Understanding the risk of breast cancer from population and geographic perspective

Understanding the risk of breast cancer from population and geographic perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the risk of breast cancer from population and geographic perspective Chenchang Xiao, Juan Chen, Kai Wang, Yunan Xu, Akemi Wijayabahu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5426841/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background Breast cancer is one of the most significant public health challenges in the United States. This study aims to deepen the understanding of burden of breast cancer from a four-dimensional view based on population and geographic information to inform better evidence-based decision making and resource optimization. Methods Data of breast cancer in the study were derived from National Cancer Institutes’ State Cancer Profiles, including total count and age-standardized prevalence of breast cancer by state. Four indicators were estimated, including total count, population-based P rate, geographic-based G rate and population and geographic-based PG rate. The free software R was used to do the geographic mapping to visualize the pattern of the four indicators across states. Results The top five states with the largest count of breast cancer were California, Florida, Texas, New York and Pennsylvania, informing the resources needed for treatment. The top five states with highest P rate were Maryland, Wyoming, Virginia, Wisconsin, and Oregon, while the top five states with highest G rate included New Jersey, Rhode Island, Massachusetts, Connecticut, and Maryland, revealing breast cancer risk from population and geographic perspective. When controlling for both population and geographic size, the states with highest PG rate were Rhode Island, Delaware, Connecticut, Hawaii, and New Jersey, presenting a declining trend of breast cancer burden from Northeast to Southwest. Conclusion This study added two indicators to the conventional measures of disease risk to incorporate the influence of geographic information, presenting a four-dimensional national pattern of breast cancer risk. Study findings will provide evidence informing better decision making for optimal resource allocation. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction 1.1 Breast cancer in the world and the United States Breast cancer is the most commonly diagnosed (11.6% of all cancers) and the second leading cause of death (6.6% of all death by cancers) among women worldwide (Bray et al., 2018 ; WHO, 2018 ). The burden of breast cancer is higher in developed countries while the extent of the burden is expanding in developing countries (Bray et al., 2018 ). In the United States (U.S.), breast cancer is the second leading cancer after lung cancer (CDC, 2018 ). According to the latest report from the Center for Disease Control and Prevention (CDC), the incidence rate of breast cancer was 124.8 per 100,000 with 242,476 new cases in 2015, and with a five-year prevalence rate of 606.7 per 100,000 with over one million women living with breast cancer (U.S. Cancer Statistics Working Group, 2017 ). Thus, prevention of breast cancer is of great significance in improving the nation’s health in general. 1.2 Public health efforts in breast cancer research and prevention Advancements in early detection methods, treatment options and population based interventions based on the copious amount of evidence on breast cancer has led to a decrease in breast cancer related mortality and increased survival after breast cancer diagnosis in the U.S. (Arab et al., 2019 ; de la Mare et al., 2014 ; Ju, Zhu, & Yuan, 2018 ; McCart Reed, Kalita-de Croft, Kutasovic, Saunus, & Lakhani, 2018 ; Peart, 2015 ). Despite the great achievements in prevention and treatment, health disparities of breast cancer still exist between different geographic areas. Previous research has shown geographic differences in breast cancer prevalence and mortality by state (National Cancer Institute, 2018; Sighoko et al., 2018 ). According to the latest State Cancer Profiles report, breast cancer incidence has increased in Connecticut, New Jersey, Kentucky, Louisiana, Alabama, Oklahoma (National Cancer Institute, 2018). These differences in mortality and morbidity could be attributed to the disproportionate distribution of genetically predisposing factors, disparities in access to early detection (mammography) and health care, and underlying lifestyle and behavioral risk factors (Samson, Porter, Hurley, Adams, & Eberth, 2016 ; Sighoko et al., 2018 ; Yedjou et al., 2017 ). However, when comparing the state differences, geographic information was not considered, leading to a biased understanding of differences of breast cancer epidemic in different states. More comprehensive understanding of the burden of breast cancer across different states is strongly needed to add new geographic information to inform better evidence-based decision making and optimal utilization of health resources. 1.3 Four-indicator system in understanding breast cancer Traditional epidemiological studies always use the prevalence rate to describe the epidemic of breast cancer. However, no data have been reported regarding the geographic information, particularly the geographic area size. Thus, a four-indicator system, including count, population-based rate (P rate), geographic-based rate (G rate) and population-geographic-based rate (PG rate), was used to better understand the risk of breast cancer (D.-G. Chen, 2017 ; X. Chen & Wang, 2017 ; X. Chen & Yu, 2018 ). This system was initially developed for infectious diseases, such as HIV, to better estimate the disease spread and to assess the need for resource allocation (X. Chen & Wang, 2017 ). Now we innovatively applied it to better understand chronic disease. Although disease spread is not of concern in chronic diseases, we can evaluate the need for resources by different geographical areas to optimize the resource allocation and use. First of all, it is important to know the total number of people living with breast cancer (count). These data could be used to estimate the total amount of money and health resources needed to prevent and treat breast cancer. For example, the average cost for one patient at the stage 0 of breast cancer was $ 60,637 in one year (Blumen, Fitch, & Polkus, 2016 ). If we assume all one million women living with breast cancer were at stage 0, then the total cost for one year would be $ 60.637 billion, accounting for the 6.06% of the GDP in the U.S. in 2017. The actual cost would be much greater if we take the advanced stages of breast cancer into account. The second indicator is the population-based rate (P rate), also known as prevalence rate. This is the most common indicator epidemiologists always use to understand and describe the risk of public health problems. It is measured as the number of breast cancer patients divided by the total population. For example, the count of breast cancer in California was 125,300 in 2017 and ranked first in the count, but when considering the large population, the prevalence rate would be 520 per 100,000 population after adjusted for age and ranked 48 in all 50 U.S. states. However, this indicator is limited by ignoring the influence of geographic area size. For example, Maryland and Wyoming have the same age-standardized prevalence rate of breast cancer (670 per 100,000 people for both), and Maryland has larger population size but smaller geographic area size. Thus, it would be much easier in Maryland to locate one breast cancer. On the other side, Wyoming would spend more money, time, travel distance and human efforts to screen one breast cancer case. P rate alone could not reflect these differences in Maryland and Wyoming, and is limited in understanding the risk of breast cancer and optimizing the resource allocation. To address the limitation of P rate, another two indicators were developed to incorporate the influence of geographic area size. The third indicator is the geographic-based rate, named as G rate. It is estimated as the count of breast cancer divided by the total geographic area size. For example, the count of breast cancer was 105,600 in Florida and 34,900 in New Jersey, and the geographic area size was 139,670 square kilometers and 19,209 for Florida and New Jersey, respectively. Thus, the G rate should be 756 cases per 1000 square kilometers in Florida, and 1817 per 1000 square kilometers in New Jersey. Given the same geographic area size, the risk of breast cancer would be 2.40 (1817/756) times higher in New Jersey than Florida. It would be much easier to locate one case in the states with high G rate, like New Jersey. While for states with low G rate, more resources and human efforts are needed to do the screening of breast cancer. The fourth indicator considers the impacts of both population and geographic area size, named as the population-geographic-based rate (PG rate). It could be estimated as the total number of breast cancer cases divided by the total population and total geographic size. For example, the count of breast cancer in Rhode Island was 4300 in 2017, the population was 543,323 and the geographic area size was 2707 square kilometers. Thus, the age adjusted PG rate would be 200 cases per 100,000 population and 1000 square kilometers. PG rate is a better indicator than count, P rate and G rate since it controls the confounding effects of both population and geographic area size, and can be used to compare across states with different size of population and geographic area. A system of the four-indicator of breast cancer (e.g. count, P rate, G rate and PG rate) may provide comprehensive information to understand and describe the breast cancer epidemic, and devise, plan and implement effective screening and treatment for breast cancer. 