemic (COVID-19)}},
url = {https://ourworldindata.org/excess-mortality-covid},
urldate = {2020-12-03},
year = {2020}
}
@article{Vestergaard2020,
abstract = {{\textless}p{\textgreater}A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected  ≥ 65 year olds (91{\%} of all excess deaths), but also 45–64 (8{\%}) and 15–44 year olds (1{\%}). No excess mortality was observed in 0–14 year olds.{\textless}/p{\textgreater}},
author = {Vestergaard, Lasse S and Nielsen, Jens and Richter, Lukas and Schmid, Daniela and Bustos, Natalia and Braeye, Toon and Denissov, Gleb and Veideman, Tatjana and Luomala, Oskari and M{\"{o}}tt{\"{o}}nen, Teemu and Fouillet, Anne and Caserio-Sch{\"{o}}nemann, C{\'{e}}line and an der Heiden, Matthias and Uphoff, Helmut and Lytras, Theodore and Gkolfinopoulou, Kassiani and Paldy, Anna and Domegan, Lisa and O'Donnell, Joan and de' Donato, Francesca and Noccioli, Fiammetta and Hoffmann, Patrick and Velez, Telma and England, Kathleen and van Asten, Liselotte and White, Richard A and T{\o}nnessen, Ragnhild and da Silva, Susana P and Rodrigues, Ana P and Larrauri, Amparo and Delgado-Sanz, Concepci{\'{o}}n and Farah, Ahmed and Galanis, Ilias and Junker, Christoph and Perisa, Damir and Sinnathamby, Mary and Andrews, Nick and O'Doherty, Mark and Marquess, Diogo FP and Kennedy, Sharon and Olsen, Sonja J and Pebody, Richard and Krause, Tyra G and M{\o}lbak, K{\aa}re},
doi = {10.2807/1560-7917.ES.2020.25.26.2001214},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vestergaard et al. - 2020 - Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the(3).pdf:pdf},
issn = {1560-7917},
journal = {Eurosurveillance},
month = {jul},
number = {26},
pages = {2001214},
publisher = {European Centre for Disease Prevention and Control (ECDC)},
title = {{Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the EuroMOMO network, March to April 2020}},
url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.26.2001214},
volume = {25},
year = {2020}
}
@misc{Vincent2020,
author = {Vincent, Jean Louis and Taccone, Fabio S.},
booktitle = {The Lancet Respiratory Medicine},
doi = {10.1016/S2213-2600(20)30165-X},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vincent, Taccone - 2020 - Understanding pathways to death in patients with COVID-19.pdf:pdf},
issn = {22132619},
month = {may},
number = {5},
pages = {430--432},
pmid = {32272081},
publisher = {Lancet Publishing Group},
title = {{Understanding pathways to death in patients with COVID-19}},
url = {https://doi.org/10.1016/},
volume = {8},
year = {2020}
}
@article{Vega2013,
abstract = {Background Timely influenza surveillance is important to monitor influenza epidemics. Objectives (i) To calculate the epidemic threshold for influenza-like illness (ILI) and acute respiratory infections (ARI) in 19 countries, as well as the thresholds for different levels of intensity. (ii) To evaluate the performance of these thresholds. Methods The moving epidemic method (MEM) has been developed to determine the baseline influenza activity and an epidemic threshold. False alerts, detection lags and timeliness of the detection of epidemics were calculated. The performance was evaluated using a cross-validation procedure. Results The overall sensitivity of the MEM threshold was 71AE8{\%} and the specificity was 95AE5{\%}. The median of the timeliness was 1 week (range: 0-4AE5). Conclusions The method produced a robust and specific signal to detect influenza epidemics. The good balance between the sensitivity and specificity of the epidemic threshold to detect seasonal epidemics and avoid false alerts has advantages for public health purposes. This method may serve as standard to define the start of the annual influenza epidemic in countries in Europe. Please cite this paper as: Vega et al. (2013) Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method. Influenza and Other Respiratory Viruses 7(4), 546-558.},
author = {Vega, Tom{\'{a}} and {Eugenio Lozano}, Jose and Meerhoff, Tamara and Snacken, Ren{\'{e}} and Mott, Joshua and {Ortiz de Lejarazu}, Raul and Nunes, Baltazar and Vega, Tom{\'{a}}s},
doi = {10.1111/j.1750-2659.2012.00422.x},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vega et al. - 2013 - Influenza surveillance in Europe establishing epidemic thresholds by the Moving Epidemic Method(2).pdf:pdf},
journal = {Influenza and Other Respiratory Viruses},
keywords = {Epidemiology,influenza,modelling,surveillance},
number = {4},
pages = {546--558},
title = {{Influenza surveillance in Europe: establishing epidemic thresholds by the Moving Epidemic Method}},
url = {www.influenzajournal.com},
volume = {7},
year = {2013}
}
@article{Michelozzi2020,
abstract = {BACKGROUND: Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months. METHODS: Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex. RESULTS: COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16{\%} overlap was estimated in northern cities, with quotas decreasing by age, from 67{\%} in the 15-64 years old to 1{\%} only among subjects 85+ years old. CONCLUSIONS: An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.},
author = {Michelozzi, Paola and De'Donato, Francesca and Scortichini, Matteo and Pezzotti, Patrizio and Stafoggia, Massimo and {De Sario}, Manuela and Costa, Giuseppe and Noccioli, Fiammetta and Riccardo, Flavia and Bella, Antonino and Demaria, Moreno and Rossi, Pasqualino and Brusaferro, Silvio and Rezza, Giovanni and Davoli, Marina},
doi = {10.1186/s12889-020-09335-8},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Michelozzi et al. - 2020 - Temporal dynamics in total excess mortality and COVID-19 deaths in Italian cities.pdf:pdf},
issn = {14712458},
journal = {BMC public health},
keywords = {COVID-19-related death,Demographic factors,Mortality displacement,Surveillance system,Total excess mortality},
number = {1},
pages = {1238},
pmid = {32795276},
publisher = {BMC Public Health},
title = {{Temporal dynamics in total excess mortality and COVID-19 deaths in Italian cities}},
volume = {20},
year = {2020}
}
@article{Vargas2018,
abstract = {Peru has a low coverage of deaths with a cause of death (54{\%}) and a poor-quality registration of causes of death, as about 30{\%} of causes of death are classified as poorly-defined or not very useful for the formulation of public policies. In response to these problems, the Ministry of Health, together with other government agencies, with the support of the Bloomberg Philanthropies «Data for Health Initiative,» is implementing the National Death Registry Information System (SINADEF). The objective of this article is to describe the process of strengthening the mortality information system in Peru, focused on the implementation of SINADEF. The activities that have been carried out are described in the following areas: a) Management of the mortality information system, b) Process standardization, c) Use of information and communication technology, d) Coverage of deaths with medical certificate, e) Improvement of the quality of information, f) Development of studies, and g) Monitoring of processes. Since the implementation of SINADEF in August 2016 until July 2018, 28,407 users of the SINADEF application have been created and a total of 122,411 deaths have been registered. The quality of data recording, including the cause of death, has been improved, while low coverage of deaths with a cause of death still persists.},
