Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data

Azizi, Zahra; Shiba, Yumika; Alipour, Pouria; Maleki, Farhad; Raparelli, Valeria; Norris, Colleen M.; Forghani, Reza; Pilote, Louise; Emam, Khaled El

doi:10.1136/bmjopen-2021-050450

Cited by 13 publications

(13 citation statements)

References 42 publications

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“…This study shows that the willingness of male residents in Chinese communities to be vaccinated is 1.48 times that of female residents in Chinese communities. The gender difference reflected by this vaccination may be related to the higher mortality rate of COVID-19 in men than in women, which is consistent with previous research results in China and internationally (17)(18)(19); a study (20) has reported that in the COVID-19 pandemic, women generally implement non-drug intervention measures such as wearing masks and washing hands frequently, so it can be summarized that men tend to vaccinate and women tend to use non-drug intervention to prevent COVID-19.…”

Section: Discussionsupporting

confidence: 91%

A systematic review and meta-analysis of the factors associating the willingness of Chinese community residents to receive COVID-19 vaccine

Xu¹,

Zhu²

2022

Ann Palliat Med

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Background: Amid the coronavirus disease of 2019 (COVID-19) pandemic, China's vaccination campaign is progressing in an orderly manner. In the process of vaccination, the vaccination rates in different parts of China are different, and the factors affecting people's vaccination are also different, which may be caused by some reasons affecting people's willingness to vaccinate or complex sociodemographic characteristics factors.We found that inconsistent findings on factors associated with willingness to get COVID-19 vaccination in available studies. Therefore, we conducted a meta-analysis of current factors influencing people's willingness to receive COVID-19 vaccination to assess the associated factors influencing people's COVID-19 vaccination. Methods: The databases of CNKI, Chinese Biomedical Literature (CBM), Wanfang, VIP, PubMed, Web of Science, and Cochrane Library were searched by computer to collect the relevant literature on the factors affecting the willingness of Chinese community residents to undergo COVID-19 vaccination. After extracting the data, RevMan 5.3 and R software were used for statistical analysis. Population included in the study were Chinese community residents; outcome indicators were associated factors of willingness to get COVID-19 vaccination; COVID-19, odds ratio (OR), confidence interval (CI). Study designs were Crosssectional study. Egger's tests was used to check potential publication bias. Results:The willingness to get COVID-19 vaccination of community residents who think COVID-19 vaccine is effective is 4.10 times that of community residents who think COVID-19 vaccine is ineffective (OR =4.10, 95% CI: 3.08-5.46), and community residents who think COVID-19 vaccine is safe are 1.82 times more willing to receive COVID-19 vaccine than those who think COVID-19 vaccine is unsafe (OR =1.82, 95% CI:1.42-2.33); the willingness to get COVID-19 vaccination of community residents who think COVID-19 infection risk is high was 1.53 times that of community residents who think COVID-19 infection risk is low (OR =1.53, 95% CI: 1.43-1.64); the willingness of male community residents to vaccinate COVID-19 is 1.48 times higher than that of female community residents (OR =1.48, 95% CI: 1.23-1.76). Conclusions:The finding means that vaccination strategies need to be formulated according to the gender of community residents, propaganda of vaccination information, and dissemination of epidemic information to achieve higher levels of COVID-19 vaccination.

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Section: Discussionsupporting

confidence: 91%

A systematic review and meta-analysis of the factors associating the willingness of Chinese community residents to receive COVID-19 vaccine

Xu¹,

Zhu²

2022

Ann Palliat Med

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“…Sakatani et al [ 29 ] utilized a machine-learning approach to estimate human cerebral atrophy on the basis of metabolic status. Shiba et al [ 30 ] identified high risk factors for COVID-19 infection and hospitalization utilizing UK biobank data with machine-learning-based analysis. In addition, there are several previous works applying machine-learning methods for diagnosing NAFLD by utilizing electronic medical records or biochemical variables ( Table 5 ).…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.

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“…Depending on the availability of data it might be worthy to test the effect of gender using a composite measure as was performed by GENESIS-PRAXY Investigators [9,10]. The methodology of constructing the gender score has been validated since the inception in cohorts from retrospective and prospective studies [11][12][13][14][15][16][17][18][19]. Furthermore, international scientists have been supporting the assessment of even one domain or few individual gender-related factors depending on the availability of data [20][21][22].…”

Section: Discussionmentioning

confidence: 99%

“…The applicability of the GENESIS-PRAXY methodology using retrospective cohorts have been tested in other contexts [11,12] and systematically incorporated as a potential tool in the application of the 'Gender Outcomes International Group: to Further Well-being Development' (GOING-FWD framework [13][14][15][16][17][18][19]. The GOING-FWD methodology has led to understand more clearly the effect of gender on outcomes also in population-based health surveys and in diverse clinical scenarios.…”

Section: What (And How) Gender-related Variables-gender Core Datasetmentioning

confidence: 99%

“…The GOING-FWD methodology has led to understand more clearly the effect of gender on outcomes also in population-based health surveys and in diverse clinical scenarios. The most intriguing finding is that the genderrelated factors included in the gender score measure vary by countries underlying the cultural and country-specific nature of gender [15][16][17][18][19].…”

Section: What (And How) Gender-related Variables-gender Core Datasetmentioning

confidence: 99%

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The SIMI Gender ‘5 Ws’ Rule for the integration of sex and gender-related variables in clinical studies towards internal medicine equitable research

Raparelli

Santilli²,

Marra³

et al. 2022

Intern Emerg Med

Self Cite

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Biological sex and sociocultural gender matter when it comes to health and diseases. They have been both proposed as the undeniable gateways towards a personalized approach in care delivery. The Gender Working Group of the Italian Society of Internal Medicine (SIMI) was funded in 2019 with the aim of promoting good practice in the integration of sex and gender domains in clinical studies. Starting from a narrative literature review and based on regular meetings which led to a shared virtual discussion during the national SIMI congress in 2021, the members of the WG provided a core operational framework to be applied by internal medicine (IM) specialists to understand and implement their daily activity as researchers and clinicians. The SIMI Gender ‘5 Ws’ Rule for clinical studies has been conceptualized as follows: Who (Clinical Internal Medicine Scientists and Practitioners), What (Gender-related Variables—Gender Core Dataset), Where (Clinical Studies/Translational Research), When (Every Time It Makes Sense) and Why (Explanatory Power of Gender and Opportunities). In particular, the gender core dataset was identified by the following domains (variables to collect accordingly): relations (marital status, social support, discrimination); roles (occupation, caregiver status, household responsibility, primary earner, household dimension); institutionalized gender (education level, personal income, living in rural vs urban areas); and gender identity (validated questionnaires on personality traits). The SIMI Gender ‘5 Ws’ Rule is a simple and easy conceptual framework that will guide IM for the design and analysis of clinical studies.

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Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data

Cited by 13 publications

References 42 publications

A systematic review and meta-analysis of the factors associating the willingness of Chinese community residents to receive COVID-19 vaccine

A systematic review and meta-analysis of the factors associating the willingness of Chinese community residents to receive COVID-19 vaccine

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

The SIMI Gender ‘5 Ws’ Rule for the integration of sex and gender-related variables in clinical studies towards internal medicine equitable research

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