Browsing by Subject "Women's health"

Now showing 1 - 7 of 7
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    Big Data and Dysmenorrhea: What Questions Do Women and Men Ask About Menstrual Pain?
    (Mary Ann Liebert, 2018-10) Chen, Chen X.; Groves, Doyle; Miller, Wendy R.; Carpenter, Janet S.; School of Nursing
    BACKGROUND: Menstrual pain is highly prevalent among women of reproductive age. As the general public increasingly obtains health information online, Big Data from online platforms provide novel sources to understand the public's perspectives and information needs about menstrual pain. The study's purpose was to describe salient queries about dysmenorrhea using Big Data from a question and answer platform. MATERIALS AND METHODS: We performed text-mining of 1.9 billion queries from ChaCha, a United States-based question and answer platform. Dysmenorrhea-related queries were identified by using keyword searching. Each relevant query was split into token words (i.e., meaningful words or phrases) and stop words (i.e., not meaningful functional words). Word Adjacency Graph (WAG) modeling was used to detect clusters of queries and visualize the range of dysmenorrhea-related topics. We constructed two WAG models respectively from queries by women of reproductive age and bymen. Salient themes were identified through inspecting clusters of WAG models. RESULTS: We identified two subsets of queries: Subset 1 contained 507,327 queries from women aged 13-50 years. Subset 2 contained 113,888 queries from men aged 13 or above. WAG modeling revealed topic clusters for each subset. Between female and male subsets, topic clusters overlapped on dysmenorrhea symptoms and management. Among female queries, there were distinctive topics on approaching menstrual pain at school and menstrual pain-related conditions; while among male queries, there was a distinctive cluster of queries on menstrual pain from male's perspectives. CONCLUSIONS: Big Data mining of the ChaCha® question and answer service revealed a series of information needs among women and men on menstrual pain. Findings may be useful in structuring the content and informing the delivery platform for educational interventions.
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    Cost-effectiveness of low-dose aspirin for the prevention of preterm birth: a prospective study of the Global Network for Women's and Children's Health Research
    (Elsevier, 2023) Patterson, Jackie K.; Neuwahl, Simon; Goco, Norman; Moore, Janet; Goudar, Shivaprasad S.; Derman, Richard J.; Hoffman, Matthew; Metgud, Mrityunjay; Somannavar, Manjunath; Kavi, Avinash; Okitawutshu, Jean; Lokangaka, Adrien; Tshefu, Antoinette; Bose, Carl L.; Mwapule, Abigail; Mwenechanya, Musaku; Chomba, Elwyn; Carlo, Waldemar A.; Chicuy, Javier; Figueroa, Lester; Krebs, Nancy F.; Jessani, Saleem; Saleem, Sarah; Goldenberg, Robert L.; Kurhe, Kunal; Das, Prabir; Patel, Archana; Hibberd, Patricia L.; Achieng, Emmah; Nyongesa, Paul; Esamai, Fabian; Bucher, Sherri; Liechty, Edward A.; Bresnahan, Brian W.; Koso-Thomas, Marion; McClure, Elizabeth M.; Pediatrics, School of Medicine
    Background: Premature birth is associated with an increased risk of mortality and morbidity, and strategies to prevent preterm birth are few in number and resource intensive. In 2020, the ASPIRIN trial showed the efficacy of low-dose aspirin (LDA) in nulliparous, singleton pregnancies for the prevention of preterm birth. We sought to investigate the cost-effectiveness of this therapy in low-income and middle-income countries. Methods: In this post-hoc, prospective, cost-effectiveness study, we constructed a probabilistic decision tree model to compare the benefits and costs of LDA treatment compared with standard care using primary data and published results from the ASPIRIN trial. In this analysis from a health-care sector perspective, we considered the costs and effects of LDA treatment, pregnancy outcomes, and neonatal health-care use. We did sensitivity analyses to understand the effect of the price of the LDA regimen, and the effectiveness of LDA in reducing both preterm birth and perinatal death. Findings: In model simulations, LDA was associated with 141 averted preterm births, 74 averted perinatal deaths, and 31 averted hospitalisations per 10 000 pregnancies. The reduction in hospitalisation resulted in a cost of US$248 per averted preterm birth, $471 per averted perinatal death, and $15·95 per disability-adjusted life year. Interpretation: LDA treatment in nulliparous, singleton pregnancies is a low-cost, effective treatment to reduce preterm birth and perinatal death. The low cost per disability-adjusted life year averted strengthens the evidence in support of prioritising the implementation of LDA in publicly funded health care in low-income and middle-income countries.
