The effect of smoking on breast cancer screening results in female population:a study in Wu wei, China

doi:10.21203/rs.3.rs-4126440/v1

Download PDF

Research Article

The effect of smoking on breast cancer screening results in female population:a study in Wu wei, China

https://doi.org/10.21203/rs.3.rs-4126440/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective To construct a pathway analysis model of breast cancer screening population in this region based on Anderson model theory, and explore the influencing factors and pathways of screening results of breast cancer screening population in this region, in order to provide reference for refining breast cancer screening strategies and programs.

Methods Firstly, the distribution of each variable in the detection of breast related diseases was described by single factor analysis, and then a pathway analysis model for screening results of female breast cancer screening population in 2021-2023 was constructed according to the results of factor screening, so as to clarify the influence path and influence coefficient of each factor.

Results A total of 1792 female residents participated in breast cancer screening between 2021 and 2023, and 353 were detected with breast related diseases, with a detection rate of 19.7%. Single factor analysis showed that: Exposure to harmful substances, age, highest education, marital status, family history of breast cancer, menopause, weekly intake of fresh vegetables and fruits, weekly intake of processed foods, weekly intake of meat, eggs and milk, taste preference, preferred food or drink temperature, smoking, alcohol consumption, regular physical exercise, history of breast biopsy or breast benign Variables such as history of surgery and history of chest radiotherapy were different in whether breast related disease was detected. The result of path analysis model shows that the overall model fits well. The most significant variable of propensity characteristic dimension is the highest education; The significant variable in the dimension of medical behavior was smoking. The path coefficient indicates that the female population with lower education and smoking is more likely to detect mamma-related lesions in the screening population. In addition to the direct effect, the highest degree of education also had an indirect effect on whether breast related lesions were detected in the female screening population through the mediating variable of smoking.

Conclusion The results of this study confirm the importance of a healthy lifestyle. We should further strengthen the publicity and education on smoking cessation, and persuade smokers, especially pay attention to the promotion of smoking cessation behavior in the female population with low education level.

Screening

Breast cancer

Smoking

Path analysis

Anderson model

In 2020, there will be an estimated 19,292,789 new cases of cancer worldwide, with the top 10 cancer types accounting for more than 60% of all new cancers. For the first time, female breast cancer has surpassed lung cancer to become the most common cancer, with 159 out of 185 countries having the highest incidence of breast cancer. Its incidence (47.8/100,000) and death (13.6/100,000) are the first cancers in women ^[1]. In 2018, female breast cancer ranked first in the incidence spectrum of female cancer in the tumor registration area of Gansu Province, with 822 new cases, accounting for 14.03% of all female malignant tumors, with an incidence rate of 31.37/100,000 and 161 deaths, accounting for 6.17% of all female malignant tumor deaths, with a mortality rate of 6.14/100,000 ^[2]. The increase of morbidity and mortality will inevitably lead to the increase of the disease burden of cancer patients, and early detection and treatment can greatly improve the disease outcome. Since 2022, Wuwei City of Gansu Province has joined the major national public health service project ---- Urban Early Cancer Diagnosis and Treatment Project ^[3] to carry out breast cancer screening program for female population.

Path Analysis (PA), also known as path analysis, is a method that can study the multi-layer causal relationship between multiple variables and its correlation strength ^[4]. Path analysis can contain multiple regression equations at the same time, and the overall common factor model can be evaluated statistically to understand whether the common factor model constructed by the theory is consistent with the data actually collected by the researcher, that is, the fitness of the entire hypothesis model can be tested ^[5]. Anderson model, full name of "Medical service utilization model or medical care service utilization model", is recognized as the mainstream model applicable to medical and health service research by the international medical sociology and health service research field ^[6]. Based on the theory of Anderson model, this paper constructs a pathway analysis model for breast cancer screening population in this region, and explores the influencing factors and pathways of the screening results of breast cancer screening population in this region, so as to provide reference for refining breast cancer screening strategies and programs.

1.1 Data Analysis

Epidata 3.1 was used to record the health assessment questionnaires of the breast cancer screening population from the city cancer screening platform, and the data were recorded by two persons, and the quality control was carried out in the whole process. All residents participating in the screening program signed informed consent and volunteered to participate in clinical screening.