author = {Vargas-Herrera, Javier and {Pardo Ruiz}, Karim and {Garro Nu{\~{n}}ez}, Gladys and {Miki Ohno}, Janet and P{\'{e}}rez-Lu, Jos{\'{e}} Enrique and {Valdez Huarcaya}, William and Clapham, Benjamin and Cortez-Escalante, Juan},
doi = {10.17843/rpmesp.2018.353.3913},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vargas-Herrera et al. - 2018 - Resultados preliminares del fortalecimiento del sistema inform{\'{a}}tico nacional de defunciones(2).pdf:pdf},
issn = {1726-4642},
journal = {Revista Peruana de Medicina Experimental y Salud P{\'{u}}blica},
keywords = {Causes of death,Death certificate,Health information systems,Mortality,Vital statistics (source: MeSH NLM)},
month = {oct},
number = {3},
pages = {505},
publisher = {Instituto Nacional de Salud},
title = {{Resultados preliminares del fortalecimiento del sistema inform{\'{a}}tico nacional de defunciones}},
url = {https://rpmesp.ins.gob.pe/index.php/rpmesp/article/view/3913},
volume = {35},
year = {2018}
}
@article{Dicker2018,
abstract = {Background: Assessments of age-specifc mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Afairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods: The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specifc mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in diferent components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings: Globally, 18{\textperiodcentered}7{\%} (95{\%} uncertainty interval 18{\textperiodcentered}4-19{\textperiodcentered}0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58{\textperiodcentered}8{\%} (58{\textperiodcentered}2-59{\textperiodcentered}3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48{\textperiodcentered}1 years (46{\textperiodcentered}5-49{\textperiodcentered}6) to 70{\textperiodcentered}5 years (70{\textperiodcentered}1-70{\textperiodcentered}8) for men and from 52{\textperiodcentered}9 years (51{\textperiodcentered}7-54{\textperiodcentered}0) to 75{\textperiodcentered}6 years (75{\textperiodcentered}3-75{\textperiodcentered}9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49{\textperiodcentered}1 years (46{\textperiodcentered}5-51{\textperiodcentered}7) for men in the Central African Republic to 87{\textperiodcentered}6 years (86{\textperiodcentered}9-88{\textperiodcentered}1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216{\textperiodcentered}0 deaths (196{\textperiodcentered}3-238{\textperiodcentered}1) per 1000 livebirths in 1950 to 38{\textperiodcentered}9 deaths (35{\textperiodcentered}6-42{\textperiodcentered}83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5{\textperiodcentered}4 million (5{\textperiodcentered}2-5{\textperiodcentered}6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation: This analysis of age-sex-specifc mortality shows that there are remarkably complex patterns in population mortality across countries. The fndings of this study highlight global successes, such as the large decline in under-5 mortality, which refects signifcant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing.},
author = {Dicker, Daniel and Nguyen, Grant and Abate, Degu and Abate, Kalkidan Hassen and Abay, Solomon M. and Abbafati, Cristiana and Abbasi, Nooshin and Abbastabar, Hedayat and Abd-Allah, Foad and Abdela, Jemal and Abdelalim, Ahmed and Abdel-Rahman, Omar and Abdi, Alireza and Abdollahpour, Ibrahim and Abdulkader, Rizwan Suliankatchi and Abdurahman, Ahmed Abdulahi and Abebe, Haftom Temesgen and Abebe, Molla and Abebe, Zegeye and Abebo, Teshome Abuka and Aboyans, Victor and Abraha, Haftom Niguse and Abrham, Aklilu Roba and Abu-Raddad, Laith Jamal and Abu-Rmeileh, Niveen M.E. and Accrombessi, Manfred Mario Kokou and Acharya, Pawan and Adebayo, Oladimeji M. and Adedeji, Isaac Akinkunmi and Adedoyin, Rufus Adesoji and Adekanmbi, Victor and Adetokunboh, Olatunji O. and Adhena, Beyene Meressa and Adhikari, Tara Ballav and Adib, Mina G. and Adou, Ars{\~{A}}¨ne Kouablan and Adsuar, Jose C. and Afarideh, Mohsen and Afshin, Ashkan and Agarwal, Gina and Aggarwal, Rakesh and Aghayan, Sargis Aghasi and Agrawal, Sutapa and Agrawal, Anurag and Ahmadi, Mehdi and Ahmadi, Alireza and Ahmadieh, Hamid and Ahmed, Mohamed Lemine Cheikh Brahim and Ahmed, Sayem and Ahmed, Muktar Beshir and Aichour, Amani Nidhal and Aichour, Ibtihel and Aichour, Miloud Taki Eddine and Akanda, Ali S. and Akbari, Mohammad Esmaeil and Akibu, Mohammed and Akinyemi, Rufus Olusola and Akinyemiju, Tomi and Akseer, Nadia and Alahdab, Fares and Al-Aly, Ziyad and Alam, Khurshid and Alebel, Animut and Aleman, Alicia V. and Alene, Kefyalew Addis and Al-Eyadhy, Ayman and Ali, Raghib and Alijanzadeh, Mehran and Alizadeh-Navaei, Reza and Aljunid, Syed Mohamed and Alkerwi, Ala{\^{a}}€™A and Alla, Fran{\~{A}}{\S}ois and Allebeck, Peter and Allen, Christine A. and Alonso, Jordi and Al-Raddadi, Rajaa M. and Alsharif, Ubai and Altirkawi, Khalid and Alvis-Guzman, Nelson and Amare, Azmeraw T. and Amini, Erfan and Ammar, Walid and Amoako, Yaw Ampem and Anber, Nahla Hamed and Andrei, Catalina Liliana and Androudi, Sofia and Animut, Megbaru Debalkie and Anjomshoa, Mina and Anlay, Degefaye Zelalem and Ansari, Hossein and Ansariadi, Ansariadi and Ansha, Mustafa Geleto and Antonio, Carl Abelardo T. and Appiah, Seth Christopher Yaw and Aremu, Olatunde and Areri, Habtamu Abera and {\~{A}}„rnl{\~{A}}{\P}v, Johan and Arora, Megha and Artaman, Al and Aryal, Krishna K. and Asadi-Lari, Mohsen and Asayesh, Hamid and Asfaw, Ephrem Tsegay and Asgedom, Solomon Weldegebreal and Assadi, Reza and Ataro, Zerihun and Atey, Tesfay Mehari Mehari and Athari, Seyyed Shamsadin and Atique, Suleman and Atre, Sachin R. and Atteraya, Madhu Sudhan and Attia, Engi F. and Ausloos, Marcel and Avila-Burgos, Leticia and Avokpaho, Euripide F.G.A. and Awasthi, Ashish and Awuah, Baffour and {Ayala Quintanilla}, Beatriz Paulina and Ayele, Henok Tadesse and Ayele, Yohanes and Ayer, Rakesh and Ayuk, Tambe B. and Azzopardi, Peter S. and Azzopardi-Muscat, Natasha and Badali, Hamid and Badawi, Alaa and Balakrishnan, Kalpana and Bali, Ayele Geleto and Banach, Maciej and Banstola, Amrit and Barac, Aleksandra and Barboza, Miguel A. and Barquera, Simon and Barrero, Lope H. and Basaleem, Huda and Bassat, Quique and Basu, Arindam and Basu, Sanjay and Baune, Bernhard T. and Bazargan-Hejazi, Shahrzad and Bedi, Neeraj and Beghi, Ettore and Behzadifar, Masoud and Behzadifar, Meysam and B{\~{A}}{\textcopyright}jot, Yannick and Bekele, Bayu Begashaw and Belachew, Abate Bekele and Belay, Aregawi Gebreyesus and Belay, Ezra and Belay, Saba Abraham and Belay, Yihalem Abebe and Bell, Michelle L. and Bello, Aminu K. and Bennett, Derrick A. and Bensenor, Isabela M. and