1.4 Purpose of the study The study aims to describe the epidemic of breast cancer by applying the four indicators, including total count, P rate, G rate and PG rate using the state specific data in the U.S. The ultimate goal is to provide a four-dimensional view of the national pattern of breast cancer epidemic, and add new data supporting the research and practice of breast cancer. 2. Materials and Methods 2.1 Data source and collection Total count and age-standardized prevalence rate of breast cancer The projected total count and age-standardized prevalence rate in 2017 were derived from the National Cancer Institutes’ State Cancer Profiles (National Cancer Institute, 2018). Information on data collection and calculation of prevalence has been previously described in detail (G. De Angelis, De Angelis, Frova, & Verdecchia, 1994 ; R. De Angelis et al., 2009 ). Briefly, the State Cancer Profiles has derived the breast cancer prevalence estimates from state-specific cancer mortality and survival data using “Mortality-Incidence Analysis MODEL (MIAMOD)” statistical package (National Cancer Institute, 2018). This model utilizes an age-period-cohort model on the logistic scale to estimate breast cancer incidence, which will be used in the prevalence calculations. Data were collected from National Center of Health Statistics, Surveillance, Epidemiology, and End Results Program (SEER) and US Census Bureau and for the age standardization U.S. Census population (2000) population data has been used. Geographic area size by state The geographic area size by state was derived from the designated website ( https://state.1keydata.com/states-by-size.php ). Only land area (excluding water) was used for analysis in the study. 2.2 Measurement Breast cancer count Breast cancer count is defined as the number of patients living with breast cancer. Count = total number of cases living with breast cancer (1000)………………………….…… (1) Breast cancer P rate P rate is the population-based rate, and it is the same as the prevalence rate. It was estimated as the total number of breast cancer cases divided by the total population. The age-standardized prevalence rate was used as P rate in the study. P rate = total number of breast cancer cases/total population (per 100,000 population)……… (2) Breast cancer G rate G rate, geographic area-based rate, is defined as the number of breast cancer cases divided by the total geographic area in each state. It is calculated using the Eq. (3). G rate = total number of breast cancer cases/total geographic area size (per 1000 km 2 )………. (3) Breast cancer PG rate PG rate, named as population-geographic area-based rate, is defined as the total number of breast cancer cases divided by the total population and total geographic area size in specific state. The Eq. (4) presents the way to calculate the PG rate. In the study, we computed the PG rate as the age-standardized prevalence rate divided by the geographic size. PG rate = total number of breast cancer cases/(total population*total geographic area size) (per 100,000 pop per 1000 km 2 )………………………………………………………………… (4) 2.3 Geographic mapping Geographic mapping in the study was conducted in five steps. First, the four indicators, including total count, P rate, G rate and PG rate, were estimated using the commercial software Microsoft Excel Version 2016 (Microsoft, Seattle, WA). A dataset named as “USBreastCancer” was created with the variables of the state name and the four indicators. Second, the free software R version 3.4.2 was used for the global mapping. Three packages were used in the mapping process, including “ggplot2”, “usmap”, and “RColorBrewer”. A dataset named as “USmap” was created by extracting geographic information (e.g. state name, longitude, latitude) of individual states in the U.S. from the package “usmap”. Third, another dataset named as “USBreastCancerMap” was created by merging “USBreastCancer” and “USmap” by the state name. Fourth, after the data preparation, a blank U.S. base map was created by using the packages “usmap” and “ggplot2”. In the last step, we used the package “RColorBrewer” to define the colors of the map with dark color representing the high level of breast cancer. Then we added a layer of one indicator to the base map using the package “ggplot2”, and finally generated four maps for breast cancer. The R code could be provided upon request. 3. Results 3.1 National pattern of the total count of breast cancer Results in Fig. 1 show the total count of breast cancer by state in the U.S. The state with largest number of people living with breast cancer was California, followed by Florida, Texas, New York, Pennsylvania, Illinois, Ohio, Michigan, North Carolina, and Virginia with a range from 35300 to 125300. The two to three columns of Table 1 listed the top 10 states with highest number of breast cancer. The overall pattern for the count map was that the states close to oceans and lakes had more breast cancer cases than the states in the inland. 3.2 National pattern of P rate of breast cancer Figure 2 presents the national pattern of P rate of breast cancer that adjusted for the population size. It provided a better measurement of the risk of breast cancer than the count. The top 10 states with highest P rate were Maryland, Wyoming, Virginia, Wisconsin, Oregon, Michigan, Hawaii, Florida, Washington and Oklahoma ranging from 630 to 670 per 100,000 population (4–5 columns in Table 1 ). Compared to Fig. 1 , the states with top counts of breast cancer but with large population size did not rank top for P rate, such as California, Texas, and New York. The states in the inland ranked high in P rate due to the small population size compared to the states close to the coastal line, such as Wyoming, Utah, Colorado, Kansas, Arizona, and Oklahoma. 3.3 National pattern of G rate of breast cancer The results in Fig. 3 show the national pattern of G rate of breast cancer. When the geographic area size was considered, the map presented different information from the count and P rate. The top 10 states with highest G rate of breast cancer included New Jersey, Rhode Island, Massachusetts, Connecticut, Maryland, Delaware, Florida, New York, Pennsylvania and Ohio. The values for these states ranged from 430 to 1817 cases per 1000 km 2 . Compared to count, states with small geographic area size had higher G rate than states with large geographic area size, such as New Jersey, Maryland, and Delaware. 3.4 National pattern of the PG rate of breast cancer Results in Fig. 4 considered both the population and geographic information. The pattern of PG rate showed a declining trend from the Northeast (e.g. Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, and New Jersey) to the Southwest (e.g. California, Arizona, New Mexico and Texas). States in the East and North had higher PG rate than the states in the West and South. Results in the 8–9 columns of Table 1 listed the top 10 states with highest PG rate, including Rhode Island, Delaware, Connecticut, Hawaii, New Jersey, Massachusetts, Vermont, New Hampshire, Maryland, and West Virginia with a range of 8-200 per 100,000 population and 1000 km 2 . Table 1 Top 10 states of the four indicators of breast cancer Rank Count P rate (per 100,000 pop) G rate (Per 1000 km 2 ) PG rate (per 100,000 pop 1000 km 2 ) 1 California 125300 Maryland 670 New Jersey 1817 Rhode Island 200 2 Florida 105600 Wyoming 670 Rhode Island 1589 Delaware 117 3 Texas 79200 Virginia 660 Massachusetts 1384 Connecticut 45 4 New York 69700 Wisconsin 660 Connecticut 1203 Hawaii 38 5 Pennsylvania 52100 Oregon 660 Maryland 1043 Massachusetts 29 6 Illinois 46400 Michigan 650 Delaware 790 New Jersey 28 7 Ohio 45600 Hawaii 640 Florida 756 Maryland 26 8 Michigan 43500 Florida 630 New York 570 New Hampshire 24 9 North Carolina 37900 Washington 630 Pennsylvania 449 Vermont 23 10 Virginia 35300 Oklahoma 630 Ohio 430 West Virginia 8 Table 2 Results of linear regression of Influential factors of the four indicators of breast cancer Variables Count P rate G rate PG rate Percentage of White -691.5** -0.19 -4.94 -0.08 Divorce rate -716.8 4.32 -196.5 -7.68 Obesity prevalence -1161.0 -1.63 -27.83 -0.92 Cigarette smoke in past month -2113.0* -2.93 -41.50* -1.75 Drink alcohol in past month -9.1 1.44 13.89 0.89 Physical inactive rate 12.4 -4.30* 4.46 0.24 Sexual active rate -2334.0 -3.16 -41.00* -1.04 Mammography rate in past year 848.6 -0.51 44.91** 2.67** Mammography rate in two-year 1093.3 -0.40 50.33** 3.17** Social capital -9391** 15.33* -38.84 0.82 4. Discussion In this study, we applied an innovative approach to understand and describe the epidemic of breast cancer in the U.S. by considering the impacts of both population and geographic information. In addition to the conventional indicators of total count and P rate, we reported two newly developed indicators, G rate and PG rate, to form a four-dimensional view of the breast cancer epidemic. These four indicators provide new data to understand the national pattern of breast cancer epidemic, and to inform evidence-based resource planning and optimal allocation for breast cancer screening and treatment. 