Data analysis was performed using SPSS27.0 and MPLUS 8.3 software. Firstly, the distribution of each variable in terms of whether breast related diseases are detected was described through single factor analysis. Then, based on the results of factor screening, a pathway analysis model for screening results of female breast cancer screening population in 2021–2023 was constructed to clarify the influence path and influence coefficient of each factor. For the model whose outcome variables are categorical variables, the weighted least squares,mean and variance (WLSMV) method is used for parameter estimation. Using the deviation-correcting bootstrap program described by Preacher and Hayes, the significance of indirect effects was tested using a beta = 2000 bootstrap sample ^{[7, 8]}. P < 0.05 was considered statistically significant.

In this paper, the path analysis hypothesis model ^[9] is constructed based on the Anderson model (revised in 2013) theory. The scenario feature dimension involves only one variable, "Whether you have been exposed to the following hazardous substances for more than one year". The characteristic dimension of personal characteristics includes 8 variables, including age, occupation, highest education, marital status, family history of breast cancer, pausimenia, childbearing history,and whether a second-degree relative was diagnosed with ovarian cancer before age 50.

The dimensions of medical behavior included weekly intake frequency of fresh vegetables and fruits, weekly intake frequency of processed foods, weekly intake frequency of meat, eggs and milk, weekly intake frequency of carbohydrate, taste preference in the past year, preferred food or product temperature, whether smoking, whether drinking, whether regular physical exercise, history of breast biopsy or surgery for benign breast disease, History of thoracic radiotherap, and whether hormone replacement therapy intervenes in menopause intervenes in menopause. It includes the following path hypothesis: "scenario feature dimension" can directly affect "medical outcome", but also indirectly affect "medical outcome" through "personal characteristics" and "medical behavior"; "Personal characteristics", as a pre-factor affecting "medical behavior" and "medical result", can not only indirectly affect "medical result" through "medical behavior", but also directly affect "medical result".

2.1 Description of basic situation of breast cancer screening population

A total of 1792 female residents participated in breast cancer screening between 2021 and 2023, of which 6 were breast cancer, accounting for 0.3%, 347 were suspected positive or positive, accounting for 19.4%, and 1439 were negative, accounting for 80.3%. In the final analysis model, breast cancer and the suspiciously positive or positive population were classified as screening for lesions, and 1,439 people with no abnormal results were classified as disease-free. Of the women who participated in breast cancer screening, 1,421 (79.2%) had never smoked, 293 (16.4%) were current smokers, and 78 (4.4%) were former smokers who had quit.

2.2 Distribution of scenario characteristic dimension variables

The scenario feature dimension involves only one variable (Table 1) : whether the exposure to the following harmful substances has accumulated for more than one year, and 296 people who choose "No" have mamma-related diseases in the screening results; 18 people exposed to asbestos for more than 1 year, 4 of whom were screened for breast related diseases; 19 people exposed to radon, beryllium and other substances for more than one year, 5 people were screened for mammary gland related diseases; 48 people exposed to diesel exhaust, soot and soot for more than a year and screened for breast related diseases. There was a significant difference in screening for breast related diseases among the four exposure groups (χ2 = 19.310, P < 0.001).

Table 1

Distribution of dimension variables of scenario feature dimension (demand) of female breast cancer screening population from 2021–2023
Characteristics	Groups	No lesion		Have lesions		χ²	P
Characteristics	Groups	n	%	n	%	χ²	P
Whether you have been exposed to the following hazardous substances for more than one year						19.31	<0.001
	Asbestos	14	1.00%	4	1.10%
	Radon, beryllium, etc	14	1.00%	5	1.40%
	Diesel exhaust, soot and soot, etc	96	6.70%	48	13.60%
	No	1315	91.40%	296	83.90%

