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Gmanyami JM, Quentin W, Lambert O, Jarynowski A, Belik V, Amuasi JH. Excess mortality during the COVID-19 pandemic in low-and lower-middle-income countries: a systematic review and meta-analysis. BMC Public Health 2024; 24:1643. [PMID: 38902661 PMCID: PMC11188207 DOI: 10.1186/s12889-024-19154-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Although the COVID-19 pandemic claimed a great deal of lives, it is still unclear how it affected mortality in low- and lower-middle-income countries (LLMICs). This review summarized the available literature on excess mortality during the COVID-19 pandemic in LLMICs, including methods, sources of data, and potential contributing factors that might have influenced excess mortality. METHODS We conducted a systematic review and meta-analysis on excess mortality during the COVID-19 pandemic in LLMICs in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines We searched PubMed, Embase, Web of Science, Cochrane Library, Google Scholar, and Scopus. We included studies published from 2019 onwards with a non-COVID-19 period of at least one year as a comparator. The meta-analysis included studies reporting data on population size, as well as observed and expected deaths. We used the Mantel-Haenszel method to estimate the pooled risk ratio with 95% confidence intervals. The protocol was registered in PROSPERO (ID: CRD42022378267). RESULTS The review covered 29 countries, with 10 countries included in the meta-analysis. The pooled meta-analysis included 1,405,128,717 individuals, for which 2,152,474 deaths were expected, and 3,555,880 deaths were reported. Calculated excess mortality was 100.3 deaths per 100,000 population per year, with an excess risk of death of 1.65 (95% CI: 1.649, 1.655, p < 0.001). The data sources used in the studies included civil registration systems, surveys, public cemeteries, funeral counts, obituary notifications, burial site imaging, and demographic surveillance systems. The primary techniques used to estimate excess mortality were statistical forecast modelling and geospatial analysis. One out of the 24 studies found higher excess mortality in urban settings. CONCLUSION Our findings demonstrate that excess mortality in LLMICs during the pandemic was substantial. However, estimates of excess mortality are uncertain due to relatively poor data. Understanding the drivers of excess mortality, will require more research using various techniques and data sources.
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Affiliation(s)
- Jonathan Mawutor Gmanyami
- School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
- German West-African Centre for Global Health and Pandemic Prevention, Berlin, Germany.
- Global Health and Infectious Diseases Research Group, Kumasi Centre for Collaborative Research in Tropical Medicine, Kumasi, Ghana.
| | - Wilm Quentin
- German West-African Centre for Global Health and Pandemic Prevention, Berlin, Germany
- Department of Health Care Management, Technische Universität Berlin, Berlin, Germany
- Chair of Planetary & Public Health, University of Bayreuth, Bayreuth, Germany
| | - Oscar Lambert
- School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Andrzej Jarynowski
- Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany
| | - Vitaly Belik
- Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany
| | - John Humphrey Amuasi
- School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- German West-African Centre for Global Health and Pandemic Prevention, Berlin, Germany
- Global Health and Infectious Diseases Research Group, Kumasi Centre for Collaborative Research in Tropical Medicine, Kumasi, Ghana
- Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
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Garchitorena A, Rasoloharimanana LT, Rakotonanahary RJ, Evans MV, Miller AC, Finnegan KE, Cordier LF, Cowley G, Razafinjato B, Randriamanambintsoa M, Andrianambinina S, Popper SJ, Hotahiene R, Bonds MH, Schoenhals M. Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study. Int J Epidemiol 2023; 52:1745-1755. [PMID: 37793001 DOI: 10.1093/ije/dyad135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
INTRODUCTION Three years into the pandemic, there remains significant uncertainty about the true infection and mortality burden of COVID-19 in the World Health Organization Africa region. High quality, population-representative studies in Africa are rare and tend to be conducted in national capitals or large cities, leaving a substantial gap in our understanding of the impact of COVID-19 in rural, low-resource settings. Here, we estimated the spatio-temporal morbidity and mortality burden associated with COVID-19 in a rural health district of Madagascar until the first half of 2021. METHODS We integrated a nested seroprevalence study within a pre-existing longitudinal cohort conducted in a representative sample of 1600 households in Ifanadiana District, Madagascar. Socio-demographic and health information was collected in combination with dried blood spots for about 6500 individuals of all ages, which were analysed to detect IgG and IgM antibodies against four specific proteins of SARS-CoV-2 in a bead-based multiplex immunoassay. We evaluated spatio-temporal patterns in COVID-19 infection history and its associations with several geographic, socio-economic and demographic factors via logistic regressions. RESULTS Eighteen percent of people had been infected by April-June 2021, with seroprevalence increasing with individuals' age. COVID-19 primarily spread along the only paved road and in major towns during the first epidemic wave, subsequently spreading along secondary roads during the second wave to more remote areas. Wealthier individuals and those with occupations such as commerce and formal employment were at higher risk of being infected in the first wave. Adult mortality increased in 2020, particularly for older men for whom it nearly doubled up to nearly 40 deaths per 1000. Less than 10% of mortality in this period would be directly attributed to COVID-19 deaths if known infection fatality ratios are applied to observed seroprevalence in the district. CONCLUSION Our study provides a very granular understanding on COVID-19 transmission and mortality in a rural population of sub-Saharan Africa and suggests that the disease burden in these areas may have been substantially underestimated.
