Effects of COVID-19 on mortality: A 5-year population-based study in Oman

Wahaibi, Adil Al; Al-Maani, Amal; Alyaquobi, Fatma; Harthy, Khalid Al; Al-Jardani, Amina; Rawahi, Bader Al; Al-Abri, Seif

doi:10.1016/j.ijid.2020.12.054

Cited by 14 publications

(17 citation statements)

References 15 publications

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“…As a report from the National Center for Statistic and information (NCSI) 2021, Oman population is 4,471,148 as shown in annex1. Oman reported the first case of COVID-19 on 24 February 2020, and since the epidemic began, the national health system has evolved to impact the health and lives of the population directly or through different interventions implemented to control it [2,3]. Almost one year later, on 20 February 2021, a total of 139,692 confirmed cases of SARS-CoV-2 infection and 1555 cumulative deaths were reported in Oman by the Ministry of Health (MOH) [4].…”

Section: Introductionmentioning

confidence: 99%

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Knowledge, Attitudes, and Practices (KAP) toward the COVID-19 Vaccine in Oman: A Pre-Campaign Cross-Sectional Study

et al. 2021

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Oman is globally acknowledged for its well-structured immunization program with high vaccination coverage. The massive spread of misinformation brought on by the COVID-19 pandemic, as well as the easy access to various media channels, may affect acceptance of a vaccine, despite the inherent trust in the local system. This cross-sectional study evaluated the knowledge, attitudes, and practice (KAP) in Oman toward COVID-19 vaccines. It included 3000 randomly selected adults answering a structured questionnaire via telephone. Participants were 66.7% Omani, 76% male, and 83.7% without comorbidities. Their mean age was 38.27 years (SD ± 10.45). Knowledge of COVID-19′s symptoms, mode of transmission, and attitudes toward the disease was adequate; 88.4% had heard of the vaccine, 59.3% would advise others to take it, 56.8% would take it themselves, and 47.5% would take a second dose. Males (CI = 2.37, OR = (2.00–2.81)) and non-Omanis (CI = 0.49, OR = (0.42–0.57)) were more willing to be vaccinated. The history of chronic disease, source of vaccine knowledge, and education level were factors that affected the willingness to accept the vaccine. The Omani community’s willingness to take the COVID-19 vaccine can be enhanced by utilizing social media and community influencers to spread awareness about the vaccine’s safety and efficacy.

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Section: Introductionmentioning

confidence: 99%

“…Almost one year later, on 20 February 2021, a total of 139,692 confirmed cases of SARS-CoV-2 infection and 1555 cumulative deaths were reported in Oman by the Ministry of Health (MOH) [4]. The pandemic has resulted in 15% access to all causes of mortality [2].…”

Section: Introductionmentioning

confidence: 99%

Knowledge, Attitudes, and Practices (KAP) toward the COVID-19 Vaccine in Oman: A Pre-Campaign Cross-Sectional Study

et al. 2021

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“…The response to outbreaks and epidemics is not a new experience for the health care system in Oman but the lengthy duration this pandemic has truly tested the capacity of the current system in sustaining response while observing global development goals. The lack of health insurance for the immigrant workers in Oman, dependence on government funding for health care, the lockdown situations as a restricted mobility intervention for control of the disease spread especially before the availability of vaccines, and the burden of chronic diseases were reflected in the excess mortality rates during 1st year of the pandemic (15%) ( 28 ).…”

Section: Lessons and Way Forward In The Post-covid-19 Pandemic Eramentioning

confidence: 99%

Challenges and Opportunities for Public Health Service in Oman From the COVID-19 Pandemic: Learning Lessons for a Better Future