2.3 Distribution of personal characteristic dimension variables

The dimensions of personal characteristics include three sub-dimensions: propensity characteristics, enabling resources and needs. The data in this paper only involve 8 variables in the dimension of propensity characteristics, among which five variables, such as age, highest education background, marital status, family history of breast cancer and pausimenia, have differences in detecting breast related diseases (Table 2). There were 48 patients in the 45–50 years old group, 205 patients in the 51–60 years old group, 98 patients in the 61–70 years old group, and 2 patients in the 71–75 years old group. There were statistically significant differences in whether breast related diseases were detected among all age groups (χ2 = 9.983, P = 0.019). The number of breast lesions in the population with primary school education and below was the least (5 people, accounting for 1.4%). Followed by master's degree and above (8 people, accounting for 2.3%), undergraduate (18 people, accounting for 5.1%); Among the population with junior high school education, 59 (16.7%) were found to have breast related diseases. Among the junior college population, 83 (23.5%) were found to have breast related diseases. The highest number of people with senior high school education were screened for mast-related diseases (180, or 51.0%). The proportions of widowed, unmarried, married and divorced people were 14.4%, 18.4%, 33.4% and 33.7%, respectively. There was a statistical difference in the detection of breast related diseases among all groups (χ2 = 64.166, P < 0.001). Among those with no family history of breast cancer, 1389 were not screened for mast-related diseases, 330 had lesions, and 23 were screened for breast related diseases among those with a family history of breast cancer, the difference was statistically significant (χ2 = 6.708, P = 0.010). There were 62 people(17.6%) screened breast related diseases in the premenopausal group, 279 (79.0%) in the natural menopausal group, and 12 (3.4%) in the manual menopausal group. There were differences in the screening of breast diseases among the three groups (χ2 = 16.592, P < 0.001).

Table 2

Distribution of dimensional variables of personal characteristics (propensity characteristics) of female breast cancer screening population from 2021 to 2023
Characteristics	Groups	No lesion		Have lesions		χ²	P
Characteristics	Groups	n	%	n	%	χ²	P
Age						9.983	0.019
	45 ~ 50 years old	155	10.80%	48	13.60%
	51 ~ 60 years old	915	63.60%	205	58.10%
	61 ~ 70 years old	336	23.30%	98	27.80%
	71 ~ 75 years old	33	2.30%	2	0.60%
Occupation						2.556	0.110
	Technical personnel	1401		338
	Others	38		15
Highest Education						117.641	<0.001
	Primary and below	359	24.90%	5	1.40%
	Junior high school	251	17.40%	59	16.70%
	Senior high school	590	41.00%	180	51.00%
	Junior college	182	12.60%	83	23.50%
	Undergraduate	28	1.90%	18	5.10%
	Master degree or above	29	2.00%	8	2.30%
Marital status						64.166	<0.001
	Unmarried	178	12.40%	65	18.40%
	Married	822	57.10%	118	33.40%
	Divorced	300	20.80%	119	33.70%
	widowed	139	9.70%	51	14.40%
Family history of breast cancer						6.708	0.010
	No	1389	96.50%	330	93.50%
	Yes	50	3.50%	23	6.50%
Pausimenia						16.592	<0.001
	No	145	10.10%	62	17.60%
	Yes, natural menopause	1256	87.30%	279	79.00%
	Yes, manual menopause intervention	38	2.60%	12	3.40%
Childbearing history						0.987	0.321
	No	11	0.80%	1	0.30%
	Yes	1428	99.20%	352	99.70%
Whether a second-degree relative was diagnosed with ovarian cancer before age 50						1.159	0.282
	No	1418	98.50%	345	97.70%
	Yes	21	1.50%	8	2.30%

2.4 Distribution of dimensional characteristics of medical behavior

The medical behavior dimension of Anderson model includes three sub-dimensions: personal self-care, medical service process and medical service utilization. The dimension of personal self-care includes 9 variables, including weekly intake frequency of fresh vegetables and fruits, weekly intake frequency of processed food, weekly intake frequency of meat, egg and milk, weekly intake frequency of carbohydrate (rice, flour, etc.), taste preference, temperature of food or drink preferred, whether smoking, drinking and regular physical exercise. The dimensions of medical service utilization included three variables: history of breast biopsy or surgery for benign breast diseases, history of chest radiotherapy and whether hormone replacement therapy intervenes in menopause intervenes in menopause. In the dimension of personal self-care, 8 variables including weekly intake frequency of fresh vegetables and fruits, weekly intake frequency of processed foods, weekly intake frequency of meat, egg and milk, taste preference, preferred temperature of food or drink, whether smoking, whether drinking and whether regular physical exercise, had statistical differences in whether breast related diseases were detected. The dimension of medical service utilization, including the history of breast biopsy or surgical history of benign breast diseases and the history of thoracic radiotherapy, showed statistical differences in whether breast related diseases were detected (Table 3).