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    Dermatographism with vulvar symptoms
    (Elsevier, 2021-05-05) Rivera, Sydney; Mirowski, Ginat W.; Dermatology, School of Medicine
    Dermatographism (DG) is characterized by a localized, inducible, wheal-and-flare response along the distribution of mechanical pressure. We report an illustrative case of DG with vulvar symptoms (DG-VS) and review the literature on this rarely recognized but easily treated etiology of vulvar complaints. A 35-year-old woman presented with a 1-year history of vulvar pruritus unresponsive to antifungal, antibacterial, and steroid treatments. A prior punch biopsy was nondiagnostic. Vulvar examination revealed normal architecture and no cutaneous abnormalities. She was markedly dermatographic with a scratch test. DG-VS was diagnosed. The patient achieved complete symptomatic control on low-dose hydroxyzine. She maintains excellent control at 3.5 years. In the literature, a typical patient with DG-VS is of reproductive age, with several years' history of vulvar symptoms (itching, burning, pain, or swelling) and repeated empiric treatment for infectious/inflammatory etiologies. Exacerbation with sexual activity, menstruation, or wearing tight clothing is characteristic and supports the role of mechanical pressure in inducing focal symptoms. Dermatologic changes to the vulvar skin are rarely noted. DG-VS is diagnosed based on clinical findings, symptom patterns, and a positive scratch test and is treated with antihistamines. DG-VS remains absent from current vulvar disease guidelines. In the complex world of vulvar pain and itch, an etiology so easily screened for and readily treated warrants consideration.
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    Group Dynamics and Allocation of Advanced Heart Failure Therapies-Heart Transplants and Ventricular Assist Devices-By Gender, Racial, and Ethnic Group
    (American Heart Association, 2023) Breathett, Khadijah; Yee, Ryan; Pool, Natalie; Thomas Hebdon, Megan C.; Knapp, Shannon M.; Herrera-Theut, Kathryn; de Groot, Esther; Yee, Erika; Allen, Larry A.; Hasan, Ayesha; Lindenfeld, JoAnn; Calhoun, Elizabeth; Carnes, Molly; Sweitzer, Nancy K.; Medicine, School of Medicine
    Background: US regulatory framework for advanced heart failure therapies (AHFT), ventricular assist devices, and heart transplants, delegate eligibility decisions to multidisciplinary groups at the center level. The subjective nature of decision‐making is at risk for racial, ethnic, and gender bias. We sought to determine how group dynamics impact allocation decision‐making by patient gender, racial, and ethnic group. Methods and Results: We performed a mixed‐methods study among 4 AHFT centers. For ≈ 1 month, AHFT meetings were audio recorded. Meeting transcripts were evaluated for group function scores using de Groot Critically Reflective Diagnoses protocol (metrics: challenging groupthink, critical opinion sharing, openness to mistakes, asking/giving feedback, and experimentation; scoring: 1 to 4 [high to low quality]). The relationship between summed group function scores and AHFT allocation was assessed via hierarchical logistic regression with patients nested within meetings nested within centers, and interaction effects of group function score with gender and race, adjusting for patient age and comorbidities. Among 87 patients (24% women, 66% White race) evaluated for AHFT, 57% of women, 38% of men, 44% of White race, and 40% of patients of color were allocated to AHFT. The interaction between group function score and allocation by patient gender was statistically significant (P=0.035); as group function scores improved, the probability of AHFT allocation increased for women and decreased for men, a pattern that was similar irrespective of racial and ethnic groups. Conclusions: Women evaluated for AHFT were more likely to receive AHFT when group decision‐making processes were of higher quality. Further investigation is needed to promote routine high‐quality group decision‐making and reduce known disparities in AHFT allocation.
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    Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort
    (Elsevier, 2021) Tison, Geoffrey H.; Avram, Robert; Nah, Gregory; Klein, Liviu; Howard, Barbara V.; Allison, Matthew A.; Casanova, Ramon; Blair, Rachael H.; Breathett, Khadijah; Foraker, Randi E.; Olgin, Jeffrey E.; Parikh, Nisha I.; Medicine, School of Medicine
    Background: Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI). Methods: We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years. Results: LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76). Conclusions: Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.
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    Response to Comment on Dunne et al. The Women's Leadership Gap in Diabetes: A Call for Equity and Excellence. Diabetes Care 2021;44:1734-1743
    (American Diabetes Association, 2022) Dunne, Jessica L.; Maizel, Jennifer L.; Posgai, Amanda L.; Atkinson, Mark A.; DiMeglio, Linda A.; Pediatrics, School of Medicine