4.1 Total cost of breast cancer treatment The total count of breast cancer cases provides data for the absolute cost of the treatment of breast cancer, and can be used to compare the total cost between different states. For example, the annual cost for one patient at the stage 0 of breast cancer was $ 60,637, and even higher for patients at more advanced stages (Blumen et al., 2016 ). The state with largest number of total count was California with 125,300 cases, while the Wyoming state had the least number of breast cases of 2600. Given the same cost for each case in the two states, the cost in California ( $ 7.6 billion) would be nearly 48 times higher than the Wyoming State ( $ 0.16 billion). Financial burden would be much greater for states with largest number of total count of breast cancer cases, including California, Florida, Texas, New York and Pennsylvania. 4.2 Breast cancer risk from population perspective Prevalence rate, or P rate in the study, is the most commonly used indicator for epidemiologist to describe the breast cancer epidemic. It has been adjusted for the population size, and high P rate indicates more breast cases in the given population size. Findings of the study indicated that Maryland had the highest P rate with 670 per 100,000 people, while Nevada had the lowest P rate with 480 per 100,000 people. Given the same population size, more breast cancer cases would be diagnosed in Maryland than in Nevada. Another typical example was California who had the largest number of breast cancer cases. But when the population size was adjusted, the rank of California changed from 1 in total count to 48 in P rate due to its large population. 4.3 Breast cancer risk from geographic perspective Like the P rate adjusted for population size, G rate presented the epidemic of breast cancer from geographic perspective by controlling the geographic area size in different states. High G rate indicates more cases located in the given area, and the states with large geographic area size tend to have lower G rate. For example, the state with the highest G rate was New Jersey with 1817 cases per 1000 km 2 , while the state with the lowest G rate was Alaska with 2 cases per 1000 km 2 . Given the same geographic area size, New Jersey had 900 times more breast cancer cases than Alaska. The cost of treatment and need of hospital resources within the region of the same geographic area size will be 900 times higher in New Jersey than Alaska. More hospitals or health clinics are strongly needed in specific areas in New Jersey than in Alaska. On the other hand, in the states with low G rate, it would be more difficult for public health workers to screen and diagnose one case. Public health workers may need to reach out for larger distance and spend more time to identify one breast cancer case. Thus, for states with low G rate, the cost for treatment may be less, but the expenses for screening may be much higher than the states with high G rate. The decision-makers may consider the disparity of breast cancer G rate to optimize the resource allocation (X. Chen & Wang, 2017 ; X. Chen & Yu, 2018 ). 4.4 Comparable risk of breast cancer controlling for population and geographic size To control the impacts from population and geographic area size, a newly developed indicator, PG rate, was reported in the study. It measures the number of breast cancer cases in certain size of population and geographic area, and can be used to compare the risk of breast cancer in states with different population and geographic area size. For example, the PG rate was 200 in Rhode Island, the highest in the U.S., which means 200 breast cancer patients in every 100,000 population within 1000 square kilometers, while the number was 8 in West Virginia. Additionally, the national pattern of PG rate revealed a decline trend from the Northeast to the Southwest. Further studies are strongly needed to investigate the potential mechanisms underlying the Northeast-Southwest breast cancer pattern. More prevention, screening and treatment resources for breast cancer may be needed in the Northeast area. In addition to the public health implications, PG rate also has social-psycho impact. If a state has higher PG rate, it means it will be much easier for people to identify one breast cancer case around, increase the worries of being diagnosed of breast cancer for themselves, and then may increase the level of social anxiety. People living in that area may feel that it is very easy for them to be diagnosed as breast cancer, leading to higher likelihood of developing stress and mental health problems (Drageset & Lindstrøm, 2003 ). Thus, in addition to the specific prevention and treatment strategies, public health decision-makers may also consider the social-psycho impacts from individual and population level in the states with high PG rate, and develop particular health education and promotion programs to release the burden in the society as well as the individuals. 4.5 Limitations The study has limitations. First, the geographic area size was not adjusted for the non-residential area, such as mountains, forest and dissert, and may lead to biased G rate and PG rate. Second, the study did not consider the rural and urban difference that rural area has lower population density than urban. Third, the study only presented the breast cancer data collected in 2017, and caution may be needed when generalizing the findings to other time periods. Despite the limitations, this study provided an innovative four-dimensional view of breast cancer epidemic considering the population and geographic area size. This study also provided data and evidence for future effective prevention and treatment strategies, and resource optimization for breast cancer. Declarations Funding The study was partly supported by Outstanding Young and Middle-aged Science and Technology Innovation Team Project for Colleges and Universities of Hubei Province, China (NO: T2023052, PI: Chenchang Xiao) and the starting funding package from Wuhan University (PI: Bin Yu). Author Contribution Chenchang Xiao: Conceptualization, Methodology, Writing- Original draft preparation.Juan Chen,Bin Yu: Data curation, Writing- Original draft preparation. Kai Wang: Conceptualization, Investigation. Yunan Xu: Supervision, Conceptualization, Writing- Original draft preparation. Akemi Wijayabahu,Bin Yu: Supervision, Conceptualization, Writing- Reviewing and Editing References Arab, A., Behravan, N., Razazn, A., Barati, N., Mosaffa, F., Nicastro, J., … Behravan, J. (2019). The viral approach to breast cancer immunotherapy. Journal of Cellular Physiology , 234 (2), 1257–1267. doi:10.1002/jcp.27150 Blumen, H., Fitch, K., & Polkus, V. (2016). Comparison of treatment costs for breast cancer, by tumor stage and type of service. American Health & Drug Benefits , 9 (1), 23–32. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians , 68 (6), 394–424. doi:10.3322/caac.21492 CDC. (2018). Breast Cancer. Retrieved November 12, 2018, from https://www.cdc.gov/cancer/breast/index.htm Chen, D.-G. (2017). Comparing geographic area-based and classical population-based incidence and prevalence rates, and their confidence intervals. Preventive Medicine Reports , 7 , 116–118. doi:10.1016/j.pmedr.2017.05.017 Chen, X., & Wang, K. (2017). Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control. Preventive Medicine Reports , 5 , 301–307. doi:10.1016/j.pmedr.2017.01.009 Chen, X., & Yu, B. (2018). Mapping the Global Epidemic of HIV/AIDS with a Novel Approach Using Country-SpecificCounts of HIV Infections and Three Rates Controlled for Population and Geographic Area. Global Health Journal . De Angelis, G., De Angelis, R., Frova, L., & Verdecchia, A. (1994). MIAMOD: a computer package to estimate chronic disease morbidity using mortality and survival data. Computer Methods and Programs in Biomedicine , 44 (2), 99–107. De Angelis, R., Tavilla, A., Verdecchia, A., Scoppa, S., Hachey, M., Feuer, E. J., & Mariotto, A. B. (2009). Breast cancer survivors in the United States: geographic variability and time trends, 2005-2015. Cancer , 115 (9), 1954–1966. doi:10.1002/cncr.24217 De la Mare, J.-A., Contu, L., Hunter, M. C., Moyo, B., Sterrenberg, J. N., Dhanani, K. C. H., … Edkins, A. L. (2014). Breast cancer: current developments in molecular approaches to diagnosis and treatment. Recent Patents on Anti-cancer Drug Discovery , 9 (2), 153–175. Drageset, S., & Lindstrøm, T. C. (2003). The mental health of women with suspected breast cancer: the relationship between social support, anxiety, coping and defence in maintaining mental health. Journal of Psychiatric and Mental Health Nursing , 10 (4), 401–409. Ju, J., Zhu, A.