Table 3

Distribution of medical behavior dimension characteristics of female breast cancer screening population from 2021 to 2023
Characteristics	Groups	No lesion		Have lesions		χ²	P
Characteristics	Groups	n	%	n	%	χ²	P
Weekly intake frequency of fresh vegetables and fruits						16.700	0.001
	Everyday	1288	89.50%	290	82.20%
	4–6 days a week	66	4.60%	22	6.20%
	2–3 days a week	59	4.10%	30	8.50%
	1 day or less per week	26	1.80%	11	3.10%
Weekly intake frequency of processed foods						28.822	<0.001
	Everyday	90	6.30%	29	8.20%
	4–6 days a week	89	6.20%	23	6.50%
	2–3 days a week	117	8.10%	60	17.00%
	1 day or less per week	1143	79.40%	241	68.30%
Weekly intake frequency of meat, eggs and milk						14.328	0.002
	Everyday	1238	86.00%	278	78.80%
	4–6 days a week	86	6.00%	30	8.50%
	2–3 days a week	82	5.70%	37	10.50%
	1 day or less per week	33	2.30%	8	2.30%
Weekly intake frequency of carbohydrate						3.398	0.334
	Everyday	1374	95.50%	334	94.60%
	4–6 days a week	49	3.40%	13	3.70%
	2–3 days a week	8	0.60%	5	1.40%
	1 day or less per week	8	0.60%	1	0.30%
Taste preference						47.155	<0.001
	Saltish	175	12.20%	63	17.80%
	Moderation	994	69.10%	176	49.90%
	Light taste	270	18.80%	114	32.30%
Preferred food or product temperature						32.742	<0.001
	Below normal temperature	18	1.30%	7	2.00%
	Normal temperature	1011	70.30%	195	55.20%
	Hot	388	27.00%	148	41.90%
	Very hot	22	1.50%	3	0.80%
Smoking						21.979	<0.001
	No, never smoke	1173	81.50%	248	70.30%
	Yeah, I'm still smoking	211	14.70%	82	23.20%
	I used to smoke, but I have stopped smoking	55	3.80%	23	6.50%
Alcohol drinking						14.376	<0.001
	No	1255	87.20%	280	79.30%
	Yes	184	12.80%	73	20.70%
Regular physical exercise						40.656	<0.001
	No	358	24.90%	148	41.90%
	Yes	1081	75.10%	205	58.10%
History of breast biopsy or surgery for benign breast disease						19.474	<0.001
	No	982	68.20%	197	55.80%
	Yes	457	31.80%	156	44.20%
History of thoracic radiotherapy						4.642	0.031
	No	1433	99.60%	348	98.60%
	Yes	6	0.40%	5	1.40%
Whether hormone replacement therapy intervenes in menopause intervenes in menopause						1.683	0.431
	No	614	98.60%	217	99.50%
	Yes, estrogen only	5	0.80%	1	0.50%
	Yes, estrogen and progesterone combined	4	0.60%	0	0.00%

2.5 Results of population pathway model analysis for female breast cancer screening in 2021–2023

The above dimensional variables are incorporated into the path analysis model according to the assumption model. After several adjustments, the path analysis model of female breast cancer screening results is shown in Fig. 1. The results of path analysis model showed that RMSEA < 0.001, CFI = 1.000, TLI = 1.001, SRMR = 0.024, and the model fitting index showed that the overall model fit was good. The most significant variable of propensity characteristic dimension is the highest education; The significant variable in the dimension of medical behavior was smoking. Path coefficients indicate that the screening population of women with lower education and who smoke is more likely to detect breast related lesions (Table 4).