Berhane, Adugnaw and Berman, Adam E. and Bernabe, Eduardo and Bernstein, Robert S. and Bertolacci, Gregory J. and Beuran, Mircea and Beyranvand, Tina and Bhala, Neeraj and Bhatia, Eesh and Bhatt, Samir and Bhattarai, Suraj and Bhaumik, Soumyadeeep and Bhutta, Zulfiqar A. and Biadgo, Belete and Bijani, Ali and Bikbov, Boris and Bililign, Nigus and {Bin Sayeed}, Muhammad Shahdaat and Birlik, Sait Mentes and Birungi, Charles and Bisanzio, Donal and Biswas, Tuhin and Bj{\~{A}}¸rge, Tone and Bleyer, Archie and {Bora Basara}, Berrak and Bose, Dipan and Bosetti, Cristina and Boufous, Soufiane and Bourne, Rupert and Brady, Oliver J. and Bragazzi, Nicola Luigi and Brant, Luisa C. and Brazinova, Alexandra and Breitborde, Nicholas J.K. and Brenner, Hermann and Britton, Gabrielle and Brugha, Traolach and Burke, Kristin E. and Busse, Reinhard and Butt, Zahid A. and Cahuana-Hurtado, Lucero and Callender, Charlton S.K.H. and Campos-Nonato, Ismael R. and {Campuzano Rincon}, Julio Cesar and Cano, Jorge and Car, Mate and C{\~{A}}¡rdenas, Rosario and Carreras, Giulia and Carrero, Juan J. and Carter, Austin and Carvalho, F{\~{A}}{\textcopyright}lix and Casta{\~{A}}±eda-Orjuela, Carlos A. and {Castillo Rivas}, Jacqueline and Castro, Franz and Catal{\~{A}}¡-L{\~{A}}³pez, Ferr{\~{A}}¡n and {\~{A}}‡avlin, Alanur and Cerin, Ester and Chaiah, Yazan and Champs, Ana Paula and Chang, Hsing Yi and Chang, Jung Chen and Chattopadhyay, Aparajita and Chaturvedi, Pankaj and Chen, Wanqing and Chiang, Peggy Pei Chia and Chimed-Ochir, Odgerel and Chin, Ken Lee and Chisumpa, Vesper Hichilombwe and Chitheer, Abdulaal and Choi, Jee Young J. and Christensen, Hanne and Christopher, Devasahayam J. and Chung, Sheng Chia and Cicuttini, Flavia M. and Ciobanu, Liliana G. and Cirillo, Massimo and Claro, Rafael M. and Cohen, Aaron J. and Collado-Mateo, Daniel and Constantin, Maria Magdalena and Conti, Sara and Cooper, Cyrus and Cooper, Leslie Trumbull and Cortesi, Paolo Angelo and Cortinovis, Monica and Cousin, Ewerton and Criqui, Michael H. and Cromwell, Elizabeth A. and Crowe, Christopher Stephen and Crump, John A. and Cucu, Alexandra and Cunningham, Matthew and Daba, Alemneh Kabeta and Dachew, Berihun Assefa and Dadi, Abel Fekadu and Dandona, Lalit and Dandona, Rakhi and Dang, Anh Kim and Dargan, Paul I. and Daryani, Ahmad and Das, Siddharth K. and {Das Gupta}, Rajat and {Das Neves}, Jos{\~{A}}{\textcopyright} and Dasa, Tamirat Tesfaye and Dash, Aditya Prasad and {Davis Weaver}, Nicole and Davitoiu, Dragos Virgil and Davletov, Kairat and Dayama, Anand and de Courten, Barbora and {De la Hoz}, Fernando Pio and De leo, Diego and {De Neve}, Jan Walter and Degefa, Meaza Girma and Degenhardt, Louisa and Degfie, Tizta T. and Deiparine, Selina and Dellavalle, Robert P. and Demoz, Gebre Teklemariam and Demtsu, Balem Betsu and Denova-Guti{\~{A}}{\textcopyright}rrez, Edgar and Deribe, Kebede and Dervenis, Nikolaos and {Des Jarlais}, Don C. and Dessie, Getenet Ayalew and Dey, Subhojit and Dharmaratne, Samath Dhamminda and Dhimal, Meghnath and Ding, Eric L. and Djalalinia, Shirin and Doku, David Teye and Dolan, Kate A. and Donnelly, Christl A. and Dorsey, E. 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doi = {10.1016/S0140-6736(18)31891-9},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dicker et al. - 2018 - Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017 A systematic analysis fo.pdf:pdf},
issn = {1474547X},
journal = {The Lancet},
number = {10159},
pages = {1684--1735},
pmid = {30496102},
title = {{Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: A systematic analysis for the Global Burden of Disease Study 2017}},
volume = {392},
year = {2018}
}
@misc{Economist2020,
author = {{The Economist}},
title = {{Covid-19 data - Tracking covid-19 excess deaths across countries}},
url = {https://www.economist.com/graphic-detail/2020/07/15/tracking-covid-19-excess-deaths-across-countries},
urldate = {2020-12-03},
year = {2020}
}
@article{Cevallos2020,
author = {Cevallos-Valdiviezo, Holger and Vergara-Montesdeoca, Allan and Zambrano-Zambrano, Gema},
doi = {10.1016/j.ijid.2020.12.045},
issn = {12019712},
journal = {International Journal of Infectious Diseases},
month = {dec},
publisher = {Elsevier},
title = {{Measuring the impact of the COVID-19 outbreak in Ecuador using preliminary estimates of excess mortality, March 17–October 22, 2020}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1201971220325674},
year = {2020}
}
@misc{Ineiseries2020,
author = {INEI},
title = {{Series Nacionales}},
url = {http://webapp.inei.gob.pe:8080/sirtod-series/},
urldate = {2020-12-08},
year = {2020}
}
@article{Adair2020,
abstract = {Background: Globally, an estimated two-thirds of all deaths occur in the community, the majority of which are not attended by a physician and remain unregistered. Identifying and registering these deaths in civil registration and vital statistics (CRVS) systems, and ascertaining the cause of death, is thus a critical challenge to ensure that policy benefits from reliable evidence on mortality levels and patterns in populations. In contrast to traditional processes for registration, death notification can be faster and more efficient at informing responsible government agencies about the event and at triggering a verbal autopsy for ascertaining cause of death. Thus, innovative approaches to death notification, tailored to suit the setting, can improve the availability and quality of information on community deaths in CRVS systems. Improving the notification of community deaths: Here, we present case studies in four countries (Bangladesh, Colombia, Myanmar and Papua New Guinea) that were part of the initial phases of the Bloomberg Data for Health Initiative at the University of Melbourne, each of which faces unique challenges to community death registration. The approaches taken promote improved notification of community deaths through a combination of interventions, including integration with the health sector, using various notifying agents and methods, and the application of information and communication technologies. One key factor for success has been the smoothing of processes linking notification, registration and initiation of a verbal autopsy interview. The processes implemented champion more active notification systems in relation to the passive systems commonly in place in these countries. Conclusions: The case studies demonstrate the significant potential for improving death reporting through the implementation of notification practices tailored to a country's specific circumstances, including geography, cultural factors, structure of the existing CRVS system, and available human, information and communication technology resources. Strategic deployment of some, or all, of these innovations can result in rapid improvements to death notification systems and should be trialled in other settings.},