-J., & Yuan, P. (2018). Progress in targeted therapy for breast cancer. Chronic Diseases and Translational Medicine , 4 (3), 164–175. doi:10.1016/j.cdtm.2018.04.002 McCart Reed, A. E., Kalita-de Croft, P., Kutasovic, J., Saunus, J. M., & Lakhani, S. R. (2018). Recent advances in breast cancer research impacting clinical diagnostic practice. The Journal of Pathology . doi:10.1002/path.5199 National Cancer Institute. (2018). State Cancer Profiles. Retrieved November 28, 2018, from https://www.statecancerprofiles.cancer.gov/incidencerates/index.php?stateFIPS=00&cancer=055&race=05&age=001&year=0&type=incd&sortVariableName=rate&sortOrder=default#results Peart, O. (2015). Breast intervention and breast cancer treatment options. Radiologic Technology , 86 (5), 535M–558M; quiz 559. Samson, M. E., Porter, N. G., Hurley, D. M., Adams, S. A., & Eberth, J. M. (2016). Disparities in Breast Cancer Incidence, Mortality, and Quality of Care among African American and European American Women in South Carolina. Southern Medical Journal , 109 (1), 24–30. doi:10.14423/SMJ.0000000000000396 Sighoko, D., Hunt, B. R., Irizarry, B., Watson, K., Ansell, D., & Murphy, A. M. (2018). Disparity in breast cancer mortality by age and geography in 10 racially diverse US cities. Cancer Epidemiology , 53 , 178–183. doi:10.1016/j.canep.2018.02.003 U.S. Cancer Statistics Working Group. (2017). U.S. Cancer Statistics Data Visualizations Tool. Retrieved November 12, 2018, from https://gis.cdc.gov/Cancer/USCS/DataViz.html WHO. (2018). Breast cancer: prevention and control. Retrieved November 12, 2018, from http://www.who.int/cancer/detection/breastcancer/en/ Yedjou, C. G., Tchounwou, P. B., Payton, M., Miele, L., Fonseca, D. D., Lowe, L., & Alo, R. A. (2017). Assessing the racial and ethnic disparities in breast cancer mortality in the united states. International Journal of Environmental Research and Public Health , 14 (5). doi:10.3390/ijerph14050486 Additional Declarations No competing interests reported. Supplementary Files AppendixTableA1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 Jul, 2026 Reviews received at journal 23 Jun, 2026 Reviews received at journal 08 Jun, 2026 Reviewers agreed at journal 08 Jun, 2026 Reviewers agreed at journal 05 Jun, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 17 Dec, 2024 Reviewers agreed at journal 17 Dec, 2024 Reviewers invited by journal 05 Dec, 2024 Editor invited by journal 14 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 11 Nov, 2024 First submitted to journal 10 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5426841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382476511,"identity":"db3d4c7d-e041-41e5-a062-51fbc66e2abd","order_by":0,"name":"Chenchang Xiao","email":"","orcid":"","institution":"City University of Wuhan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenchang","middleName":"","lastName":"Xiao","suffix":""},{"id":382476512,"identity":"7a192d48-b1a7-4e80-8e1a-2517e7c713ef","order_by":1,"name":"Juan Chen","email":"","orcid":"","institution":"Wuhan 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Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akemi","middleName":"","lastName":"Wijayabahu","suffix":""},{"id":382476516,"identity":"26f332df-6906-46ff-9415-61c2732db439","order_by":5,"name":"Bin Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACA2YwKSHHz97ABmIyNhCnpcDGWLLnALFawOSHtMQNNxKI1GLOzntMmsfgMOOGm2/MHvMw2MhuOMD87AE+LZbNfGkgLcySt3PMjXkY0ow3HGAzN8DrsMM8ZiAtbHy3c7dJ8zAcTtxwgIdNghgtPAw3z4K0/CdaS5qEwA1ekJYDhLVYNvMYW84xsDGQ7Mn/JjnHINl45mE2M7xazPnPGN5480eivp/9WJrEmwo72b7jzc/wagECFiQFoKBiJqAepOQDYTWjYBSMglEwogEAEvhCnQWv484AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-11-10 16:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5426841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5426841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70133732,"identity":"7a079962-86d3-431e-91a4-6fd40e21fe2b","added_by":"auto","created_at":"2024-11-28 16:36:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92174,"visible":true,"origin":"","legend":"\u003cp\u003eNational pattern of the count of breast cancer cases in the U.S.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/b14f965de797fe5fe502dc15.png"},{"id":70133735,"identity":"5286548d-e14a-44f4-a2f7-99a82edb8718","added_by":"auto","created_at":"2024-11-28 16:36:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94918,"visible":true,"origin":"","legend":"\u003cp\u003eNational pattern of the P rate of breast cancer in the U.S.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/827c0349869c0c3b70b5581a.png"},{"id":70134520,"identity":"3bc6a98a-34aa-4670-800f-1760ead7fedf","added_by":"auto","created_at":"2024-11-28 16:44:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93560,"visible":true,"origin":"","legend":"\u003cp\u003eNational pattern of the G rate of breast cancer in the U.S.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/5b3f6fce8591990345239189.png"},{"id":70133731,"identity":"981466b7-0da3-43dc-8870-5ff9d6ee2b1b","added_by":"auto","created_at":"2024-11-28 16:36:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93152,"visible":true,"origin":"","legend":"\u003cp\u003eNational pattern of the PG rate of breast cancer in the U.S.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/ccabfb06c4513e8dfdcd4c88.png"},{"id":70134899,"identity":"cb777a43-c875-4318-9e4c-dd127b69f27f","added_by":"auto","created_at":"2024-11-28 16:52:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":964017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/53c02228-0f31-4aa5-b2f8-cbf8b3b6cfc0.pdf"},{"id":70133736,"identity":"d17c010a-0660-4cb6-9b93-1054d8b1cfde","added_by":"auto","created_at":"2024-11-28 16:36:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18238,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixTableA1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5426841/v1/c0fcc227e17da9b410b22df4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the risk of breast cancer from population and geographic perspective","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Breast cancer in the world and the United States\u003c/h2\u003e \u003cp\u003eBreast cancer is the most commonly diagnosed (11.6% of all cancers) and the second leading cause of death (6.6% of all death by cancers) among women worldwide (Bray et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; WHO, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The burden of breast cancer is higher in developed countries while the extent of the burden is expanding in developing countries (Bray et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the United States (U.S.), breast cancer is the second leading cancer after lung cancer (CDC, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the latest report from the Center for Disease Control and Prevention (CDC), the incidence rate of breast cancer was 124.8 per 100,000 with 242,476 new cases in 2015, and with a five-year prevalence rate of 606.7 per 100,000 with over one million women living with breast cancer (U.S. Cancer Statistics Working Group, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, prevention of breast cancer is of great significance in improving the nation\u0026rsquo;s health in general.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Public health efforts in breast cancer research and prevention\u003c/h2\u003e \u003cp\u003eAdvancements in early detection methods, treatment options and population based interventions based on the copious amount of evidence on breast cancer has led to a decrease in breast cancer related mortality and increased survival after breast cancer diagnosis in the U.S. (Arab et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de la Mare et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ju, Zhu, \u0026amp; Yuan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McCart Reed, Kalita-de Croft, Kutasovic, Saunus, \u0026amp; Lakhani, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peart, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite the great achievements in prevention and treatment, health disparities of breast cancer still exist between different geographic areas. Previous research has shown geographic differences in breast cancer prevalence and mortality by state (National Cancer Institute, 2018; Sighoko et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the latest State Cancer Profiles report, breast cancer incidence has increased in Connecticut, New Jersey, Kentucky, Louisiana, Alabama, Oklahoma (National Cancer Institute, 2018). These differences in mortality and morbidity could be attributed to the disproportionate distribution of genetically predisposing factors, disparities in access to early detection (mammography) and health care, and underlying lifestyle and behavioral risk factors (Samson, Porter, Hurley, Adams, \u0026amp; Eberth, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sighoko et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yedjou et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, when comparing the state differences, geographic information was not considered, leading to a biased understanding of differences of breast cancer epidemic in different states. More comprehensive understanding of the burden of breast cancer across different states is strongly needed to add new geographic information to inform better evidence-based decision making and optimal utilization of health resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Four-indicator system in understanding breast cancer\u003c/h2\u003e \u003cp\u003eTraditional epidemiological studies always use the prevalence rate to describe the epidemic of breast cancer. However, no data have been reported regarding the geographic information, particularly the geographic area size. Thus, a four-indicator system, including count, population-based rate (P rate), geographic-based rate (G rate) and population-geographic-based rate (PG rate), was used to better understand the risk of breast cancer (D.