Table 4

Coefficient table of population path model for female breast cancer screening from 2021–2023
Dimension	Variables	Path coefficient	Standard error	t	P
Propensity characteristics	Highest Education→ Smoking	-0.087	0.024	-3.566	<0.001
	Pausimenia→ Smoking	0.124	0.049	2.531	0.011
	Give birth to→ Smoking	-0.142	0.053	-2.689	0.007
	Highest Education→ Results	-0.105	0.049	-2.135	0.033
Medical behavior	Smoking→ Results	0.187	0.075	2.504	0.012

In addition to the direct effect, the highest education also had an indirect effect on whether breast related lesions were detected in the female screening population through the mediating variable of smoking. The proportion of indirect pathways and indirect effect sizes is described as follows (Table 5) : highest education → results, and the direct effect size is -0.105, accounting for 86.8% of the total effect size (-0.121). Highest education → smoking → results, indirect effect size (-0.016) accounted for 13.2% of the total effect size (-0.121).

Table 5

Distribution of effect sizes of female breast cancer screening population pathway analysis model from 2021–2023
Dimension	Variables	Total effect size	Direct effect size	Total indirect effect size
Propensity characteristics	Highest Education → Results	-0.121*	-0.105*	-0.016*
	Pausimenia → Results	0.023	0.000	0.023
	give birth to→ Results	-0.027	0.000	-0.027
Note: * indicates significant at 0.05 level; ** indicates significant at the 0.01 level.

Cancer is a global health problem that has a serious impact on individuals and society and is one of the most serious threats to public health. Cancer is the leading cause of death in most countries around the world, killing millions of people every year, and is an important obstacle to improving human life expectancy ^[10]. Breast cancer is an important factor affecting women's health, and early detection and treatment can reduce mortality. Color Doppler ultrasound and mammography are commonly used for breast cancer screening. The advantage of X-ray mammography is that it can observe the entire breast, retain clear images, facilitate comparative observation, and is not easy to miss diagnosis, but X-ray mammography may miss diagnosis of lumps with severe edema and lumps without obvious boundaries. Color ultrasound examination is simple and non-invasive, suitable for patients of any age, but color ultrasound examination on fat rich lesions, small lesions show poor, the overall image quality is not high. Therefore, in the process of urban breast screening, the combination of color Doppler ultrasound and X-ray molybdenum target is used for breast examination to reduce the rate of missed diagnosis and find each patient as early as possible.

The results of this study show that women who smoke are more likely to be diagnosed with breast cancer-related diseases, and the highest education level also has an indirect impact on the results of women's breast cancer screening through the intermediary variable of smoking. Women with lower education level are more likely to smoke, so are more likely to be diagnosed with breast cancer-related diseases. Uong et al. 's study also shows that women with lower educational level are more likely to smoke ^[11]. This may be because women with lower education level have lower awareness of the harmful health of smoking, the harm of tobacco to the human body has a lag, with the increase of smoking age, the harmful effect of smoking accumulation will be slowly highlighted, this disease will become more obvious with the growth of age, we should find more effective tobacco control measures to increase tobacco control efforts for young people.

A number of studies have also confirmed that smoking can increase the risk of female breast cancer and is a controllable risk factor for female breast cancer ^[12–14]. Lai et al. found that current smoking status was significantly associated with an increased risk of overall and cancer-specific death in women with breast cancer ^[15]. Jones et al. also showed that smoking was significantly associated with increased risk of breast cancer, especially for women who started smoking around puberty or menarche ^[16]. Studies have also shown that menopausal status plays a key role in the relationship between tobacco and breast cancer for all cancer subtypes. While smoking appears to increase the risk of breast cancer in premenopausal women, it may be associated with a lower risk of breast cancer in postmenopausal women who are overweight ^[17]. Therefore, the relationship between menopausal status and smoking status in women remains uncertain and more research is needed to confirm it. Van et al. 's study showed that when premenopausal and postmenopausal smoking adjusted for each other, breast cancer risk was significantly positively correlated with premenopausal smoking pack year, but negatively correlated with postmenopausal smoking pack year, both in a dose-dependent manner. The study highlights the importance of differentiating and adjusting for smoking across different life periods and suggests a dual effect of smoking on postmenopausal breast cancer risk ^[18]. Jordahl et al. showed that for women previously diagnosed with carcinoma in situ, drinking an average of at least one alcoholic beverage per day was positively associated with invasive breast cancer. After adjusting for alcohol consumption, smoking was not significantly associated with the risk of developing invasive breast cancer ^[19]. The study data of Wang Qian et al. showed that alcohol consumption was correlated with the occurrence of breast cancer to some extent, and this correlation was different with the dose, frequency, time and type of alcohol consumption ^[20], but the results of this study did not show the correlation between alcohol consumption and mammary gland related lesions in the female screening population. Smoking and drinking have a synergistic effect on the occurrence of cancer. Therefore, actively promoting tobacco control and alcohol restriction is an important measure to improve residents' health and effectively control the cancer burden.

