Mortality Analysis of COVID-19 Confirmed Cases in Pakistan

Chaudhry, Ambreen; Ikram, Aamer; Baig, Mirza Amir; Salman, Muhammad; Ghafoor, Tamkeen; Hussain, Zakir; Khan, Mumtaz Ali; Ansari, Jamil; Syed, Anjum; Javed, Wasif; Larik, Ehsan; Mohsan, Muhammad; Masood, Naveed; Iqbal, Zeeshan; Akram, Khurram

doi:10.1101/2020.06.07.20121939

Cited by 8 publications

(5 citation statements)

References 14 publications

(12 reference statements)

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“…The stimulation of adaptive immunity is dependent not only on the identification of a specific antigen receptor but also on key signals provided by the innate immune system. The decrease of de-novo production of T and B cells is a well-known age-associated immune system modification ( 13 ).…”

Section: Aging and Adaptive Immunitymentioning

confidence: 99%

“…However, no evidence of a greater risk among pregnant women has been found ( 12 ). According to the literature, the death rate among hospitalized patients was 15%, with an average time from onset of symptoms to death of 14 days ( 13 ). Based on the data collected from Italy and China, Corona patients have a mortality rate of 2.3%, with more than half of the mortality reported in patients with age 50 years or older ( 14 ).…”

Section: Lung Complications and Their Mortality Rates With Respect To Agementioning

confidence: 99%

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Aging Immune System and Its Correlation With Liability to Severe Lung Complications

Wang

Peng

2021

Front. Public Health

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Aging is considered to be a decline in physical and physiological events that extensively affect the body's immunity, and is linked with deterioration in both innate and adaptive immune responses. The immune system exhibits profound age-associated variations, known as immunosenescence, comprising a significantly low production of B and T lymphocytes in bone marrow and thymus, a decreased function of mature lymphocytes in secondary lymphoid tissues, a decrease in the synthesis of fresh naïve T cells, and reduced activation of T cells. Elderly individuals face a greater risk for many diseases particularly respiratory diseases due to their poor response to immune challenges as vigorously as the young. The current review explored the aging immune system, highlight the mortality rates of severe lung complications, such as pneumonia, COVID-19, asthma, COPD, lung cancer, IPF, and acute lung injury, and their correlation with aging immunity. This study can be helpful in better understanding the pathophysiology of aging, immune responses, and developing new approaches to improve the average age of the elderly population.

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Section: Aging and Adaptive Immunitymentioning

confidence: 99%

Section: Lung Complications and Their Mortality Rates With Respect To Agementioning

confidence: 99%

Aging Immune System and Its Correlation With Liability to Severe Lung Complications

Wang

Peng

2021

Front. Public Health

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“…Focuses on asymptomatic individuals and high incidence sub-wards Monteiro, [124] India-Pune, Maharashtra Provides no relevant seroprevalence or IFR Sharma, [125] India-Punjab Provides no relevant seroprevalence or IFR Vignesh, [126] India-Tamil Nadu Provides no relevant seroprevalence or IFR Laxminarayan, [127] India-Tamil Nadu and Andhra Pradesh Provides no relevant seroprevalence or IFR Panchamia, [128] India-three states in Western India Focuses on elderly homes Conducted at a hospital/healthcare clinic/ICU Abbas, [135] Pakistan Focuses on healthcare workers Chaudhry, [136] Pakistan Provides no relevant seroprevalence or IFR Din, [137, p. 19] Pakistan Provides no relevant seroprevalence or IFR Peter, [138] Pakistan Provides forecasted estimates Waqar, [139] Pakistan Focuses on symptomatic individuals Zaidi, [140] Pakistan Focuses on working individuals Naiyar, [141] Pakistan-Gujrat, Punjab…”

Section: -36mentioning

confidence: 99%

SARS-CoV-2 infection fatality rates in India: systematic review, meta-analysis and model-based estimation