-G. Chen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; X. Chen \u0026amp; Wang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; X. Chen \u0026amp; Yu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This system was initially developed for infectious diseases, such as HIV, to better estimate the disease spread and to assess the need for resource allocation (X. Chen \u0026amp; Wang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Now we innovatively applied it to better understand chronic disease. Although disease spread is not of concern in chronic diseases, we can evaluate the need for resources by different geographical areas to optimize the resource allocation and use.\u003c/p\u003e \u003cp\u003eFirst of all, it is important to know the total number of people living with breast cancer (count). These data could be used to estimate the total amount of money and health resources needed to prevent and treat breast cancer. For example, the average cost for one patient at the stage 0 of breast cancer was \u003cspan\u003e$\u003c/span\u003e60,637 in one year (Blumen, Fitch, \u0026amp; Polkus, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). If we assume all one million women living with breast cancer were at stage 0, then the total cost for one year would be \u003cspan\u003e$\u003c/span\u003e60.637\u0026nbsp;billion, accounting for the 6.06% of the GDP in the U.S. in 2017. The actual cost would be much greater if we take the advanced stages of breast cancer into account.\u003c/p\u003e \u003cp\u003eThe second indicator is the population-based rate (P rate), also known as prevalence rate. This is the most common indicator epidemiologists always use to understand and describe the risk of public health problems. It is measured as the number of breast cancer patients divided by the total population. For example, the count of breast cancer in California was 125,300 in 2017 and ranked first in the count, but when considering the large population, the prevalence rate would be 520 per 100,000 population after adjusted for age and ranked 48 in all 50 U.S. states. However, this indicator is limited by ignoring the influence of geographic area size. For example, Maryland and Wyoming have the same age-standardized prevalence rate of breast cancer (670 per 100,000 people for both), and Maryland has larger population size but smaller geographic area size. Thus, it would be much easier in Maryland to locate one breast cancer. On the other side, Wyoming would spend more money, time, travel distance and human efforts to screen one breast cancer case. P rate alone could not reflect these differences in Maryland and Wyoming, and is limited in understanding the risk of breast cancer and optimizing the resource allocation.\u003c/p\u003e \u003cp\u003eTo address the limitation of P rate, another two indicators were developed to incorporate the influence of geographic area size. The third indicator is the geographic-based rate, named as G rate. It is estimated as the count of breast cancer divided by the total geographic area size. For example, the count of breast cancer was 105,600 in Florida and 34,900 in New Jersey, and the geographic area size was 139,670 square kilometers and 19,209 for Florida and New Jersey, respectively. Thus, the G rate should be 756 cases per 1000 square kilometers in Florida, and 1817 per 1000 square kilometers in New Jersey. Given the same geographic area size, the risk of breast cancer would be 2.40 (1817/756) times higher in New Jersey than Florida. It would be much easier to locate one case in the states with high G rate, like New Jersey. While for states with low G rate, more resources and human efforts are needed to do the screening of breast cancer.\u003c/p\u003e \u003cp\u003eThe fourth indicator considers the impacts of both population and geographic area size, named as the population-geographic-based rate (PG rate). It could be estimated as the total number of breast cancer cases divided by the total population and total geographic size. For example, the count of breast cancer in Rhode Island was 4300 in 2017, the population was 543,323 and the geographic area size was 2707 square kilometers. Thus, the age adjusted PG rate would be 200 cases per 100,000 population and 1000 square kilometers. PG rate is a better indicator than count, P rate and G rate since it controls the confounding effects of both population and geographic area size, and can be used to compare across states with different size of population and geographic area. A system of the four-indicator of breast cancer (e.g. count, P rate, G rate and PG rate) may provide comprehensive information to understand and describe the breast cancer epidemic, and devise, plan and implement effective screening and treatment for breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Purpose of the study\u003c/h2\u003e \u003cp\u003eThe study aims to describe the epidemic of breast cancer by applying the four indicators, including total count, P rate, G rate and PG rate using the state specific data in the U.S. The ultimate goal is to provide a four-dimensional view of the national pattern of breast cancer epidemic, and add new data supporting the research and practice of breast cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and collection\u003c/h2\u003e \u003cp\u003e \u003cem\u003eTotal count and age-standardized prevalence rate of breast cancer\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe projected total count and age-standardized prevalence rate in 2017 were derived from the National Cancer Institutes\u0026rsquo; State Cancer Profiles (National Cancer Institute, 2018). Information on data collection and calculation of prevalence has been previously described in detail (G. De Angelis, De Angelis, Frova, \u0026amp; Verdecchia, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; R. De Angelis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Briefly, the State Cancer Profiles has derived the breast cancer prevalence estimates from state-specific cancer mortality and survival data using \u0026ldquo;Mortality-Incidence Analysis MODEL (MIAMOD)\u0026rdquo; statistical package (National Cancer Institute, 2018). This model utilizes an age-period-cohort model on the logistic scale to estimate breast cancer incidence, which will be used in the prevalence calculations. Data were collected from National Center of Health Statistics, Surveillance, Epidemiology, and End Results Program (SEER) and US Census Bureau and for the age standardization U.S. Census population (2000) population data has been used.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGeographic area size by state\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe geographic area size by state was derived from the designated website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://state.1keydata.com/states-by-size.php\u003c/span\u003e\u003cspan address=\"https://state.1keydata.com/states-by-size.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only land area (excluding water) was used for analysis in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBreast cancer count\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBreast cancer count is defined as the number of patients living with breast cancer.\u003c/p\u003e \u003cp\u003eCount\u0026thinsp;=\u0026thinsp;total number of cases living with breast cancer (1000)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip; (1)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBreast cancer P rate\u003c/em\u003e \u003c/p\u003e \u003cp\u003eP rate is the population-based rate, and it is the same as the prevalence rate. It was estimated as the total number of breast cancer cases divided by the total population. The age-standardized prevalence rate was used as P rate in the study.\u003c/p\u003e \u003cp\u003eP rate\u0026thinsp;=\u0026thinsp;total number of breast cancer cases/total population (per 100,000 population)\u0026hellip;\u0026hellip;\u0026hellip; (2)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBreast cancer G rate\u003c/em\u003e \u003c/p\u003e \u003cp\u003eG rate, geographic area-based rate, is defined as the number of breast cancer cases divided by the total geographic area in each state. It is calculated using the Eq.\u0026nbsp;(3).