Studies by Allahqoli et al. show that the highest rates of breast cancer are found in countries of higher socioeconomic status, such as those in continental Europe. Although the incidence of breast cancer is lower in countries with a lower socio-demographic index (SDI), secondary mortality from breast cancer is higher in these countries than in high-income countries ^[21]. However, the data in this paper is limited by the content of the questionnaire on the platform, and the lack of income-related data makes it impossible to classify the socioeconomic status of the population. As a result, the Anderson model lacks the variables of enabling resources dimension, which reduces the richness of the interpretation of the results, which is one of the limitations of this paper.

With the rapid aging of the population in China, the incidence and mortality of cancer will also rise ^[22]. The results of this study confirm the importance of a healthy lifestyle. Publicity and education on smoking cessation should be further strengthened, and smokers should be advised. Promote health by forming a healthy lifestyle in all aspects.

Author Contributions:

Concept and design: Y.Y and J.H

Acquisition of data: R.Y and X.Z

Statistical Analysis: Q.S

Drafting of the manuscript: Q.S and J.H.

Analysis and interpretation of data: Q.S, L.M and J.H

Administrative, technical, or logistic support: L.M. and Y.Y

Obtaining funding: Y.H.

Critical revision of the paper for important intellectual content: Q.S, J.H, and Y.Y.

All authors reviewed the manuscript.

Funding/Support

This study was funded by Scientific Research Project of China Physicians' Joint Development Plan for Early Gastrointestinal Cancer (GTCZ-2022-GS-62-0004)

Ethical approval

This study was reviewed by the Drug Clinical Trial Committee of Wuwei Cancer Hospital of Gansu Province (Ethics Review No. 2023-35).

Role of the Funder

The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Acknowledgements

The authors thank all the residents who participated in the screening and the screening service providers who contributed to the project.

Competing interests

The authors declare that they have no conflicts of interest.

Consent

All residents participating in the screening program signed informed consent and volunteered to participate in clinical screening