author = {Adair, Tim and Rajasekhar, Megha and Bo, Khin Sandar and Hart, John and Kwa, Viola and Mukut, Md Ashfaqul Amin and Reeve, Matthew and Richards, Nicola and Ronderos-Torres, Margarita and {De Savigny}, Don and Mu{\~{n}}oz, Daniel Cobos and Lopez, Alan D.},
doi = {10.1186/s12916-020-01524-x},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Adair et al. - 2020 - Where there is no hospital Improving the notification of community deaths(2).pdf:pdf},
issn = {17417015},
journal = {BMC Medicine},
keywords = {Cause of death,Civil registration and vital statistics,Community,Death notification,Death registration,Innovation,Mortality,Verbal autopsy},
month = {mar},
number = {1},
pages = {65},
pmid = {32146904},
publisher = {BioMed Central Ltd.},
title = {{Where there is no hospital: Improving the notification of community deaths}},
url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01524-x},
volume = {18},
year = {2020}
}
@techreport{PCM2021,
address = {Lima},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {PCM},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/PCM - 2021 - Informe Final del Grupo de Trabajo T{\'{e}}cnico para proponer los criterios para actualizar la cifra de fallecidos por COVID.pdf:pdf;:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/Informe final del grupo de trabajo técnico con cifra de fallecidos por la COVID-19.pdf.pdf:pdf},
institution = {PCM},
isbn = {9788578110796},
issn = {1098-6596},
pmid = {25246403},
title = {{Informe Final del Grupo de Trabajo T{\'{e}}cnico para proponer los criterios para actualizar la cifra de fallecidos por COVID}},
year = {2021}
}
@article{Calderon2020,
abstract = {Although lockdown measures to stop COVID-19 have direct effects on disease transmission, their impact on violent and accidental deaths remains unknown. Our study aims to assess the early impact of COVID-19 lockdown on violent and accidental deaths in Peru. Based on data from the Peruvian National Death Information System, an interrupted time series analysis was performed to assess the immediate impact and change in the trend of COVID-19 lockdown on external causes of death including homicide, suicide, and traffic accidents. The analysis was stratified by sex and the time unit was every 15 days. All forms of deaths examined presented a sudden drop after the lockdown. The biggest drop was in deaths related to traffic accidents, with a reduction of 12.22 deaths per million men per month (95{\%} CI: −14.45, −9.98) and 3.55 deaths per million women per month (95{\%} CI:-4.81, −2.30). Homicide and suicide presented similar level drop in women, while the homicide reduction was 2.5 the size of the suicide reduction in men. The slope in homicide in men during the lock-down period increased by 6.66 deaths per million men per year (95{\%} CI:3.18, 10.15). External deaths presented a sudden drop after the lockdown was implemented and an increase in homicide in men was observed. Falls in mobility have a natural impact on traffic accidents, however, the patterns for suicide and homicide are less intuitive and reveal important characteristics of these events, although we expect all of these changes to be transient.},
author = {Calderon-Anyosa, Renzo J.C. and Kaufman, Jay S.},
doi = {10.1016/j.ypmed.2020.106331},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Calderon-Anyosa, Kaufman - 2021 - Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru.pdf:pdf},
issn = {10960260},
journal = {Preventive Medicine},
keywords = {COVID-19,External deaths,Lockdown,Public policy,Suicides},
month = {feb},
pages = {106331},
pmid = {33232687},
publisher = {Academic Press Inc.},
title = {{Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru}},
volume = {143},
year = {2021}
}
@article{Calderon2021,
abstract = {Although lockdown measures to stop COVID-19 have direct effects on disease transmission, their impact on violent and accidental deaths remains unknown. Our study aims to assess the early impact of COVID-19 lockdown on violent and accidental deaths in Peru. Based on data from the Peruvian National Death Information System, an interrupted time series analysis was performed to assess the immediate impact and change in the trend of COVID-19 lockdown on external causes of death including homicide, suicide, and traffic accidents. The analysis was stratified by sex and the time unit was every 15 days. All forms of deaths examined presented a sudden drop after the lockdown. The biggest drop was in deaths related to traffic accidents, with a reduction of 12.22 deaths per million men per month (95{\%} CI: −14.45, −9.98) and 3.55 deaths per million women per month (95{\%} CI:-4.81, −2.30). Homicide and suicide presented similar level drop in women, while the homicide reduction was 2.5 the size of the suicide reduction in men. The slope in homicide in men during the lock-down period increased by 6.66 deaths per million men per year (95{\%} CI:3.18, 10.15). External deaths presented a sudden drop after the lockdown was implemented and an increase in homicide in men was observed. Falls in mobility have a natural impact on traffic accidents, however, the patterns for suicide and homicide are less intuitive and reveal important characteristics of these events, although we expect all of these changes to be transient.},
author = {Calderon-Anyosa, Renzo J.C. and Kaufman, Jay S.},
doi = {10.1016/j.ypmed.2020.106331},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Calderon-Anyosa, Kaufman - 2021 - Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru(2).pdf:pdf},
issn = {10960260},
journal = {Preventive Medicine},
keywords = {COVID-19,External deaths,Lockdown,Public policy,Suicides},
month = {feb},
pmid = {33232687},
publisher = {Academic Press Inc.},
title = {{Impact of COVID-19 lockdown policy on homicide, suicide, and motor vehicle deaths in Peru}},
volume = {143},
year = {2021}
}
@article{Vandoros2020,
abstract = {The Covid-19 pandemic has claimed many lives in the UK and globally. The objective of this paper is to study whether the number of deaths not registered as Covid-19-related has increased compared to what would have been expected in the absence of the pandemic. Reasons behind this might include Covid-19 underreporting, avoiding visits to hospitals or GPs, and the effects of the lockdown. I used weekly ONS data on the number of deaths in England and Wales that did not officially involve Covid-19 over the period 2015–2020. Simply observing trends is not sufficient as spikes in deaths may occasionally occur. I thus followed a difference-in-differences econometric approach to study whether there was a relative increase in deaths not registered as Covid-19-related during the pandemic, compared to a control. Results suggest that there were an additional 968 weekly deaths that officially did not involve Covid-19, compared to what would have otherwise been expected. It is possible that some people are dying from Covid-19 without being diagnosed, and/or that there are excess deaths due to other causes as a result of the pandemic. Analysing the cause of death for any excess non-covid-19 deaths will shed light upon the reasons for the increase in such deaths and will help design appropriate policy responses to save lives.},