\u003c/p\u003e \u003cp\u003eG rate\u0026thinsp;=\u0026thinsp;total number of breast cancer cases/total geographic area size (per 1000 km\u003csup\u003e2\u003c/sup\u003e)\u0026hellip;\u0026hellip;\u0026hellip;. (3)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBreast cancer PG rate\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePG rate, named as population-geographic area-based rate, is defined as the total number of breast cancer cases divided by the total population and total geographic area size in specific state. The Eq.\u0026nbsp;(4) presents the way to calculate the PG rate. In the study, we computed the PG rate as the age-standardized prevalence rate divided by the geographic size.\u003c/p\u003e \u003cp\u003ePG rate\u0026thinsp;=\u0026thinsp;total number of breast cancer cases/(total population*total geographic area size) (per 100,000 pop per 1000 km\u003csup\u003e2\u003c/sup\u003e)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (4)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Geographic mapping\u003c/h2\u003e \u003cp\u003eGeographic mapping in the study was conducted in five steps. First, the four indicators, including total count, P rate, G rate and PG rate, were estimated using the commercial software Microsoft Excel Version 2016 (Microsoft, Seattle, WA). A dataset named as \u0026ldquo;USBreastCancer\u0026rdquo; was created with the variables of the state name and the four indicators. Second, the free software R version 3.4.2 was used for the global mapping. Three packages were used in the mapping process, including \u0026ldquo;ggplot2\u0026rdquo;, \u0026ldquo;usmap\u0026rdquo;, and \u0026ldquo;RColorBrewer\u0026rdquo;. A dataset named as \u0026ldquo;USmap\u0026rdquo; was created by extracting geographic information (e.g. state name, longitude, latitude) of individual states in the U.S. from the package \u0026ldquo;usmap\u0026rdquo;. Third, another dataset named as \u0026ldquo;USBreastCancerMap\u0026rdquo; was created by merging \u0026ldquo;USBreastCancer\u0026rdquo; and \u0026ldquo;USmap\u0026rdquo; by the state name. Fourth, after the data preparation, a blank U.S. base map was created by using the packages \u0026ldquo;usmap\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo;. In the last step, we used the package \u0026ldquo;RColorBrewer\u0026rdquo; to define the colors of the map with dark color representing the high level of breast cancer. Then we added a layer of one indicator to the base map using the package \u0026ldquo;ggplot2\u0026rdquo;, and finally generated four maps for breast cancer. The R code could be provided upon request.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 National pattern of the total count of breast cancer\u003c/h2\u003e \u003cp\u003eResults in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the total count of breast cancer by state in the U.S. The state with largest number of people living with breast cancer was California, followed by Florida, Texas, New York, Pennsylvania, Illinois, Ohio, Michigan, North Carolina, and Virginia with a range from 35300 to 125300. The two to three columns of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e listed the top 10 states with highest number of breast cancer. The overall pattern for the count map was that the states close to oceans and lakes had more breast cancer cases than the states in the inland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 National pattern of P rate of breast cancer\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the national pattern of P rate of breast cancer that adjusted for the population size. It provided a better measurement of the risk of breast cancer than the count. The top 10 states with highest P rate were Maryland, Wyoming, Virginia, Wisconsin, Oregon, Michigan, Hawaii, Florida, Washington and Oklahoma ranging from 630 to 670 per 100,000 population (4\u0026ndash;5 columns in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the states with top counts of breast cancer but with large population size did not rank top for P rate, such as California, Texas, and New York. The states in the inland ranked high in P rate due to the small population size compared to the states close to the coastal line, such as Wyoming, Utah, Colorado, Kansas, Arizona, and Oklahoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 National pattern of G rate of breast cancer\u003c/h2\u003e \u003cp\u003eThe results in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the national pattern of G rate of breast cancer. When the geographic area size was considered, the map presented different information from the count and P rate. The top 10 states with highest G rate of breast cancer included New Jersey, Rhode Island, Massachusetts, Connecticut, Maryland, Delaware, Florida, New York, Pennsylvania and Ohio. The values for these states ranged from 430 to 1817 cases per 1000 km\u003csup\u003e2\u003c/sup\u003e. Compared to count, states with small geographic area size had higher G rate than states with large geographic area size, such as New Jersey, Maryland, and Delaware.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 National pattern of the PG rate of breast cancer\u003c/h2\u003e \u003cp\u003eResults in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e considered both the population and geographic information. The pattern of PG rate showed a declining trend from the Northeast (e.g. Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, and New Jersey) to the Southwest (e.g. California, Arizona, New Mexico and Texas). States in the East and North had higher PG rate than the states in the West and South. Results in the 8\u0026ndash;9 columns of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e listed the top 10 states with highest PG rate, including Rhode Island, Delaware, Connecticut, Hawaii, New Jersey, Massachusetts, Vermont, New Hampshire, Maryland, and West Virginia with a range of 8-200 per 100,000 population and 1000 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 states of the four indicators of breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eP rate\u003c/p\u003e \u003cp\u003e(per 100,000 pop)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eG rate\u003c/p\u003e \u003cp\u003e(Per 1000 km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePG rate\u003c/p\u003e \u003cp\u003e(per 100,000 pop 1000 km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalifornia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaryland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNew Jersey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRhode Island\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWyoming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRhode Island\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDelaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTexas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirginia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMassachusetts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConnecticut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew York\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWisconsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConnecticut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHawaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePennsylvania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOregon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaryland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMassachusetts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIllinois\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMichigan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDelaware\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNew Jersey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOhio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHawaii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaryland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMichigan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNew York\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNew Hampshire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Carolina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWashington\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePennsylvania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVermont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirginia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOklahoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOhio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWest Virginia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of linear regression of Influential factors of the four indicators of breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePG rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-691.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorce rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-716.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-196.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity prevalence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1161.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-27.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCigarette smoke in past month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2113.0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.50*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink alcohol in past month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical inactive rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.30*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual active rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2334.