Cao Maomao C. Interpretation of global cancer statistics in GLOBOCAN 2020 [J]. Chin J Front Med (Electronic edition). 2021;13(03):63–9.
Chen L, Ding GH, Yuan HR, et al. Prevalence characteristics of female breast cancer in tumor registration areas of Gansu Province in 2018 and change trend from 2010 to 2018 [J]. Chin Oncol. 2019;31(12):1001–7.
Zhou Tianhong G, Xiuying Y, Fang, et al. Analysis of breast cancer screening results in Urumqi from 2014 to 2018 [J]. J Practical Oncol. 2019;35(5):391–5.
Wang Jichuan W. Xiaoqian. Structural Equation Modeling: Applications Using Mplus[M]. Structural Equation Modeling: Applications using Mplus[M]. Higher Education Press; 2012.
Hair JFBW. Multivariate Data Analysis (6th ed.)[M]. Pren-tice-Hall, 2006.
Study on influencing factors. of treatment behavior of hypertension patients based on Anderson model [J]. Chin J Gen Med. 2020;23(03):333–7.
F H A JPK. Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear.[J]. Multivar Behav Res. 2010, 45(4).
Wang Mengcheng. Latent Variable Modeling and Mplus Application · Basic Part [M]. Chongqing University: 201401276.
Lu, Shan. Li Yue-e. Anderson Medical and Health service utilization behavior model: Interpretation and operationalization of index system [J]. Chin Health Econ. 2018;37(09):5–10.
Bray F, Laversanne M, Weiderpass E, et al. The ever-increasing importance of cancer as a leading cause of premature death worldwide[J]. Cancer. 2021;127(16):3029–30.
Uong SP, Torres JM, Alexeeff SE, et al. Differences in Smoking Behavior by Nativity, Race/Ethnicity, and Education Among Women Diagnosed with Breast Cancer. Cancer Epidemiol Biomarkers Prev Published online Febr. 2024;12. 10.1158/1055-9965.EPI-23-0386.
Wang Jing L. Correlation between breast cancer incidence and physical activity in women [J]. Chin J Healthc Med. 2023;25(04):419–21. (in Chinese).
Dapeng LU, Weilin Z. Association between dietary habits and breast cancer in women in Xingtai area [J]. South China J De?F Med. 2022;36(06):428–31. 10.13730/j.issn.1009-2595.2022.06.004. (in Chinese).
Scala M, Bosetti C, Bagnardi V, et al. Dose-response Relationships Between Cigarette Smoking and Breast Cancer Risk: A Systematic Review and Meta-analysis. J Epidemiol. 2023;33(12):640–8. 10.2188/jea.JE20220206.
Lai YC, Chen YH, Wu YC, et al. The Association between Smoking and Mortality in Women with Breast Cancer: A Real-World Database Analysis. Cancers (Basel). 2022;14(19):4565. 10.3390/cancers14194565. Published 2022 Sep 20.
Jones ME, Schoemaker MJ, Wright LB, Ashworth A, Swerdlow AJ. Smoking and risk of breast cancer in the Generations Study cohort. Breast Cancer Res. 2017;19(1):118. 10.1186/s13058-017-0908-4. Published 2017 Nov 22.
Peñalver-Argüeso B, García-Esquinas E, Castelló A, et al. Smoking history and breast cancer risk by pathological subtype: MCC-Spain study. Tob Induc Dis. 2023;21:157. 10.18332/tid/174132. Published 2023 Nov 30.
van den Brandt PA. A possible dual effect of cigarette smoking on the risk of postmenopausal breast cancer. Eur J Epidemiol. 2017;32(8):683–90. 10.1007/s10654-017-0282-7.
Jordahl KM, Malone KE, Baglia ML, et al. Alcohol consumption, smoking, and invasive breast cancer risk after ductal carcinoma in situ. Breast Cancer Res Treat. 2022;193(2):477–84. 10.1007/s10549-022-06573-9.
Wang Qian L, Zhen D, Min, et al. Relationship between lifestyle and malignant tumor research progress [J]. J community Med J. 2021;19(24):1501–4. The DOI: 10.19790 / j.carol carroll nki JCM. 2021.24.13.
Allahqoli L, Mazidimoradi A, Momenimovahed Z, et al. The Global Incidence, Mortality, and Burden of Breast Cancer in 2019: Correlation With Smoking, Drinking, and Drug Use. Front Oncol. 2022;12:921015. 10.3389/fonc.2022.921015. Published 2022 Jul 27.
Vollset SE, Goren E, Yuan CW, et al. Fertility, mortality, migration, and population scenarios for 195 countries andterritories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study[J]. Lancet. 2020;396(10258):1285–306.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
26 Mar, 2024
Editor assigned by journal
25 Mar, 2024
Submission checks completed at journal
25 Mar, 2024
First submitted to journal
18 Mar, 2024

You are reading this latest preprint version

The effect of smoking on breast cancer screening results in female population:a study in Wu wei, China

Status:

Version 1

Abstract

Figures

Introduction

Method

1.1 Data Analysis

Results

2.1 Description of basic situation of breast cancer screening population

2.2 Distribution of scenario characteristic dimension variables

2.3 Distribution of personal characteristic dimension variables

2.4 Distribution of dimensional characteristics of medical behavior

2.5 Results of population pathway model analysis for female breast cancer screening in 2021–2023

Discussion

Declarations

References

Additional Declarations

Status:

Version 1