author = {Vandoros, Sotiris},
doi = {10.1016/j.socscimed.2020.113101},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vandoros - 2020 - Excess mortality during the Covid-19 pandemic Early evidence from England and Wales(2).pdf:pdf},
issn = {18735347},
journal = {Social Science and Medicine},
keywords = {Covid-19,Excess mortality,Lockdown,Spillover effects,Underreporting},
month = {aug},
pages = {113101},
pmid = {32521411},
publisher = {Elsevier Ltd},
title = {{Excess mortality during the Covid-19 pandemic: Early evidence from England and Wales}},
volume = {258},
year = {2020}
}
@techreport{Inei2020,
abstract = {La informaci{\'{o}}n contenida en este documento puede ser reproducida total o parcialmente, siempre y cuando se mencione la fuente de origen: Instituto Nacional de Estad{\'{i}}stica e Inform{\'{a}}tica.},
author = {INEI},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/INEI - 2020 - PER{\'{U}} Estimaciones y Proyecciones de Poblaci{\'{o}}n por Departamento, Provincia y Distrito, 2018-2020(2).pdf:pdf},
title = {{PER{\'{U}}: Estimaciones y Proyecciones de Poblaci{\'{o}}n por Departamento, Provincia y Distrito, 2018-2020}},
year = {2020}
}
@techreport{CRVS2018,
address = {Melbourne, Australia},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - 2018 - Peru Implementation Working Group. Peru An exceptional example of CRVS system advancement. CRVS country reports.pdf:pdf},
institution = {The University of Melbourne; Civil Registration and Vital Statistics Improvement, Bloomberg Philanthropies Data for Health Initiative},
number = {March},
title = {{Peru Implementation Working Group. Peru: An exceptional example of CRVS system advancement. CRVS country reports.}},
year = {2018}
}
@article{Adair2018,
author = {Adair, Tim and Lopez, Alan D.},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Adair, Lopez - 2018 - Estimating the completeness of death registration An empirical method.pdf:pdf},
isbn = {1111111111},
journal = {PLoS ONE},
number = {5},
pages = {e0197047},
title = {{Estimating the completeness of death registration: An empirical method.}},
volume = {13},
year = {2018}
}
@article{Wang2017,
abstract = {Background: Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. Methods: We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0.5{\%} and where VR systems were less than 65{\%} complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. Findings: Completeness in the registration of deaths increased from 28{\%} in 1970 to a peak of 45{\%} in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates - a measure of relative inequality - increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86.9 years (95{\%} UI 86.7-87.2), and for men in Singapore, at 81.3 years (78.8-83.7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. Interpretation Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either increased this advantage or rapidly decreased the deficit from expected levels, could provide insight into the means to accelerate progress in nations where progress has stalled.},
author = {Wang, Haidong and Abajobir, Amanuel Alemu and Abate, Kalkidan Hassen and Abbafati, Cristiana and Abbas, Kaja M. and Abd-Allah, Foad and Abera, Semaw Ferede and Abraha, Haftom Niguse and Abu-Raddad, Laith J. and Abu-Rmeileh, Niveen M.E. and Adedeji, Isaac Akinkunmi and Adedoyin, Rufus Adesoji and Adetifa, Ifedayo Morayo O. and Adetokunboh, Olatunji and Afshin, Ashkan and Aggarwal, Rakesh and Agrawal, Anurag and Agrawal, Sutapa and {Ahmad Kiadaliri}, Aliasghar and Ahmed, Muktar Beshir and Aichour, Amani Nidhal and Aichour, Ibthiel and Aichour, Miloud Taki Eddine and Aiyar, Sneha and Akanda, Ali Shafqat and Akinyemiju, Tomi F. and Akseer, Nadia and Al-Eyadhy, Ayman and {Al Lami}, Faris Hasan and Alabed, Samer and Alahdab, Fares and Al-Aly, Ziyad and Alam, Khurshid and Alam, Noore and Alasfoor, Deena and Aldridge, Robert William and Alene, Kefyalew Addis and Alhabib, Samia and Ali, Raghib and Alizadeh-Navaei, Reza and Aljunid, Syed M. and Alkaabi, Juma M. and Alkerwi, Ala'a and Alla, Fran{\c{c}}ois and Allam, Shalini D. and Allebeck, Peter and Al-Raddadi, Rajaa and Alsharif, Ubai and Altirkawi, Khalid A. and Martin, Elena Alvarez and Alvis-Guzman, Nelson and Amare, Azmeraw T. and Ameh, Emmanuel A. and Amini, Erfan and Ammar, Walid and Amoako, Yaw Ampem and Anber, Nahla and Andrei, Catalina Liliana and Androudi, Sofia and Ansari, Hossein and Ansha, Mustafa Geleto and Antonio, Carl Abelardo T. and Anwari, Palwasha and {\"{A}}rnl{\"{o}}v, Johan and Arora, Megha and Artaman, Al and Aryal, Krishna Kumar and Asayesh, Hamid and Asgedom, Solomon Weldegebreal and Asghar, Rana Jawad and Assadi, Reza and Atey, Tesfay Mehari and Atre, Sachin R. and Avila-Burgos, Leticia and Avokpaho, Euripide Frinel G.Arthur and Awasthi, Ashish and {Ayala Quintanilla}, Beatriz Paulina and Babalola, Tesleem Kayode and Bacha, Umar and Badawi, Alaa and Balakrishnan, Kalpana and Balalla, Shivanthi and Barac, Aleksandra and Barber, Ryan M. and Barboza, Miguel A. and Barker-Collo, Suzanne L. and B{\"{a}}rnighausen, Till and Barquera, Simon and Barregard, Lars and Barrero, Lope H. and Baune, Bernhard T. and Bazargan-Hejazi, Shahrzad and Bedi, Neeraj and Beghi, Ettore and B{\'{e}}jot, Yannick and Bekele, Bayu Begashaw and Bell, Michelle L. and Bello, Aminu K. and Bennett, Derrick A. and Bennett, James R. and Bensenor, Isabela M. and Benson, Jennifer and Berhane, Adugnaw and Berhe, Derbew Fikadu and Bernab{\'{e}}, Eduardo and Beuran, Mircea and Beyene, Addisu Shunu and Bhala, Neeraj and Bhansali, Anil and Bhaumik, Soumyadeep and Bhutta, Zulfiqar A. and Bikbov, Boris and Birungi, Charles and Biryukov, Stan and Bisanzio, Donal and Bizuayehu, Habtamu Mellie and Bjerregaard, Peter and Blosser, Christopher D. and Boneya, Dube Jara and Boufous, Soufiane and Bourne, Rupert R.A. and Brazinova, Alexandra and Breitborde, Nicholas J.K. and Brenner, Hermann and Brugha, Traolach S. and Bukhman, Gene and Bulto, Lemma Negesa Bulto and Bumgarner, Blair Randal and Burch, Michael and Butt, Zahid A. and Cahill, Leah E. and Cahuana-Hurtado, Lucero and Campos-Nonato, Ismael Ricardo and Car, Josip and Car, Mate and C{\'{a}}rdenas, Rosario and Carpenter, David O. and Carrero, Juan Jesus and Carter, Austin and Casta{\~{n}}eda-Orjuela, Carlos A. and {Castillo Rivas}, Jacqueline and Castro, Franz F. and Castro, Ruben Estanislao and Catal{\'{a}}-L{\'{o}}pez, Ferr{\'{a}}n and Chen, Honglei and Chiang, Peggy Pei Chia and Chibalabala, Mirriam and Chisumpa, Vesper Hichilombwe and Chitheer, Abdulaal A. and Choi, Jee Young Jasmine and Christensen, Hanne and Christopher, Devasahayam Jesudas and Ciobanu, Liliana G. and Cirillo, Massimo and Cohen, Aaron J. and Colquhoun, Samantha M. and Coresh, Josef and Criqui, Michael H. and Cromwell, Elizabeth A. and Crump, John A. and Dandona, Lalit and Dandona, Rakhi and Dargan, Paul I. and {Das Neves}, Jos{\'{e}} and Davey, Gail and