scholarly journals A Mobile Health Breast Cancer Educational and Screening Intervention Tailored for Low-Income, Uninsured Latina Immigrants

2021 ◽  
Vol 2 (1) ◽  
pp. 325-336
Author(s):  
Maria De Jesus ◽  
Shalini Ramachandra ◽  
Alexis De Silva ◽  
Shirley Liu ◽  
Ethan Dubnansky ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mobile Health ◽  
Low Income ◽  
Latina Immigrants

scholarly journals Identifying Mobile Health Technology Experiences and Preferences of Low-Income Pregnant Women with Diabetes

2021 ◽  
pp. 193229682199317
Author(s):  
Karolina Leziak ◽  
Eleanor Birch ◽  
Jenise Jackson ◽  
Angelina Strohbach ◽  
Charlotte Niznik ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Mobile Health ◽  
Low Income ◽  
Self Regulation ◽  
Health Inequities ◽  
Comparison Method ◽  
Rapid Expansion ◽  
Self Management ◽  
Postpartum Women ◽  
Design Elements

Background: Rapid expansion of mobile technology has resulted in the development of many mobile health (“mHealth”) platforms for health monitoring and support. However, applicability, desirability, and extent of tailoring of these platforms for pregnant women, particularly in populations who experience the greatest health inequities—such as women with diabetes mellitus (DM) and/or those with greater socioeconomic barriers—remains unknown. The objective is to understand low-income pregnant women’s experiences and preferences for mHealth tools to support DM health and improve DM self-management during pregnancy. Methods: Low-income pregnant and postpartum women were included in individual interviews or focus groups; women with type 2 DM, gestational DM, or no DM were included. Analysis was performed with the constant comparison method. Results: In this population of 45 ( N=37 with DM) low-income, largely minority, pregnant and postpartum women, 100% reported access to smartphones and prior experience with apps. Interest in mHealth to support health and engagement during pregnancy was high. Preferences for general mHealth features included education that reduces uncertainty, support communities, visualizing progress, convenient access to information, and support for better management of pregnancy-related tasks. Preferred design elements included personalization, interactive features, and integrated graphics. Women with DM expressed multiple additional DM-specific needs, including support tools for DM self-management and self-regulation tasks. Conclusion: Pregnant and postpartum women, especially those with DM, desire mHealth technology to support engagement and to adapt lifestyle guidelines and treatment requirements for a healthy pregnancy. Further work to develop mHealth interventions tailored for target populations remains a key step in reducing health inequities and promoting access to evidence-based perinatal health interventions.

scholarly journals Breast Cancer in Low- and Middle-Income Countries: An Emerging and Challenging Epidemic

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Arafat Tfayli ◽  
Sally Temraz ◽  
Rachel Abou Mrad ◽  
Ali Shamseddine
Keyword(s):  
Breast Cancer ◽  
Health Care ◽  
Low Income ◽  
Therapeutic Interventions ◽  
Low Income Countries ◽  
Middle Income ◽  
Major Health ◽  
Middle Income Countries ◽  
Low And Middle Income

Breast cancer is a major health care problem that affects more than one million women yearly. While it is traditionally thought of as a disease of the industrialized world, around 45% of breast cancer cases and 55% of breast cancer deaths occur in low and middle income countries. Managing breast cancer in low income countries poses a different set of challenges including access to screening, stage at presentation, adequacy of management and availability of therapeutic interventions. In this paper, we will review the challenges faced in the management of breast cancer in low and middle income countries.

scholarly journals Multidisciplinary Approach to Neoadjuvant Endocrine Therapy in Breast Cancer: A Comprehensive Review

2016 ◽  
Vol 38 (12) ◽  
pp. 615-622 ◽  
Author(s):  
Tomás Reinert ◽  
Susana Ramalho ◽  
Rodrigo Gonçalves ◽  
Carlos Barrios ◽  
Marcia Graudenz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endocrine Therapy ◽  
Low Income ◽  
Hormone Receptor ◽  
Urban Areas ◽  
Low Cost ◽  
Locally Advanced Breast Cancer ◽  
Locally Advanced ◽  
Breast Cancers ◽  
Neoadjuvant Endocrine Therapy