Zimmermann

Bhattacharya

Purkayastha

et al. 2021

Preprint

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Introduction Fervorous investigation and dialogue surrounding the true number of SARS-CoV-2 related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nations devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India, through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from April 1, 2020 to June 30, 2021. Methods Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch), were searched on July 3, 2021 (with results verified through August 15, 2021). Altogether using a two-step approach, 4,765 initial citations were screened resulting in 37 citations included in the narrative review and 19 studies with 41 datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analyze IFR1 which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provide lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2 related IFRs in India. We also try to stratify our empirical results across the first and the second wave. In tandem, we present updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from April 1, 2020 to June 30, 2021. Results For India countrywide, underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3-29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4-11.9 with cumulative excess deaths ranging from 1.79-4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067 - 0.140) and 0.365% (95% CI: 0.264 - 0.504) to 0.485% (95% CI: 0.344 - 0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, the IFR1 generally appear to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290-1.316) to (0.241-0.651) %). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097 - 0.116) and 0.367% (95% CI: 0.358 - 0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data with the disadvantages being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion When incorporating case and death underreporting, the meta-analyzed cumulative infection fatality rate in India varies from 0.36%-0.48%, with a case underreporting factor ranging from 25-30 and a death underreporting factor ranging from 4-12. This implies, by June 30, 2021, India may have seen nearly 900 million infections and 1.7-4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (covid19india.org) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India.

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“…In a study by Elizabeth [12] et al, older age was the most significant risk factor for death, followed by hematologic malignancies, immunosuppressive drugs, organ transplantation, uncontrolled diabetes mellitus, black race, and obesity. A study from Pakistan by Ambreen Chaudhry [13] et al found that the risk factors responsible for worse outcomes, defined as ICU admission, invasive mechanical ventilation (IMV), and death, were older age and the presence of comorbidities.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 outcomes associated with clinical and demographic characteristics in patients hospitalized with severe and critical disease in Peshawar

Imran

Uddin

Yousaf³

et al. 2022

Preprint

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Background As a novel disease, understanding the relationship between the clinical and demographic characteristics of coronavirus disease 2019 (COVID-19) patients and their outcome is critical. We investigated this relationship in hospitalized patients in a tertiary healthcare setting. Aims/objectives To study COVID-19 severity and outcomes in relation to clinical and demographic characteristics of in admitted patients Methodology In this cross-sectional study, medical records for 1087 COVID-19 patients were reviewed to extract symptoms, comorbidities, demographic characteristics, and outcomes data. Statistical analyses included the post-stratification chi-square test, independent sample t-test, multivariate logistic regression, and time-to-event analysis. Results The majority of the study participants were >50 years old (67%) and male (59%) and had the following symptoms: fever (96%), cough (95%), shortness of breath (73%), loss of taste (77%), and loss of smell (77%). Regarding worst outcome, multivariate regression analysis showed that these characteristics were statistically significant: shortness of breath (adjusted odds ratio [aOR] 31.3; 95% CI, 11.87–82.53; p < 0.001), intensive care unit (ICU) admission (aOR 28.3; 95% CI,9.0–89.6; p < 0.001), diabetes mellitus (aOR 5.1; 95% CI;3.2–8.2; p < 0.001), ischemic heart disease (aOR 3.4; 95% CI,1.6–7; p = 0.001), nausea and vomiting (aOR 3.3; 95% CI, 1.7–6.6; p = 0.001), and prolonged hospital stay (aOR 1.04; 95% CI, 1.02–1.08; p = 0.001), while patients with rhinorrhea were significantly protected (aOR 0.3; 95% CI, 0.2–0.5; p < 0.001). A Kaplan–Meier curve showed that the symptoms of shortness of breath, ICU admission, fever, nausea and vomiting, and diarrhea increased the risk of mortality. Conclusion Increasing age, certain comorbidities and symptoms, and direct admission to the ICU increased the risk of worse outcomes. Further research is needed to determine risk factors that may increase disease severity and devise a proper risk-scoring system to initiate timely management.

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Mortality Analysis of COVID-19 Confirmed Cases in Pakistan

Cited by 8 publications

References 14 publications

Aging Immune System and Its Correlation With Liability to Severe Lung Complications

Aging Immune System and Its Correlation With Liability to Severe Lung Complications

SARS-CoV-2 infection fatality rates in India: systematic review, meta-analysis and model-based estimation

COVID-19 outcomes associated with clinical and demographic characteristics in patients hospitalized with severe and critical disease in Peshawar

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