Davitoiu, Dragos V. and Davletov, Kairat and {De Courten}, Barbora and {De Leo}, Diego and Degenhardt, Louisa and Deiparine, Selina and Dellavalle, Robert P. and Deribe, Kebede and Deribew, Amare and {Des Jarlais}, Don C. and Dey, Subhojit and Dharmaratne, Samath D. and Dherani, Mukesh K. and Diaz-Torn{\'{e}}, Cesar and Ding, Eric L. and Dixit, Priyanka and Djalalinia, Shirin and Do, Huyen Phuc and Doku, David Teye and Donnelly, Christl Ann and {Dos Santos}, Kadine Priscila Bender and Douwes-Schultz, Dirk and Driscoll, Tim R. and Duan, Leilei and Dubey, Manisha and Duncan, Bruce Bartholow and Dwivedi, Laxmi Kant and Ebrahimi, Hedyeh and {El Bcheraoui}, Charbel and Ellingsen, Christian Lycke and Enayati, Ahmadali and Endries, Aman Yesuf and Ermakov, Sergey Petrovich and Eshetie, Setegn and Eshrati, Babak and Eskandarieh, Sharareh and Esteghamati, Alireza and Estep, Kara and Fanuel, Fanuel Belayneh Bekele and Faro, Andr{\'{e}} and Farvid, Maryam S. and Farzadfar, Farshad and Feigin, Valery L. and Fereshtehnejad, Seyed Mohammad and Fernandes, Jefferson G. and Fernandes, Jo{\~{a}}o C. and Feyissa, Tesfaye Regassa and Filip, Irina and Fischer, Florian and Foigt, Nataliya and Foreman, Kyle J. and Frank, Tahvi and Franklin, Richard C. and Fraser, Maya and Friedman, Joseph and Frostad, Joseph J. and Fullman, Nancy and F{\"{u}}rst, Thomas and Furtado, Joao M. and Futran, Neal D. and Gakidou, Emmanuela and Gambashidze, Ketevan and Gamkrelidze, Amiran and Gankp{\'{e}}, Fortun{\'{e}} Gb{\`{e}}toho and Garcia-Basteiro, Alberto L. and Gebregergs, Gebremedhin Berhe and Gebrehiwot, Tsegaye Tewelde and Gebrekidan, Kahsu Gebrekirstos and Gebremichael, Mengistu Welday and Gelaye, Amha Admasie and Geleijnse, Johanna M. and Gemechu, Bikila Lencha and Gemechu, Kasiye Shiferaw and Genova-Maleras, Ricard and Gesesew, Hailay Abrha and Gething, Peter W. and Gibney, Katherine B. and Gill, Paramjit Singh and Gillum, Richard F. and Giref, Ababi Zergaw and Girma, Bedilu Weji and Giussani, Giorgia and Goenka, Shifalika and Gomez, Beatriz and Gona, Philimon N. and Gopalani, Sameer Vali and Goulart, Alessandra Carvalho and Graetz, Nicholas and Gugnani, Harish Chander and Gupta, Prakash C. and Gupta, Rahul and Gupta, Rajeev and Gupta, Tanush and Gupta, Vipin and Haagsma, Juanita A. and Hafezi-Nejad, Nima and {Haghparast Bidgoli}, Hassan and Hakuzimana, Alex and Halasa, Yara A. and Hamadeh, Randah Ribhi and Hambisa, Mitiku Teshome and Hamidi, Samer and Hammami, Mouhanad and Hancock, Jamie and Handal, Alexis J. and Hankey, Graeme J. and Hao, Yuantao and Harb, Hilda L. and Hareri, Habtamu Abera and Harikrishnan, Sivadasanpillai and Haro, Josep Maria and Hassanvand, Mohammad Sadegh and Havmoeller, Rasmus and Hay, Roderick J. and Hay, Simon I. and He, Fei and Heredia-Pi, Ileana Beatriz and Herteliu, Claudiu and Hilawe, Esayas Haregot and Hoek, Hans W. and Horita, Nobuyuki and Hosgood, H. Dean and Hostiuc, Sorin and Hotez, Peter J. and Hoy, Damian G. and Hsairi, Mohamed and Htet, Aung Soe and Hu, Guoqing and Huang, Hsiang and Huang, John J. and Iburg, Kim Moesgaard and Igumbor, Ehimario Uche and Ileanu, Bogdan Vasile and Inoue, Manami and Irenso, Asnake Ararsa and Irvine, Caleb M.S. and Islam, Nazrul and Jacobsen, Kathryn H. and Jaenisch, Thomas and Jahanmehr, Nader and Jakovljevic, Mihajlo B. and Javanbakht, Mehdi and Jayatilleke, Achala Upendra and Jeemon, Panniyammakal and Jensen, Paul N. and Jha, Vivekanand and Jin, Ye and John, Denny and John, Oommen and Johnson, Sarah Charlotte and Jonas, Jost B. and J{\"{u}}risson, Mikk and Kabir, Zubair and Kadel, Rajendra and Kahsay, Amaha and Kalkonde, Yogeshwar and Kamal, Ritul and Kan, Haidong and Karch, Andr{\'{e}} and Karema, Corine Kakizi and Karimi, Seyed M. and Karthikeyan, Ganesan and Kasaeian, Amir and Kassaw, Nigussie Assefa and Kassebaum, Nicholas J. and Kastor, Anshul and Katikireddi, Srinivasa Vittal and Kaul, Anil and Kawakami, Norito and Kazanjan, Konstantin and Keiyoro, Peter Njenga and Kelbore, Sefonias Getachew and Kemp, Andrew Haddon and Kengne, Andre Pascal and Keren, Andre and Kereselidze, Maia and Kesavachandran, Chandrasekharan Nair and Ketema, Ezra Belay and Khader, Yousef Saleh and Khalil, Ibrahim A. and Khan, Ejaz Ahmad and Khan, Gulfaraz and Khang, Young Ho and Khera, Sahil and Khoja, Abdullah Tawfih Abdullah and Khosravi, Mohammad Hossein and Kibret, Getiye Dejenu and Kieling, Christian and Kim, Cho Il and Kim, Daniel and Kim, Pauline and Kim, Sungroul and Kim, Yun Jin and Kimokoti, Ruth W. and Kinfu, Yohannes and Kishawi, Sami and Kissimova-Skarbek, Katarzyna A. and Kissoon, Niranjan and Kivimaki, Mika and Knudsen, Ann Kristin and Kokubo, Yoshihiro and Kopec, Jacek A. and Kosen, Soewarta and Koul, Parvaiz A. and Koyanagi, Ai and Kravchenko, Michael and Krohn, Kristopher J. and {Kuate Defo}, Barthelemy and {Kucuk Bicer}, Burcu and Kuipers, Ernst J. and Kulikoff, Xie Rachel and Kulkarni, Veena S. and Kumar, G. Anil and Kumar, Pushpendra and Kumsa, Fekede Asefa and Kutz, Michael and Lachat, Carl and Lagat, Abraham K. and Lager, Anton Carl Jonas and Lal, Dharmesh Kumar and Lalloo, Ratilal and Lambert, Nkurunziza and Lan, Qing and Lansingh, Van C. and Larson, Heidi J. and Larsson, Anders and Laryea, Dennis Odai and Lavados, Pablo M. and Laxmaiah, Avula and Lee, Paul H. and Leigh, James and Leung, Janni and Leung, Ricky and Levi, Miriam and Li, Yongmei and Liao, Yu and Liben, Misgan Legesse and Lim, Stephen S. and Linn, Shai and Lipshultz, Steven E. and Liu, Shiwei and Lodha, Rakesh and Logroscino, Giancarlo and Lorch, Scott A. and Lorkowski, Stefan and Lotufo, Paulo A. and Lozano, Rafael and Lunevicius, Raimundas and Lyons, Ronan A. and Ma, Stefan and Macarayan, Erlyn Rachelle King and Machado, Isis Eloah and Mackay, Mark T. and {Magdy Abd El Razek}, Mohammed and Magis-Rodriguez, Carlos and Mahdavi, Mahdi and Majdan, Marek and Majdzadeh, Reza and Majeed, Azeem and Malekzadeh, Reza and Malhotra, Rajesh and Malta, Deborah Carvalho and Mantovani, Lorenzo G. and Manyazewal, Tsegahun and Mapoma, Chabila C. and Marczak, Laurie B. and Marks, Guy B. and Martinez-Raga, Jose and Martins-Melo, Francisco Rogerl{\^{a}}ndio and Massano, Jo{\~{a}}o and Maulik, Pallab K. and Mayosi, Bongani M. and Mazidi, Mohsen and McAlinden, Colm and McGarvey, Stephen Theodore and McGrath, John J. and McKee, Martin and Mehata, Suresh and Mehndiratta, Man Mohan and Mehta, Kala M. and Meier, Toni and Mekonnen, Tefera Chane and Meles, Kidanu Gebremariam and Memiah, Peter and Memish, Ziad A. and Mendoza, Walter and Mengesha, Melkamu Merid and Mengistie, Mubarek Abera and Mengistu, Desalegn Tadese and Menon, Geetha R. and Menota, Bereket Gebremichael and Mensah, George A. and Meretoja, Atte and Meretoja, Tuomo J. and Mezgebe, Haftay Berhane and Micha, Renata and Mikesell, Joseph and Miller, Ted R. and Mills, Edward J. and Minnig, Shawn and Mirarefin, Mojde and Mirrakhimov, Erkin M. and Misganaw, Awoke and Mishra, Shiva Raj and Mohammad, Karzan Abdulmuhsin and Mohammadi, Alireza and Mohammed, Kedir Endris and Mohammed, Shafiu