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-41.00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMammography rate in past year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e848.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.67**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMammography rate in two-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1093.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.17**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9391**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-38.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we applied an innovative approach to understand and describe the epidemic of breast cancer in the U.S. by considering the impacts of both population and geographic information. In addition to the conventional indicators of total count and P rate, we reported two newly developed indicators, G rate and PG rate, to form a four-dimensional view of the breast cancer epidemic. These four indicators provide new data to understand the national pattern of breast cancer epidemic, and to inform evidence-based resource planning and optimal allocation for breast cancer screening and treatment.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Total cost of breast cancer treatment\u003c/h2\u003e \u003cp\u003eThe total count of breast cancer cases provides data for the absolute cost of the treatment of breast cancer, and can be used to compare the total cost between different states. For example, the annual cost for one patient at the stage 0 of breast cancer was \u003cspan\u003e$\u003c/span\u003e60,637, and even higher for patients at more advanced stages (Blumen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The state with largest number of total count was California with 125,300 cases, while the Wyoming state had the least number of breast cases of 2600. Given the same cost for each case in the two states, the cost in California (\u003cspan\u003e$\u003c/span\u003e7.6\u0026nbsp;billion) would be nearly 48 times higher than the Wyoming State (\u003cspan\u003e$\u003c/span\u003e0.16\u0026nbsp;billion). Financial burden would be much greater for states with largest number of total count of breast cancer cases, including California, Florida, Texas, New York and Pennsylvania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Breast cancer risk from population perspective\u003c/h2\u003e \u003cp\u003ePrevalence rate, or P rate in the study, is the most commonly used indicator for epidemiologist to describe the breast cancer epidemic. It has been adjusted for the population size, and high P rate indicates more breast cases in the given population size. Findings of the study indicated that Maryland had the highest P rate with 670 per 100,000 people, while Nevada had the lowest P rate with 480 per 100,000 people. Given the same population size, more breast cancer cases would be diagnosed in Maryland than in Nevada. Another typical example was California who had the largest number of breast cancer cases. But when the population size was adjusted, the rank of California changed from 1 in total count to 48 in P rate due to its large population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Breast cancer risk from geographic perspective\u003c/h2\u003e \u003cp\u003eLike the P rate adjusted for population size, G rate presented the epidemic of breast cancer from geographic perspective by controlling the geographic area size in different states. High G rate indicates more cases located in the given area, and the states with large geographic area size tend to have lower G rate. For example, the state with the highest G rate was New Jersey with 1817 cases per 1000 km\u003csup\u003e2\u003c/sup\u003e, while the state with the lowest G rate was Alaska with 2 cases per 1000 km\u003csup\u003e2\u003c/sup\u003e. Given the same geographic area size, New Jersey had 900 times more breast cancer cases than Alaska. The cost of treatment and need of hospital resources within the region of the same geographic area size will be 900 times higher in New Jersey than Alaska. More hospitals or health clinics are strongly needed in specific areas in New Jersey than in Alaska. On the other hand, in the states with low G rate, it would be more difficult for public health workers to screen and diagnose one case. Public health workers may need to reach out for larger distance and spend more time to identify one breast cancer case. Thus, for states with low G rate, the cost for treatment may be less, but the expenses for screening may be much higher than the states with high G rate. The decision-makers may consider the disparity of breast cancer G rate to optimize the resource allocation (X. Chen \u0026amp; Wang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; X. Chen \u0026amp; Yu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Comparable risk of breast cancer controlling for population and geographic size\u003c/h2\u003e \u003cp\u003eTo control the impacts from population and geographic area size, a newly developed indicator, PG rate, was reported in the study. It measures the number of breast cancer cases in certain size of population and geographic area, and can be used to compare the risk of breast cancer in states with different population and geographic area size. For example, the PG rate was 200 in Rhode Island, the highest in the U.S., which means 200 breast cancer patients in every 100,000 population within 1000 square kilometers, while the number was 8 in West Virginia. Additionally, the national pattern of PG rate revealed a decline trend from the Northeast to the Southwest. Further studies are strongly needed to investigate the potential mechanisms underlying the Northeast-Southwest breast cancer pattern. More prevention, screening and treatment resources for breast cancer may be needed in the Northeast area.\u003c/p\u003e \u003cp\u003eIn addition to the public health implications, PG rate also has social-psycho impact. If a state has higher PG rate, it means it will be much easier for people to identify one breast cancer case around, increase the worries of being diagnosed of breast cancer for themselves, and then may increase the level of social anxiety. People living in that area may feel that it is very easy for them to be diagnosed as breast cancer, leading to higher likelihood of developing stress and mental health problems (Drageset \u0026amp; Lindstr\u0026oslash;m, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Thus, in addition to the specific prevention and treatment strategies, public health decision-makers may also consider the social-psycho impacts from individual and population level in the states with high PG rate, and develop particular health education and promotion programs to release the burden in the society as well as the individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Limitations\u003c/h2\u003e \u003cp\u003eThe study has limitations. First, the geographic area size was not adjusted for the non-residential area, such as mountains, forest and dissert, and may lead to biased G rate and PG rate. Second, the study did not consider the rural and urban difference that rural area has lower population density than urban. Third, the study only presented the breast cancer data collected in 2017, and caution may be needed when generalizing the findings to other time periods. Despite the limitations, this study provided an innovative four-dimensional view of breast cancer epidemic considering the population and geographic area size. This study also provided data and evidence for future effective prevention and treatment strategies, and resource optimization for breast cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was partly supported by Outstanding Young and Middle-aged Science and Technology Innovation Team Project for Colleges and Universities of Hubei Province, China (NO: T2023052, PI: Chenchang Xiao) and the starting funding package from Wuhan University (PI: Bin Yu).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChenchang Xiao: Conceptualization, Methodology, Writing- Original draft preparation.Juan Chen,Bin Yu: Data curation, Writing- Original draft preparation. Kai Wang: Conceptualization, Investigation. Yunan Xu: Supervision, Conceptualization, Writing- Original draft preparation. Akemi Wijayabahu,Bin Yu: Supervision, Conceptualization, Writing- Reviewing and Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArab, A., Behravan, N., Razazn, A., Barati, N., Mosaffa, F., Nicastro, J., \u0026hellip; Behravan, J. (2019). The viral approach to breast cancer immunotherapy. \u003cem\u003eJournal of Cellular Physiology\u003c/em\u003e, \u003cem\u003e234\u003c/em\u003e(2), 1257\u0026ndash;1267. doi:10.1002/jcp.27150\u003c/li\u003e\n \u003cli\u003eBlumen, H., Fitch, K., \u0026amp; Polkus, V. (2016). Comparison of treatment costs for breast cancer, by tumor stage and type of service. \u003cem\u003eAmerican Health \u0026amp; Drug Benefits\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 23\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eBray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., \u0026amp; Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(6), 394\u0026ndash;424. doi:10.3322/caac.21492\u003c/li\u003e\n \u003cli\u003eCDC. (2018). Breast Cancer. Retrieved November 12, 2018, from https://www.cdc.gov/cancer/breast/index.htm\u003c/li\u003e\n \u003cli\u003eChen, D.