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Affiliation(s)
- Andres Garchitorena
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
- Institut Pasteur de Madagascar, Antananarivo, Madagascar
- NGO Pivot, Ifanadiana, Madagascar
| | | | - Rado Jl Rakotonanahary
- NGO Pivot, Ifanadiana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Michelle V Evans
- MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
| | - Ann C Miller
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Karen E Finnegan
- NGO Pivot, Ifanadiana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | | | - Marius Randriamanambintsoa
- Direction de la Démographie et des Statistiques Sociales, Institut National de la Statistique, Antananarivo, Madagascar
| | - Samuel Andrianambinina
- Direction de la Démographie et des Statistiques Sociales, Institut National de la Statistique, Antananarivo, Madagascar
| | - Stephen J Popper
- Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, CA, USA
| | - Raphaël Hotahiene
- Direction de lutte contre les maladies transmissibles, Ministère de la Santé Publique, Antananarivo, Madagascar
| | - Matthew H Bonds
- NGO Pivot, Ifanadiana, Madagascar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
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Rabarison JH, Rakotondramanga JM, Ratovoson R, Masquelier B, Rasoanomenjanahary AM, Dreyfus A, Garchitorena A, Rasambainarivo F, Razanajatovo NH, Andriamandimby SF, Metcalf CJ, Lacoste V, Heraud JM, Dussart P. Excess mortality associated with the COVID-19 pandemic during the 2020 and 2021 waves in Antananarivo, Madagascar. BMJ Glob Health 2023; 8:e011801. [PMID: 37495370 PMCID: PMC10373673 DOI: 10.1136/bmjgh-2023-011801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/17/2023] [Indexed: 07/28/2023] Open
Abstract
INTRODUCTION COVID-19-associated mortality remains difficult to estimate in sub-Saharan Africa because of the lack of comprehensive systems of death registration. Based on death registers referring to the capital city of Madagascar, we sought to estimate the excess mortality during the COVID-19 pandemic and calculate the loss of life expectancy. METHODS Death records between 2016 and 2021 were used to estimate weekly excess mortality during the pandemic period. To infer its synchrony with circulation of SARS-CoV-2, a cross-wavelet analysis was performed. Life expectancy loss due to the COVID-19 pandemic was calculated by projecting mortality rates using the Lee and Carter model and extrapolating the prepandemic trends (1990-2019). Differences in life expectancy at birth were disaggregated by cause of death. RESULTS Peaks of excess mortality in 2020-21 were associated with waves of COVID-19. Estimates of all-cause excess mortality were 38.5 and 64.9 per 100 000 inhabitants in 2020 and 2021, respectively, with excess mortality reaching ≥50% over 6 weeks. In 2021, we quantified a drop of 0.8 and 1.0 years in the life expectancy for men and women, respectively attributable to increased risks of death beyond the age of 60 years. CONCLUSION We observed high excess mortality during the pandemic period, in particular around the peaks of SARS-CoV-2 circulation in Antananarivo. Our study highlights the need to implement death registration systems in low-income countries to document true toll of a pandemic.
