scholarly journals Quantification of Potential Inequities in Breast Cancer Incidence in New Mexico Through Bayesian Disease Mapping

2021 ◽  
Vol 18 ◽  
Author(s):  
David Zahrieh ◽  
Michael A. Golafshar ◽  
Samir H. Patel ◽  
Todd A. DeWees
Keyword(s):  
Breast Cancer ◽  
New Mexico ◽  
Cancer Incidence ◽  
Breast Cancer Incidence ◽  
Disease Mapping ◽  
Bayesian Disease Mapping

Quantification of inequities in breast cancer incidence in New Mexico through Bayesian disease mapping.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13617-e13617
Author(s):  
David Zahrieh ◽  
Michael A Golafshar ◽  
Samir H Patel ◽  
Todd A DeWees
Keyword(s):  
Breast Cancer ◽  
At Risk ◽  
New Mexico ◽  
Health Equity ◽  
Rate Ratio ◽  
Disease Mapping ◽  
Risk Population ◽  
Posterior Mean ◽  
Adjusted Rate ◽  
Bayesian Disease Mapping

e13617 Background: Breast cancer (BC) is the most prevalent cancer of women in the United States (US). The incidence rates for BC among American Indian and Alaska Native (AI/AN) women vary across the US. A novel application of Bayesian disease mapping was applied to quantify potential inequities in 10-year BC incidence in New Mexico (NM) in order to better inform health equity initiatives within the AI/AN at-risk population. Methods: Surveillance, Epidemiology, and End Results Program (SEER) data from 2005 to 2014 were used to identify new cases of BC within the 33 counties in NM. Initially, a Poisson-gamma model was applied to quantify the reduction in risk of BC within the at-risk AI/AN population compared with the general at-risk population. To account for spatial variation and to address the small area estimation problem inherent in these data by borrowing strength globally and locally in NM, we applied Bayesian disease mapping to the counts of county-level BC cases. We quantified the disparity effect, as measured by the rate ratio (95% credible interval [CI]), comparing the incidence of BC between at-risk AI/AN and non-AI/AN women, and assessed if the rate ratio differed between counties. Markov chain Monte Carlo sampling was used to estimate posterior quantities and the deviance information criterion was used for model selection. Results: In 2010, 1,041,758 women were at-risk for BC of which 107,656 (10.3%) were AI/AN women. During the 10-year study period, 12,974 new BC cases were recorded in the general at-risk population. In the at-risk AI/AN population, the expected number of new cases during the 10-year study period, therefore, was 1,340.74; however, 597 incidence cases of BC were diagnosed in the at-risk AI/AN population resulting in a posterior mean for the true relative risk of 0.445 (95% CI: 0.410, 0.482). Based on the selected model that accounted for over dispersion and spatial correlation among the 33 counties, the posterior mean of the overall adjusted rate ratio was 0.405 (95% CI: 0.336, 0.478). The adjusted rate of BC in AI/AN women was 0.40 times the corresponding adjusted rate for women who were non-AI/AN. Further, the adjusted rate ratios were similar for each county. Conclusions: The novel application of Bayesian disease mapping to these data provided substantial evidence of a significant overall disparity effect in BC incidence within NM between at-risk AI/AN and non-AI/AN women, which was more marked than previous reports. Targeted state-wide health equity initiatives may lead to reducing disparities in BC incidence in AI/AN at-risk women.

HUBUNGAN ANTARA UMUR DAN STATUS PERKAWINAN DENGAN KEJADIAN KANKER PAYUDARA DI INSTALASI BEDAH RUMAH SAKIT UMUM PUSAT DR. MOHAMMAD HOESIN PALEMBANG TAHUN 2012

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Rini Mayasari Rini Mayasari
Keyword(s):  
Breast Cancer ◽  
Marital Status ◽  
Cancer Incidence ◽  
Random Sampling ◽  
Breast Cancer Incidence ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional ◽  
Systematic Random Sampling ◽  
Breast Cancer Event