-G. (2017). Comparing geographic area-based and classical population-based incidence and prevalence rates, and their confidence intervals. \u003cem\u003ePreventive Medicine Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 116\u0026ndash;118. doi:10.1016/j.pmedr.2017.05.017\u003c/li\u003e\n \u003cli\u003eChen, X., \u0026amp; Wang, K. (2017). Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control. \u003cem\u003ePreventive Medicine Reports\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 301\u0026ndash;307. doi:10.1016/j.pmedr.2017.01.009\u003c/li\u003e\n \u003cli\u003eChen, X., \u0026amp; Yu, B. (2018). Mapping the Global Epidemic of HIV/AIDS with a Novel Approach Using Country-SpecificCounts of HIV Infections and Three Rates Controlled for Population and Geographic Area. \u003cem\u003eGlobal Health Journal\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eDe Angelis, G., De Angelis, R., Frova, L., \u0026amp; Verdecchia, A. (1994). MIAMOD: a computer package to estimate chronic disease morbidity using mortality and survival data. \u003cem\u003eComputer Methods and Programs in Biomedicine\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(2), 99\u0026ndash;107.\u003c/li\u003e\n \u003cli\u003eDe Angelis, R., Tavilla, A., Verdecchia, A., Scoppa, S., Hachey, M., Feuer, E. J., \u0026amp; Mariotto, A. B. (2009). Breast cancer survivors in the United States: geographic variability and time trends, 2005-2015. \u003cem\u003eCancer\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(9), 1954\u0026ndash;1966. doi:10.1002/cncr.24217\u003c/li\u003e\n \u003cli\u003eDe la Mare, J.-A., Contu, L., Hunter, M. C., Moyo, B., Sterrenberg, J. N., Dhanani, K. C. H., \u0026hellip; Edkins, A. L. (2014). Breast cancer: current developments in molecular approaches to diagnosis and treatment. \u003cem\u003eRecent Patents on Anti-cancer Drug Discovery\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 153\u0026ndash;175.\u003c/li\u003e\n \u003cli\u003eDrageset, S., \u0026amp; Lindstr\u0026oslash;m, T. C. (2003). The mental health of women with suspected breast cancer: the relationship between social support, anxiety, coping and defence in maintaining mental health. \u003cem\u003eJournal of Psychiatric and Mental Health Nursing\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 401\u0026ndash;409.\u003c/li\u003e\n \u003cli\u003eJu, J., Zhu, A.-J., \u0026amp; Yuan, P. (2018). Progress in targeted therapy for breast cancer. \u003cem\u003eChronic Diseases and Translational Medicine\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 164\u0026ndash;175. doi:10.1016/j.cdtm.2018.04.002\u003c/li\u003e\n \u003cli\u003eMcCart Reed, A. E., Kalita-de Croft, P., Kutasovic, J., Saunus, J. M., \u0026amp; Lakhani, S. R. (2018). Recent advances in breast cancer research impacting clinical diagnostic practice. \u003cem\u003eThe Journal of Pathology\u003c/em\u003e. doi:10.1002/path.5199\u003c/li\u003e\n \u003cli\u003eNational Cancer Institute. (2018). State Cancer Profiles. Retrieved November 28, 2018, from https://www.statecancerprofiles.cancer.gov/incidencerates/index.php?stateFIPS=00\u0026amp;cancer=055\u0026amp;race=05\u0026amp;age=001\u0026amp;year=0\u0026amp;type=incd\u0026amp;sortVariableName=rate\u0026amp;sortOrder=default#results\u003c/li\u003e\n \u003cli\u003ePeart, O. (2015). Breast intervention and breast cancer treatment options. \u003cem\u003eRadiologic Technology\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(5), 535M\u0026ndash;558M; quiz 559.\u003c/li\u003e\n \u003cli\u003eSamson, M. E., Porter, N. G., Hurley, D. M., Adams, S. A., \u0026amp; Eberth, J. M. (2016). Disparities in Breast Cancer Incidence, Mortality, and Quality of Care among African American and European American Women in South Carolina. \u003cem\u003eSouthern Medical Journal\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(1), 24\u0026ndash;30. doi:10.14423/SMJ.0000000000000396\u003c/li\u003e\n \u003cli\u003eSighoko, D., Hunt, B. R., Irizarry, B., Watson, K., Ansell, D., \u0026amp; Murphy, A. M. (2018). Disparity in breast cancer mortality by age and geography in 10 racially diverse US cities. \u003cem\u003eCancer Epidemiology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 178\u0026ndash;183. doi:10.1016/j.canep.2018.02.003\u003c/li\u003e\n \u003cli\u003eU.S. Cancer Statistics Working Group. (2017). U.S. Cancer Statistics Data Visualizations Tool. Retrieved November 12, 2018, from https://gis.cdc.gov/Cancer/USCS/DataViz.html\u003c/li\u003e\n \u003cli\u003eWHO. (2018). Breast cancer: prevention and control. Retrieved November 12, 2018, from http://www.who.int/cancer/detection/breastcancer/en/\u003c/li\u003e\n \u003cli\u003eYedjou, C. G., Tchounwou, P. B., Payton, M., Miele, L., Fonseca, D. D., Lowe, L., \u0026amp; Alo, R. A. (2017). Assessing the racial and ethnic disparities in breast cancer mortality in the united states. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(5). doi:10.3390/ijerph14050486\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5426841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5426841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer is one of the most significant public health challenges in the United States. This study aims to deepen the understanding of burden of breast cancer from a four-dimensional view based on population and geographic information to inform better evidence-based decision making and resource optimization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData of breast cancer in the study were derived from National Cancer Institutes\u0026rsquo; State Cancer Profiles, including total count and age-standardized prevalence of breast cancer by state. Four indicators were estimated, including total count, population-based P rate, geographic-based G rate and population and geographic-based PG rate. The free software R was used to do the geographic mapping to visualize the pattern of the four indicators across states.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe top five states with the largest count of breast cancer were California, Florida, Texas, New York and Pennsylvania, informing the resources needed for treatment. The top five states with highest P rate were Maryland, Wyoming, Virginia, Wisconsin, and Oregon, while the top five states with highest G rate included New Jersey, Rhode Island, Massachusetts, Connecticut, and Maryland, revealing breast cancer risk from population and geographic perspective. When controlling for both population and geographic size, the states with highest PG rate were Rhode Island, Delaware, Connecticut, Hawaii, and New Jersey, presenting a declining trend of breast cancer burden from Northeast to Southwest.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study added two indicators to the conventional measures of disease risk to incorporate the influence of geographic information, presenting a four-dimensional national pattern of breast cancer risk. Study findings will provide evidence informing better decision making for optimal resource allocation.\u003c/p\u003e","manuscriptTitle":"Understanding the risk of breast cancer from population and geographic perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-28 16:36:16","doi":"10.21203/rs.3.rs-5426841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-07-08T07:40:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-06-23T18:00:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-06-08T19:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147347673770984986301389420328377691412","date":"2026-06-08T16:34:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233675666816644274557427812900130637471","date":"2026-06-05T14:18:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T22:42:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188907831152896999918840395690363321201","date":"2026-03-10T20:24:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-17T22:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105521899774409949018274823683039642810","date":"2024-12-17T22:24:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-06T04:39:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T07:31:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T03:13:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-11T14:17:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-11-10T15:57:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"149a74fe-e3eb-4fdc-996e-d1d1639f033b","owner":[],"postedDate":"November 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-07-08T07:56:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-28 16:36:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5426841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5426841","identity":"rs-5426841","version":["v1"]},"buildId":"veTbxFhMMB0_faC6-Wkog","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}