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Affiliation(s)
| | | | - Rila Ratovoson
- Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - Bruno Masquelier
- Universite Catholique de Louvain Centre de recherche en demographie et societes, Louvain la neuve, Belgium
| | | | - Anou Dreyfus
- Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - Andres Garchitorena
- Institut Pasteur de Madagascar, Antananarivo, Madagascar
- UMR 224 MIVEGEC, IRD, Montpellier, France
| | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
| | | | | | - C Jessica Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | | | - Jean-Michel Heraud
- Institut Pasteur de Madagascar, Antananarivo, Madagascar
- Institut Pasteur de Dakar, Dakar, Senegal
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Rasambainarivo F, Ramiadantsoa T, Raherinandrasana A, Randrianarisoa S, Rice BL, Evans MV, Roche B, Randriatsarafara FM, Wesolowski A, Metcalf JC. Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar. BMC Public Health 2022; 22:724. [PMID: 35413894 PMCID: PMC9002044 DOI: 10.1186/s12889-022-13150-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background While mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population. Methods To address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection. Results Using cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this. Conclusion At a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13150-8.
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Affiliation(s)
- Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. .,Mahaliana Labs SARL, Antananarivo, Madagascar.
| | - Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Antananarivo, Madagascar.,Department of Mathematics, University of Fianarantsoa, Antananarivo, Madagascar.,MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Antso Raherinandrasana
- Surveillance Unit, Ministry of Health of Madagascar, Antananarivo, Madagascar.,Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar
| | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Michelle V Evans
- MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Benjamin Roche
- MIVEGEC, Université de Montpellier, CNRS, Montpellier, IRD, France
| | - Fidiniaina Mamy Randriatsarafara
- Faculty of Medicine, University of Antananarivo, Antananarivo, Madagascar.,Direction of Preventive Medicine, Ministry of Health of Madagascar, Antananarivo, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessica C Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.,Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, Shapiro JT, Solis A, Soarimalala V, Tortosa P, Kramer R, Moody J, Mucha PJ, Nunn C. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface 2022; 19:20210690. [PMID: 35016555 PMCID: PMC8753172 DOI: 10.1098/rsif.2021.0690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.
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Affiliation(s)
- Kayla Kauffman
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Courtney S. Werner
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Georgia Titcomb
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | | | - Jean Yves Rabezara
- Science de la Nature et Valorisation des Ressources Naturelles, Centre Universitaire Régional de la SAVA, Antalaha, Madagascar
| | | | - Julie Teresa Shapiro
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
| | | | - Pablo Tortosa
- UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Université de La Réunion, Ile de La Réunion, France
| | - Randall Kramer
- Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, NC 27708, USA
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Charles Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. Epidemics 2021; 38:100534. [PMID: 34915300 PMCID: PMC8641444 DOI: 10.1016/j.epidem.2021.100534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/29/2021] [Accepted: 12/02/2021] [Indexed: 12/24/2022] Open
Abstract
For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar; Department of Mathematics, University of Fianarantsoa, Madagascar; Department of Integrative Biology, University of Wisconsin-Madison, WI, USA.
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | - Antso Hasina Raherinandrasana
- Surveillance Unit, Ministry of Health of Madagascar, Madagascar; Faculty of Medicine, University of Antananarivo, Madagascar
| | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Mahaliana Labs SARL, Antananarivo, Madagascar
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7
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.30.21261392. [PMID: 34373863 PMCID: PMC8351785 DOI: 10.1101/2021.07.30.21261392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar
- Department of Mathematics, University of Fianarantsoa, Madagascar
- Department of Integrative Biology, University of Wisconsin-Madison, WI, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | | | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
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8
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Ghafari M, Kadivar A, Katzourakis A. Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis. Int J Infect Dis 2021; 107:101-115. [PMID: 33862214 PMCID: PMC8208896 DOI: 10.1016/j.ijid.2021.04.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE There has been no province-level data on the number of coronavirus disease 2019 (COVID-19)-related deaths in Iran since the start of the pandemic. This study was performed to estimate the number of COVID-19 deaths and population-level exposure per province using seasonal all-cause mortality data. METHODS Time-series data were collected from the National Organization for Civil Registration on the seasonal all-cause mortality from spring 2015 to summer 2020 (from March 21, 2015 to September 21, 2020), in accordance with the Solar Hijri (SH) calendar, to estimate the expected number of seasonal deaths for each province using a piecewise linear regression model. A population-weighted infection fatality ratio was then applied to estimate the level of exposure per province during this period. RESULTS From the start of winter to the end of summer (from December 22, 2019 to September 21, 2020), there were a total of 58 900 (95% confidence interval 46 900-69 500) excess deaths across all 31 provinces, with 27% (95% confidence interval 20-34%) estimated nationwide exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, Qom and Golestan were among the hardest-hit provinces, with nearly 57% exposure, while another 27 provinces showed significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. Unexpectedly high levels of excess mortality were also detected during fall 2019 (from September 23 to December 21, 2019) across 18 provinces, unrelated and prior to the start of the COVID-19 pandemic. CONCLUSIONS This study quantified the pattern of spread of COVID-19 across the country and identified areas with the largest epidemic growth requiring the most immediate interventions.