ABSTRACT Breast cancer on the mark with the growth or uncontrolled growth of cells that are excessive. Factors affecting the incidence of breast cancer include maternal age, marital status, family history, hormone use, radiation, manarche, and obesity. The purpose of this study was to determine the relationship between Age and Marital Status of Mother with Breast Cancer incidence in the Installation Surgeon General Hospital Dr. Mohammad Hoesin Palembang in 2012. The study design was analytical descriptive with the approach in which the independent variables Cross Sectional Age Mother and Status Perkawianan and dependent variables in breast cancer incidence collected at the same time. The study population was all women who develop breast cancer in the Installation General Hospital Surgery Center Dr. Mohammad Hoesin Palembang in 2012 and samples taken by systematic random sampling with a sample of 130 respondents. The results of univariate analysis of data showed that respondents had breast cancer with a percentage of total 58 (44.61%) while respondents who had not had breast cancer totaled 72 by the percentage (63.39%). Respondents by Age Mothers who are at risk amounted to 34 by the percentage (26.15%) while respondents with Age Mothers who are not at risk amounted to 96 by the percentage (73.85%). Respondents by Marital Status is married and the mother who totaled 69 by the percentage (53.07%) and Marital Status with the status of unmarried women totaled 61 by the percentage (46.03%). Of Chi-Square test results found no significant relationship between Age Mothers with Breast Cancer event in which the p value = 0.007, no significant relationship between Marital Status in Breast Cancer event in which the p value = 0.030. Based on the results of the study, researchers suggest counseling efforts, medical knowledge and improve the quality of health services in order to reduce the incidence of breast cancer..   ABSTRAK Kanker payudara di tandai dengan pertumbuhan atau perkembangan tidak terkontrol dari sel-sel yang berlebihan. Faktor-faktor yang mempengaruhi kejadian kanker payudara antara lain umur ibu, status perkawinan, riwayat keluarga, penggunaan hormon, radiasi, manarche, dan obesitas. Tujuan penelitian ini adalah untuk mengetahui hubungan antara Umur Ibu dan Status Perkawinan dengan kejadian Kanker Payudara di Instalasi Bedah Rumah Sakit Umum Pusat Dr. Mohammad Hoesin Palembang Tahun 2012. Desain Penelitian ini adalah deskriptif analitik dengan pendekatan Cross Sectional dimana variabel independen Umur Ibu dan Status Perkawianan dan variabel dependen kejadian kanker payudara dikumpulkan dalam waktu yang bersamaan. Populasi penelitian ini adalah semua ibu yang menderita kanker payudara di Instalasi Bedah Rumah Sakit Umum Pusat Dr. Mohammad Hoesin Palembang Tahun 2012 dan sampel di ambil secara systematic random sampling dengan jumlah sampel 130 responden. Hasil analisis univariat data menunjukan responden yang mengalami Kanker Payudara berjumlah 58 dengan persentase sebesar (44,61%) sedangkan responden yang tidak mengalami Kanker Payudara berjumlah 72 dengan persentase (63,39%). Responden dengan Umur Ibu yang beresiko berjumlah 34 dengan persentase (26,15%) sedangkan responden dengan Umur Ibu yang tidak beresiko berjumlah 96 dengan persentase (73,85%). Responden dengan Status Perkawinan ibu yang berstatus kawin berjumlah 69 dengan persentase (53,07%) dan Status Perkawinan ibu yang berstatus tidak kawin berjumlah 61 dengan persentase (46,03%). Dari hasil uji Chi-Square didapatkan ada hubungan bermakna antara Umur Ibu dengan kejadian Kanker Payudara dimana p Value = 0,007, ada hubungan bermakna antara Status Perkawinan dengan kejadian Kanker Payudara dimana p Value = 0,030. Berdasarkan hasil penelitian, peneliti menyarankan upaya-upaya penyuluhan, pengetahuan tenaga medis dan meningkatkan mutu pelayanan kesehatan guna menurunkan angka kejadian kanker payudara.

scholarly journals Impact of reproductive factors on breast cancer incidence: Pooled analysis of nine cohort studies in Japan

2021 ◽  
Vol 10 (6) ◽  
pp. 2153-2163
Author(s):  
Taro Takeuchi ◽  
Yuri Kitamura ◽  
Tomotaka Sobue ◽  
Mai Utada ◽  
Kotaro Ozasa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Studies ◽  
Cancer Incidence ◽  
Breast Cancer Incidence ◽  
Reproductive Factors ◽  
Pooled Analysis
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