Khalili

Maani

Wahaibi

et al. 2021

Front. Public Health

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Despite the apparent challenges inflicted by COVID-19 globally, the pandemic provided an opportunity to utilize and expand existing public health capacities for a more adaptive and resilient system during and after each wave of the disease. This paper provides a narrative review of Oman's public health response to the COVID-19 pandemic from January 2020 to July 2021, and the challenges it faced for a more rapid and efficient response. The review demonstrates that the three main pillars influencing the direction of the pandemic and aiding the control are Oman's unified governmental leadership, the move to expand the capacity of the health care system at all levels, and community partnership in all stages of the response including the COVID-19 vaccination campaign. The opportunities identified during response stages in the harmonization of the multisectoral response, streamlining communication channels, addressing vulnerable communities (dormitories, residences at border regions), and providing professional technical leadership provide an excellent precursor for expediting the transformation of Oman's health care system to one with a multisectoral holistic approach. Some of the major challenges faced are the shortage of the public health cadre, lack of a fully integrated digital platform for surveillance, and the scarcity of experts in risk communication and community engagement. A future health system where the center for diseases surveillance and control acts as a nucleus for multisectoral expertise and leadership, which includes community representatives, is crucial to attain optimum health. The destruction inflicted by this prolong COVID-19 pandemic at all levels of human life had valued the importance of investing on preventive and preparedness strategies.

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“…The Farrington algorithm 6,7 is commonly used to estimate excess death by the United States Centers for Disease Control and Prevention (CDC), which updates the estimates weekly and supports the decision‐making process for the control of COVID‐19 in the United States 15 . The algorithm is also based on a quasi‐Poisson regression model, which will be explained subsequently, and has been extensively used in many countries to estimate the country‐specific excess death related to COVID‐19 in a timely manner 16‐18 . Noufaily et al 7 extended the Farrington algorithm by incorporating robust residuals and conducted extensive simulation experiments for improving the specificity, whose generalization is our interest in this study.…”

Section: Introductionmentioning

confidence: 99%

“…15 The algorithm is also based on a quasi-Poisson regression model, which will be explained subsequently, and has been extensively used in many countries to estimate the country-specific excess death related to COVID-19 in a timely manner. [16][17][18] Noufaily et al 7 extended the Farrington algorithm by incorporating robust residuals and conducted extensive simulation experiments for improving the specificity, whose generalization is our interest in this study. Reviews of other outbreak detection methods using time series data can be found in Buckeridge, 19 Unkel et al, 2 and Noufaily et al 20 One of the major challenges currently facing large outbreak detection survey systems is that long-term data are sometimes unavailable.…”

Section: Introductionmentioning

confidence: 99%

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

Yoneoka

Kawashima

Makiyama³

et al. 2021

Statistics in Medicine

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The demand for rapid surveillance and early detection of local outbreaks has been growing recently. The rapid surveillance can select timely and appropriate interventions toward controlling the spread of emerging infectious diseases, such as the coronavirus disease 2019 (COVID-19). The Farrington algorithm was originally proposed by Farrington et al (1996), extended by Noufaily et al (2012), and is commonly used to estimate excess death. However, one of the major challenges in implementing this algorithm is the lack of historical information required to train it, especially for emerging diseases. Without sufficient training data the estimation/prediction accuracy of this algorithm can suffer leading to poor outbreak detection. We propose a new statistical algorithm-the geographically weighted generalized Farrington (GWGF) algorithm-by incorporating both geographically varying and geographically invariant covariates, as well as geographical information to analyze time series count data sampled from a spatially correlated process for estimating excess death. The algorithm is a type of local quasi-likelihood-based regression with geographical weights and is designed to achieve a stable detection of outbreaks even when the number of time points is small. We validate the outbreak detection performance by using extensive numerical experiments and real-data analysis in Japan during COVID-19 pandemic.We show that the GWGF algorithm succeeds in improving recall without reducing the level of precision compared with the conventional Farrington algorithm.

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Effects of COVID-19 on mortality: A 5-year population-based study in Oman

Cited by 14 publications

References 15 publications

Knowledge, Attitudes, and Practices (KAP) toward the COVID-19 Vaccine in Oman: A Pre-Campaign Cross-Sectional Study

Knowledge, Attitudes, and Practices (KAP) toward the COVID-19 Vaccine in Oman: A Pre-Campaign Cross-Sectional Study

Challenges and Opportunities for Public Health Service in Oman From the COVID-19 Pandemic: Learning Lessons for a Better Future

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

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