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Affiliation(s)
- Mahan Ghafari
- Department of Zoology, University of Oxford, Oxford, UK.
| | - Alireza Kadivar
- Center for Statistical and Operational Research, Statsminute Company, Tehran, Iran
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Bonifazi G, Lista L, Menasce D, Mezzetto M, Oliva A, Pedrini D, Spighi R, Zoccoli A. A Statistical Analysis of Death Rates in Italy for the Years 2015-2020 and a Comparison with the Casualties Reported from the COVID-19 Pandemic. Infect Dis Rep 2021; 13:285-301. [PMID: 33915702 PMCID: PMC8167797 DOI: 10.3390/idr13020030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 12/16/2022] Open
Abstract
We analyze the data about casualties in Italy in the period 1 January 2015 to 30 September 2020 released by the Italian National Institute of Statistics (ISTAT). The aim of this article was the description of a statistically robust methodology to extract quantitative values for the seasonal excesses of deaths featured by the data, accompanying them with correct estimates of the relative uncertainties. We will describe the advantages of the method adopted with respect to others listed in literature. The data exhibit a clear sinusoidal behavior, whose fit allows for a robust subtraction of the baseline trend of casualties in Italy, with a surplus of mortality in correspondence to the flu epidemics in winter and to the hottest periods in summer. The overall quality of the fit to the data turns out to be very good, an indication of the validity of the chosen model. We discuss the trend of casualties in Italy by different classes of ages and for the different genders. We finally compare the data-subtracted casualties, as reported by ISTAT, with those reported by the Italian Department for Civil Protection (DPC) relative to the deaths directly attributed to the Coronavirus Disease 2019 caused by the SARS-CoV-2 virus (COVID-19), and we point out the differences in the two samples, collected under different assumptions.
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Affiliation(s)
- Gianluca Bonifazi
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy;
- INFN Sezione di Bologna, Viale C. Berti Pichat, 6/2, 40127 Bologna, Italy; (A.O.); (R.S.); (A.Z.)
| | - Luca Lista
- Dipartimento di Fisica E. Pancini, Università degli Studi di Napoli Federico II, Complesso di Monte Sant’Angelo, ed. 6, Via Cintia, 80126 Napoli, Italy;
- INFN Sezione di Napoli, Complesso di Monte Sant’Angelo, ed. 6, Via Cintia, 80126 Napoli, Italy
| | - Dario Menasce
- INFN Sezione di Milano Bicocca, Piazza della Scienza 3, 20126 Milano, Italy;
- Correspondence:
| | - Mauro Mezzetto
- INFN Sezione di Padova, Via Francesco Marzolo, 8, 35131 Padova, Italy;
| | - Alberto Oliva
- INFN Sezione di Bologna, Viale C. Berti Pichat, 6/2, 40127 Bologna, Italy; (A.O.); (R.S.); (A.Z.)
| | - Daniele Pedrini
- INFN Sezione di Milano Bicocca, Piazza della Scienza 3, 20126 Milano, Italy;
| | - Roberto Spighi
- INFN Sezione di Bologna, Viale C. Berti Pichat, 6/2, 40127 Bologna, Italy; (A.O.); (R.S.); (A.Z.)
| | - Antonio Zoccoli
- INFN Sezione di Bologna, Viale C. Berti Pichat, 6/2, 40127 Bologna, Italy; (A.O.); (R.S.); (A.Z.)
- Alma Mater Studiorum, Università di Bologna, Via Irnerio, 46, 40126 Bologna, Italy
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