and Mohan, Murali B.V. and Mohanty, Sanjay K. and Mokdad, Ali H. and {Molla Assaye}, Ashagre and Mollenkopf, Sarah K. and Molokhia, Mariam and Monasta, Lorenzo and {Monta{\~{n}}ez Hernandez}, Julio Cesar and Montico, Marcella and Mooney, Meghan D. and Moore, Ami R. and Moradi-Lakeh, Maziar and Moraga, Paula and Morawska, Lidia and {Moreno Velasquez}, Ilais and Mori, Rintaro and Morrison, Shane D. and Mruts, Kalayu Birhane and Mueller, Ulrich O. and Mullany, Erin and Muller, Kate and Murthy, Gudlavalleti Venkata Satyanarayana and Murthy, Srinivas and Musa, Kamarul Imran and Nachega, Jean B. and Nagata, Chie and Nagel, Gabriele and Naghavi, Mohsen and Naidoo, Kovin S. and Nanda, Lipika and Nangia, Vinay and Nascimento, Bruno Ramos and Natarajan, Gopalakrishnan and Negoi, Ionut and Nguyen, Cuong Tat and Nguyen, Grant and Nguyen, Quyen Le and Nguyen, Trang Huyen and Ningrum, Dina Nur Anggraini and Nisar, Muhammad Imran and Nomura, Marika and Nong, Vuong Minh and Norheim, Ole F. and Norrving, Bo and Noubiap, Jean Jacques N. and Nyakarahuka, Luke and Obermeyer, Carla Makhlouf and O'Donnell, Martin J. and Ogbo, Felix Akpojene and Oh, In Hwan and Okoro, Anselm and Oladimeji, Olanrewaju and Olagunju, Andrew Toyin and Olusanya, Bolajoko Olubukunola and Olusanya, Jacob Olusegun and Oren, Eyal and Ortiz, Alberto and Osgood-Zimmerman, Aaron and Ota, Erika and Owolabi, Mayowa O. and Oyekale, Abayomi Samuel and Mahesh, P. A. and Pacella, Rosana E. and Pakhale, Smita and Pana, Adrian and Panda, Basant Kumar and Panda-Jonas, Songhomitra and Park, Eun Kee and Parsaeian, Mahboubeh and Patel, Tejas and Patten, Scott B. and Patton, George C. and Paudel, Deepak and Pereira, David M. and Perez-Padilla, Rogelio and Perez-Ruiz, Fernando and Perico, Norberto and Pervaiz, Aslam and Pesudovs, Konrad and Peterson, Carrie Beth and Petri, William Arthur and Petzold, Max and Phillips, Michael Robert and Piel, Fr{\'{e}}d{\'{e}}ric B. and Pigott, David M. and Pishgar, Farhad and Plass, Dietrich and Polinder, Suzanne and Popova, Svetlana and Postma, Maarten J. and Poulton, Richie G. and Pourmalek, Farshad and Prasad, Narayan and Purwar, Manorama and Qorbani, Mostafa and Rabiee, Rynaz H.S. and Radfar, Amir and Rafay, Anwar and Rahimi-Movaghar, Afarin and Rahimi-Movaghar, Vafa and Rahman, Mahfuzar and Rahman, Mohammad Hifz Ur and Rahman, Sajjad Ur and Rai, Rajesh Kumar and Rajsic, Sasa and Ram, Usha and Rana, Saleem M. and Ranabhat, Chhabi Lal and Rao, Paturi Vishnupriya and Rawaf, Salman and Ray, Sarah E. and Rego, Maria Albertina Santiago and Rehm, J{\"{u}}rgen and Reiner, Robert C. and Remuzzi, Giuseppe and Renzaho, Andre M.N.N. and Resnikoff, Serge and Rezaei, Satar and Rezai, Mohammad Sadegh and Ribeiro, Antonio L. and Rokni, Mohammad Bagher and Ronfani, Luca and Roshandel, Gholamreza and Roth, Gregory A. and Rothenbacher, Dietrich and Roy, Ambuj and Rubagotti, Enrico and Ruhago, George Mugambage and Saadat, Soheil and Sabde, Yogesh Damodar and Sachdev, Perminder S. and Sadat, Nafis and Safdarian, Mahdi and Safi, Sare and Safiri, Saeid and Sagar, Rajesh and Sahathevan, Ramesh and Sahebkar, Amirhossein and Sahraian, Mohammad Ali and Salama, Joseph and Salamati, Payman and Salomon, Joshua A. and Salvi, Sundeep Santosh and Samy, Abdallah M. and Sanabria, Juan Ramon and Sanchez-Ni{\~{n}}o, Maria Dolores and Santos, Itamar S. and {Santric Milicevic}, Milena M. and Sarmiento-Suarez, Rodrigo and Sartorius, Benn and Satpathy, Maheswar and Sawhney, Monika and Saxena, Sonia and Saylan, Mete I. and Schmidt, Maria In{\^{e}}s and Schneider, Ione J.C. and Schutte, Aletta E. and Schwebel, David C. and Schwendicke, Falk and Seedat, Soraya and Seid, Abdulbasit Musa and Sepanlou, Sadaf G. and Servan-Mori, Edson E. and Shackelford, Katya Anne and Shaheen, Amira and Shahraz, Saeid and Shaikh, Masood Ali and Shamsipour, Mansour and Shamsizadeh, Morteza and Islam, Sheikh Mohammed Shariful and Sharma, Jayendra and Sharma, Rajesh and She, Jun and Shen, Jiabin and Shetty, Balakrishna P. and Shi, Peilin and Shibuya, Kenji and Shigematsu, Mika and Shiri, Rahman and Shiue, Ivy and Shrime, Mark G. and Sigfusdottir, Inga Dora and Silberberg, Donald H. and Silpakit, Naris and Silva, Diego Augusto Santos and Silva, Jo{\~{a}}o Pedro and Silveira, Dayane Gabriele Alves and Sindi, Shireen and Singh, Abhishek and Singh, Jasvinder A. and Singh, Prashant Kumar and Singh, Virendra and Sinha, Dhirendra Narain and Skiadaresi, Eirini and Sligar, Amber and Smith, David L. and Sobaih, Badr H.A. and Sobngwi, Eugene and Soneji, Samir and Soriano, Joan B. and Sreeramareddy, Chandrashekhar T. and Srinivasan, Vinay and Stathopoulou, Vasiliki and Steel, Nicholas and Stein, Dan J. and Steiner, Caitlyn and St{\"{o}}ckl, Heidi and Stokes, Mark Andrew and Strong, Mark and Sufiyan, Muawiyyah Babale and Suliankatchi, Rizwan Abdulkader and Sunguya, Bruno F. and Sur, Patrick J. and Swaminathan, Soumya and Sykes, Bryan L. and Szoeke, Cassandra E.I. and Tabar{\'{e}}s-Seisdedos, Rafael and Tadakamadla, Santosh Kumar and Tadese, Fentaw and Tandon, Nikhil and Tanne, David and Tarajia, Musharaf and Tavakkoli, Mohammad and Taveira, Nuno and Tehrani-Banihashemi, Arash and Tekelab, Tesfalidet and Tekle, Dejen Yemane and {Temam Shifa}, Girma and Temsah, Mohamad Hani and Terkawi, Abdullah Sulieman and Tesema, Cheru Leshargie and Tesssema, Belay and Theis, Andrew and Thomas, Nihal and Thompson, Alex H. and Thomson, Alan J. and Thrift, Amanda G. and Tiruye, Tenaw Yimer and Tobe-Gai, Ruoyan and Tonelli, Marcello and Topor-Madry, Roman and Topouzis, Fotis and Tortajada, Miguel and Tran, Bach Xuan and Truelsen, Thomas and Trujillo, Ulises and Tsilimparis, Nikolaos and Tuem, Kald Beshir and Tuzcu, Emin Murat and Tyrovolas, Stefanos and Ukwaja, Kingsley Nnanna and Undurraga, Eduardo A. and Uthman, Olalekan A. and Uzochukwu, Benjamin S.Chudi and {Van Boven}, Job F.M. and Varakin, Yuri Y. and Varughese, Santosh and Vasankari, Tommi and Vasconcelos, Ana Maria Nogales and Venketasubramanian, Narayanaswamy and Vidavalur, Ramesh and Violante, Francesco S. and Vishnu, Abhishek and Vladimirov, Sergey K. and Vlassov, Vasiliy Victorovich and Vollset, Stein Emil and Vos, Theo and Waid, Jillian L. and Wakayo, Tolassa and Wang, Yuan Pang and Weichenthal, Scott and Weiderpass, Elisabete and Weintraub, Robert G. and Werdecker, Andrea and Wesana, Joshua and Wijeratne, Tissa and Wilkinson, James D. and Wiysonge, Charles Shey and Woldeyes, Belete Getahun and Wolfe, Charles D.A. and Workicho, Abdulhalik and Workie, Shimelash Bitew and Xavier, Denis and Xu, Gelin and Yaghoubi, Mohsen and Yakob, Bereket and Yalew, Ayalnesh Zemene and Yan, Lijing L. and Yano, Yuichiro and Yaseri, Mehdi and Ye, Pengpeng and Yimam, Hassen Hamid and Yip, Paul and Yirsaw, Biruck Desalegn and Yonemoto, Naohiro and Yoon, Seok Jun and Yotebieng, Marcel and Younis, Mustafa Z. and Zaidi, Zoubida and {El Sayed Zaki}, Maysaa and Zeeb, Hajo and Zenebe, Zerihun Menlkalew and Zerfu, Taddese Alemu and Zhang, Anthony Lin and Zhang, Xueying and Zodpey, Sanjay and Zuhlke, Liesl Joanna and Lopez, Alan D. and Murray, Christopher J.L.},