AbstractBreast cancer is the most common type of cancer and the leading cause of cancer-related death among women worldwide. Hormone receptor-positive (HR+) tumors represent the most common form of this disease, with more than 70% of breast cancers expressing these receptors. Response and benefit to neoadjuvant chemotherapy (NCT) varies according to HR expression, with lower responses in luminal tumors as compared with hormone receptor-negative (HR-) and human epidermal growth factor receptor 2-positive (HER2+) tumors. Neoadjuvant endocrine therapy (NET) is an option for selected patients with HR+ locally advanced breast cancer. Neoadjuvant endocrine therapy has a favorable toxicity profile, and is associated with benefits such as having low cost and being more easily available even for cancer care professionals outside major urban areas or tertiary centers. These factors are particularly relevant, as 70% of breast cancer deaths occur in women from low-income and middle-income countries. Additionally, NET is being increasingly explored, not simply to allow for less extensive surgery, but also as a scientific tool, with the use of biomarkers to predict outcomes in adjuvant trials and for the individual patient. This review details the current and most relevant evidence about NET for breast cancer as well as the future directions of this field.

Impact of Medicaid expansion on two-year mortality among stage IV breast cancer (BC) patients according to race.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6525-6525
Author(s):  
Catalina Malinowski ◽  
Xiudong Lei ◽  
Hui Zhao ◽  
Sharon H. Giordano ◽  
Mariana Chavez Mac Gregor
Keyword(s):  
Breast Cancer ◽  
Low Income ◽  
Stage Iv ◽  
Medicaid Expansion ◽  
Risk Of Death ◽  
Stage Iv Breast Cancer ◽  
Increased Risk ◽  
The Impact ◽  
Expansion Period ◽  
White Patients

6525 Background: Inadequate access to healthcare services is associated with worse outcomes. Disparities in access to cancer care are more frequently seen among racial/ethnic minorities, uninsured patients, and those with low socioeconomic status. A provision in the Affordable Care Act called for expansion of Medicaid eligibility in order to cover more low-income Americans. In this study, we evaluate the impact of Medicaid expansion in 2-year mortality among metastatic BC patients according to race. Methods: Women (aged 40-64) diagnosed with metastatic BC (stage IV de novo) between 01/01/2010 and 12/31/2015 and residing in states that underwent Medicaid expansion in 01/2014 were identified in the National Cancer Database. For comparison purposes, 2010-2013 was considered the pre-expansion period and 2014-2015 the post-expansion period. We calculated 2-year mortality difference-in-difference (DID) estimates between White and non-White patients using multivariable linear regression models. Results are presented as adjusted differences (in % points) between groups in the pre- and post-expansion periods and as adjusted DID with 95%CI. Covariates included age, comorbidity, BC subtype, insurance type, transfer of care, distance to hospital, region, residence area, education, income quartile, facility type and facility volume. In addition, overall survival (OS) was evaluated in pre- and post-expansion periods via Kaplan-Meier method and Cox proportional hazards models; results are presented as 2-year OS estimates, hazard ratios (HRs), and 95% CIs. Results: Among 7,675 patients included, 4,942 were diagnosed in the pre- and 2,733 in the post-expansion period. We observed a reduction in 2-year mortality rates in both groups according to Medicaid expansion. Among Whites 2-year mortality decreased from 42.5% to 38.7% and among non-Whites from 45.4% to 36.4%, resulting in an adjusted DID of -5.2% (95%CI -9.8 to -0.6, p = 0.027). A greater reduction in 2-year mortality was observed among non-Whites in a sub-analysis of patients who resided in the poorest quartile (n = 1372), with an adjusted DID of -14.6% (95%CI -24.8 to -4.4, p = 0.005). In the multivariable Cox model, during the pre-expansion period there was an increased risk of death for non-Whites compared to Whites (HR 1.14, 95% CI 1.03 to 1.26, P = 0.04), however no differences were seen in the post-expansion period between the two groups (HR 0.93, 95% CI 0.80 to 1.07, P = 0.31). Conclusions: Medicaid expansion reduced racial disparities by decreasing the 2-year mortality of non-White patients with metastatic breast cancer and reducing the gap when compared to Whites. These results highlight the positive impact of policies aimed at improving equity and increasing access to health care.

scholarly journals Prediction of benign and malignant breast cancer using data mining techniques

2018 ◽  
Vol 12 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal ◽  
BB Tiwari
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Low Income ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Low Income Countries ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Rbf Network

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

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