Challenges and Implications for Menopausal Health and Help-Seeking Behaviors in Midlife Women From the United States and China in Light of the COVID-19 Pandemic: Web-Based Panel Surveys

Lau, Bobo Hi Po; Tang, Catherine So Kum; Holroyd, Eleanor; Wong, William Chi Wai

doi:10.2196/46538

Cited by 3 publications

(1 citation statement)

References 61 publications

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“…This approach will aid in identifying patient subgroups with distinct clinical profiles, which can help in designing targeted interventions and personalized care. Also, more research is needed to understand the direct impact of COVID-19 on midlife women’s help-seeking behaviors related to menopause specifically [ 47 ]. As a future work we are also planning to use Machine Learning to identify patterns in healthcare seeking behaviors before and after COVID-19 diagnosis in midlife women.…”

Section: Discussionmentioning

confidence: 99%

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Benny,

Giacobini,

Costa

et al. 2024

BMC Med Res Methodol

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Background Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions. Methods The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling. Results From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68). Conclusion Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

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Section: Discussionmentioning

confidence: 99%

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Benny,

Giacobini,

Costa

et al. 2024

BMC Med Res Methodol

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Humanization promotes equality over efficiency preference in carbon allocation

Huang,

Liu,

Dang

et al. 2024

Journal of Environmental Psychology

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“Internet+” pharmacy in psychiatric hospital amid COVID-19 and post-pandemic period: analysis and development

Xie,

Wang,

Qian

et al. 2024

Front. Psychiatry

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ObjectiveThis study aims to explore the differences in “Internet+” pharmacy prescriptions in psychiatric hospitals before and after the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. It also seeks to examine changes in patient healthcare behaviors in the post-pandemic era and to identify the potential role of “Internet+” pharmacy in improving the current healthcare system.MethodsPrescriptions from the “Internet+” pharmacy at The Affiliated Mental Health Center of Jiangnan University, collected between December 1, 2021, and November 30, 2023, were analyzed. The period was divided into four stages based on the COVID-19 pandemic’s progression in China. Descriptive statistical analysis was conducted on various prescription-related factors, including patient information, prescription type, disease distribution, medication type, frequency of medication use, pharmacist review time, and instances of irrational medication use.ResultsA total of 2914 prescriptions were collected. The male-to-female ratio (MFR) varied significantly across different stages of the epidemic. In the pre-pandemic II period, females represented the highest proportion (66.10%, MFR 0.51), and individuals aged 18-39 made up the majority (56.70%) across all stages. The proportion of psycho-counseling prescriptions was highest in the pre-pandemic II period (76.74%), while the total number of psycho-counseling prescriptions was greatest during the epidemic, with 798 cases. A total of 38 diseases were involved, with depression accounting for the largest proportion (38.98%) at each stage, followed by the highest usage of antidepressants (49.60%). A total of 85 types of medications were used, with quetiapine representing the highest proportion before the epidemic (16.56%, 10.92%), while escitalopram accounted for the highest proportion after the epidemic (10.98%). The majority of patients (70.87%) took medication once daily. 42.23% of pharmacist review times were ≤1 minute, and the mean review time was longest in the post-pandemic period (6175.1 seconds). During the pre-pandemic and epidemic periods, the most common pharmacist review time occurred between 12:00 and 17:59 (41.46%), while in the post-pandemic period, the most common review time was between 18:00 and 23:59 (36.70%). The initial rate of irrational prescriptions was 37.85%. After manual review by pharmacists, the irrational prescription rate of Internet prescriptions decreased to 1.13%.ConclusionThe development of “Internet+” pharmacy has effectively addressed the medical needs of the relevant population and played a crucial role in combating the COVID-19 pandemic. Future advancements should focus on optimizing the allocation of healthcare resources and expanding innovative pharmacy services to broaden the developmental pathways of the ‘Internet+’ pharmacy ecosystem.

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Challenges and Implications for Menopausal Health and Help-Seeking Behaviors in Midlife Women From the United States and China in Light of the COVID-19 Pandemic: Web-Based Panel Surveys

Cited by 3 publications

References 61 publications

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Humanization promotes equality over efficiency preference in carbon allocation

“Internet+” pharmacy in psychiatric hospital amid COVID-19 and post-pandemic period: analysis and development

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