doi = {10.1016/S0140-6736(17)31833-0},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2017 - Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, (2).pdf:pdf},
issn = {1474547X},
journal = {The Lancet},
month = {sep},
number = {10100},
pages = {1084--1150},
pmid = {28919115},
publisher = {Lancet Publishing Group},
title = {{Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: A systematic analysis for the Global Burden of Disease Study 2016}},
url = {www.thelancet.com},
volume = {390},
year = {2017}
}
@article{Costa2020,
abstract = {Background: In Brazil, both the Civil Registry (CR) and Ministry of Health (MoH) Mortality Information System (SIM) are sources of routine mortality data, but neither is 100{\%} complete. Deaths from these two sources can be linked to facilitate estimation of completeness of mortality reporting and measurement of adjusted mortality indicators using generalized linear modeling (GLM). Methods: The 2015 and 2016 CR and SIM data were linked using deterministic methods. GLM with covariates of the deceased's sex, age, state of residence, cause of death and place of death, and municipality-level education decile and population density decile, was used to estimate total deaths and completeness nationally, subnationally and by population sub-group, and to identify the characteristics of unreported deaths. The empirical completeness method and Global Burden of Disease (GBD) 2017 estimates were comparators at the national and state level. Results: Completeness was 98{\%} for SIM and 95{\%} for CR. The vast majority of deaths in Brazil were captured by either system and 94{\%} were reported by both sources. For each source, completeness was lowest in the north. SIM completeness was consistently high across all sub-groups while CR completeness was lowest for deaths at younger ages, outside facilities, and in the lowest deciles of municipality education and population density. There was no clear municipality-level relationship in SIM and CR completeness, suggesting minimal dependence between sources. The empirical completeness method model 1 and GBD completeness estimates were each, on average, less than three percentage points different from GLM estimates at the state level. Life expectancy was lowest in the northeast and 7.5 years higher in females than males. Conclusions: GLM using socio-economic and demographic covariates is a valuable tool to accurately estimate completeness from linked data sources. Close scrutiny of the quality of variables used to link deaths, targeted identification of unreported deaths in poorer, northern states, and closer coordination of the two systems will help Brazil achieve 100{\%} death reporting completeness. The results also confirm the validity of the empirical completeness method.},
author = {Costa, Luiz Fernando Lima and {De Mesquita Silva Montenegro}, Marli and {Rabello Neto}, Dacio De Lyra and {De Oliveira}, Antonio Tadeu Ribeiro and Trindade, Jose Eduardo De Oliveira and Adair, Tim and Marinho, Maria De Fatima},
doi = {10.1186/s12963-020-00223-2},
file = {:C$\backslash$:/Users/LUCAS/Documents/Adicionar a Mendeley/s12963-020-00223-2.pdf:pdf},
isbn = {1296302000223},
issn = {14787954},
journal = {Population Health Metrics},
keywords = {Brazil,Capture-recapture methods,Civil registration and vital statistics,Generalized linear modeling,Mortality,Record linkage,Registration completeness},
number = {1},
pages = {1--12},
pmid = {32887639},
publisher = {Population Health Metrics},
title = {{Estimating completeness of national and subnational death reporting in Brazil: Application of record linkage methods}},
volume = {18},
year = {2020}
}
@article{Quevedo2020,
author = {Quevedo-Ramirez, Andres and Al-kassab-C{\'{o}}rdova, Ali and Mendez-Guerra, Carolina and Cornejo-Venegas, Gonzalo and Alva-Chavez, Kenedy P.},
doi = {10.1016/j.resp.2020.103512},
issn = {18781519},
journal = {Respiratory Physiology and Neurobiology},
month = {oct},
pmid = {32739459},
publisher = {Elsevier B.V.},
title = {{Altitude and excess mortality during COVID-19 pandemic in Peru}},
volume = {281},
year = {2020}
}
@article{Hazard2020,
abstract = {Background: The majority of low-and middle-income countries (LMICs) do not have adequate civil registration and vital statistics (CRVS) systems to properly support health policy formulation. Verbal autopsy (VA), long used in research, can provide useful information on the cause of death (COD) in populations where physicians are not available to complete medical certificates of COD. Here, we report on the application of the SmartVA tool for the collection and analysis of data in several countries as part of routine CRVS activities. Methods: Data from VA interviews conducted in 4 of 12 countries supported by the Bloomberg Philanthropies Data for Health (D4H) Initiative, and at different stages of health statistical development, were analysed and assessed for plausibility: Myanmar, Papua New Guinea (PNG), Bangladesh and the Philippines. Analyses by age-and cause-specific mortality fractions were compared to the Global Burden of Disease (GBD) study data by country. VA interviews were analysed using SmartVA-Analyze-automated software that was designed for use in CRVS systems. The method in the Philippines differed from the other sites in that the VA output was used as a decision support tool for health officers.},
author = {Hazard, Riley H and {K Buddhika}, Mahesh P and Hart, John D and Chowdhury, Hafizur R and Firth, Sonja and Joshi, Rohina and Avelino, Ferchito and Segarra, Agnes and {Carmina Sarmiento}, Deborah and {Kalam Azad}, Abdul and {Ali Akbar Ashrafi}, Shah and {Sandar Bo}, Khin and Kwa, Violoa and Lopez, Alan D},
doi = {10.1186/s12916-020-01520-1},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hazard et al. - 2020 - Automated verbal autopsy from researchto routine use in civil registration and vitalstatistics systems.pdf:pdf;:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hazard et al. - 2020 - Automated verbal autopsy from researchto routine use in civil registration and vitalstatistics systems(2).pdf:pdf},
journal = {BMC Medicine},
pages = {18--60},
title = {{Automated verbal autopsy: from researchto routine use in civil registration and vitalstatistics systems}},
url = {https://doi.org/10.1186/s12916-020-01520-1},
year = {2020}
}
@article{Woolf2020,
author = {Woolf, Steven H. and Chapman, Derek A. and Sabo, Roy T. and Weinberger, Daniel M. and Hill, Latoya},
doi = {10.1001/jama.2020.11787},
file = {:C$\backslash$:/Users/LUCAS/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Woolf et al. - 2020 - Excess Deaths from COVID-19 and Other Causes, March-April 2020.pdf:pdf},
issn = {15383598},
journal = {JAMA - Journal of the American Medical Association},
keywords = {The JAMA Network},
number = {5},
pages = {E1--E3},
pmid = {32609307},
title = {{Excess Deaths from COVID-19 and Other Causes, March-April 2020}},
volume = {324},
year = {2020}
}