Epidemiology And Etiology Study Of Female Uterine Myomas

IntroductionUterine leiomyomas are the most common uterine neoplasm and are composed of smooth muscle with varying amounts of fibrous connective tissue. Occurring in women of reproductive age, frequently found in women between the ages 41-50. Fibroids can also grow beneath the uterine lining. Small fibroids had no symptoms. As they expand, they can cause menstrual disorder, heavy menstrual bleeding, urgency, constipation and severe pain. Myomatous anaplasia can cause acute abdomen and infertilitas feminis. Large uterine fibroids can cause the cancer of the cervix and carcinoma of corpus uteri, frequency of canceration is 0.13%～1.39%.At present, study of uterus myoma presumed that the prevelence of uterus myoma was difficult to statistic. Because many nonsymptomatic patients did not visit and discover, the prevalence of uterus myoma had more difference in different area. Occurrence of uterus myoma was unification of various factors, but the specific factor did not discover. So it is important to explore the morbidity and the risk of uterine leiomyoma in epidemiology and etiology aspect and to provide the reasonable proofs of preventing and controlling uterine leiomyoma. This section include: 1. Population study. A descriptive statistics were used to describe the population characteristics of uterine leiomyoma, and an 1:2 matched case-control studie was conducted to analyze the related influencing factors of uterine leiomyomas. 2. Neural network study: BP network were used to analyzed the mean impact value (MIV) for each input variables, and compared with multiple Logistic regression, to evaluate the value of a back propagation (BP) network on analyzing the risk factors of uterine myomas. 3. Tendency study: BP network were used to training set to predict the prevalence rate of uterine myomas, in order to find the relationship between the crucial factors and prevalence rate of uterine myomas, and to give clues for population prevention of uterine myomas.Objectives1. To describe the morbidity and the population characteristics of uterine leiomyomas among women in Shenyang; to analyze the related influencing factors of uterine leiomyomas, and find out the risk factors of uterine leiomyomas morbidity from women's reproductive health condition, working conditions, living habits, sexual actions, emmenia and reproductive history, contraceptive methods, etc; and to provide the reasonable proofs of preventing and controlling uterine myomas.2. By compareing with Logistic Logistic regression analysis, to evaluate the value of a back propagation (BP) network on analyzing the risk factors of uterine myomas.3. To evaluate the applying value of BP network in uterine myomas prediction, in order to explore the relationship between the crucial factors and prevalence rate of uterine myomas, and to give clues for population prevention of uterine myomas.Methods1. Using stratified random sampling method, 1260 women were surveyed by questionnaire, we acquired the prevalence of uterine leiomyoma, 1:2 matched case-control study was used to explore the influential factors of Uterine myomas. The statistics methods included the matched x² test, univariate analysis, and conditional multinomal Logistic regressiona nalysis.2. 1:2 matched case-control study was conducted with 112 cases of uterine myomas. Neural network tools box of Software MATLAB 6.5 was used to train and simulate BP artificial network. Analyzed the mean impact value (MIV) for each input variables, and compared with multiple Logistic regression analysis and log-linear model for interaction between factors.3. The data of uterine myomas in Shenyang from 1993 to 2005 were used as a training set to predict the prevalence rate of uterine myomas. Neural network tools box of Software MATLAB 6.5 was used to structure, train and simulate BP Artificial Neural Network.Results1. Morbidity of uterine myomas: 1260 women were surveyed in this study. 112 of them were uterine myomas patients. The average morbidity rate of uterine myomas was 8.89%. The average age was 41.98±0.34 years old . The group aged 40 to 49 had the highest morbidity rate. The different group morbidity rates were not statistically significant among women of different occupations.2. Influencing factors: The risk factors of uterine myomas include: menstrual disorder, pelvic inflammatory, cervicitis, oral contraceptive medication, elytritis, and induced abortion, respectively while delayed menstruation was proved to be a preventive factor.3. BP artificial neural analysis showed that the leading risk factors for uterine myomas were delayed menstruation, family history of uterine myomas, cervicitis, menstrual disorder, induced abortion, pelvic inflammatory, oral contraceptive medication, and elytritis, with mean impact value -0.0405, 0.0361, 0.0162, 0.0143, 0.0135, 0.0117, 0.0094, 0.0087, respectively. Both BP artificial neural and Logistic regression analysis showed that the sequence of leading risk factors were similar in the whole, but there were some differences, induced abortion was proved to be an important cooperation variable through logline model analysis respectively.4. Using the data of the year 1993～2003 to predict the prevalence of uterine myomas in 2004～2005, the results showed that: Using BP neural network, the fittingaverage error of incidence was 3.17%, R_NL was 0.9592, and the predict in averageerror was 1.01%.Using the ARIMA model,the fitting average error of incidence was 13.01%, R_NL was0.8514, and the predict in average error was 12.39%;Using theGM(1,1), the fitting average error of incidence was 5.96%, R_NL was0.9491, and the predict in average error was 3.15%; Using the data of the year 1993～2005 to predict the prevalence of uterine myomas in 2006～2009, the results showed that : the fittingaverage error was 0.52%,R_NL=0.9944,the incidence of uterine myomas in 2006～2009 were 8.67%, 8.74%, 8.87%, 8.91%.Conclusions1. Uterine myomas seemed to be related to menstrual disorder, pelvic inflammatory, cervicitis, elytritis, and induced abortion. There is a great need for emphasizing culturally acceptable reproductive health education in various kinds of risk factor to improve women's knowledge about uterine myomas.2. Study the risk factors of disease with BP artificial neural network, the information quantity supplied by artificial neural network are richer than traditional model such as Logistic regression, it wouldn't be affected by the relations among factors, and no affected by noise, it can fit all relation between input and output in one time, it is not only help to find the unknown but complicating relations among factors. In addition, when analysis the risk factors of disease with NN, the distribution form of variables can be any, so it is very fit for studying any causality, specially the relationship of having no any previous understanding about variables. So it provided a powerful method to risk factor analysis.3. In the next few years , the incidence of uterine myomas will show a slowly rising trend.