【作者】 李俊；

【导师】 费腾；

【作者基本信息】 武汉大学 ， 地图学与地理信息系统， 2021， 硕士

【摘要】 乙型肝炎是中国最严重、最常见的病毒性肝炎。乙肝病毒不仅对患者的身体产生病痛折磨,还对患者的精神产生影响,也就是歧视。先前有关乙肝歧视的研究主要集中在针对个人层面的各种统计分析上,没有考虑个人所属的更高层级,例如村庄、城市等带来的交互影响,更缺乏对乙肝歧视地域性差异的进一步探讨。鉴于此,本研究基于在我国广大的农村地区展开的问卷调查数据,引入双层线性模型和地理探测器作为具体的分析手段,重点从个人尺度和村庄尺度两个角度出发,探究影响乙肝歧视的因素。一方面,本研究以个人乙肝歧视水平为研究对象,在考虑个人层面相关变量的基础上,将个人所属区域层面的变量纳入进来,构建更加完整的双层线性回归模型。通过最终确定的模型可知,个人乙肝歧视水平除了受到个人层面变量的影响之外,还受到村庄层面变量的影响。其中,影响个人乙肝歧视水平的微观因素包括家庭经济水平、乙肝认知和乙肝疫苗接种;宏观因素也就是村庄层面的因素,包括灯光强度、路网密度和建设用地占比。另一方面,本研究以区域乙肝歧视均值为研究对象,通过线性关系结果与前人研究的矛盾之处,提出区域乙肝歧视均值与影响因素之间可能存在非线性关系的假设,并使用地理探测器,在不限定于线性假设的条件下,探究区域乙肝歧视均值与影响因素之间的关系。通过风险探测器的结果可知,各自变量与乙肝歧视之间的关系存在多样的非线性。这些非线性关系进一步表明,经济发展和加速的城市化不能自动消除乙肝歧视。除了由医疗机构开展的宣传运动外,还必须加强对学生的教育,以便他们从小就对乙肝有清晰而正确的认识。当新生儿在医院接受强制性乙肝疫苗接种时,应进行成人教育。为了防止人们误以为是乙肝病毒的病因和后果,政府监管机构需要更严格地监管非法医疗机构和虚假夸大行为。最后,需要加强村庄级的个人隐私教育,尤其是保护个人病历,以确保对乙肝病毒感染者保持匿名和保密,这限制了歧视的可能性。除此之外,本研究通过构建神经网络模型以定量分析乙肝歧视均值与影响因素之间的关系。模型验证集的~2为0.85,说明模型的拟合效果较好。更多还原

【Abstract】 Hepatitis B(HB)is the most serious and common viral hepatitis in China.Hepatitis B virus(HBV)not only causes pain and suffering to the patient’s body,but also affects the patient’s spirit,which is discrimination.Previous studies on HB discrimination mainly focused on various statistical analyses at the individual level,without considering the interaction of higher levels of individuals,such as villages,cities,etc.,and lack of further discussion on the regional differences in HB discrimination.In view of this,based on the questionnaire survey data carried out in the vast rural areas of China,this study introduced two-layer linear model and geographical detector as specific analysis methods,focusing on the personal scale and the village scale to explore the influence of HB discrimination factors.On the one hand,taking the level of discrimination of individuals against HB as the research object,this study takes into account the relevant variables both at the individual level and at the regional level to construct a more complete two-layer linear regression model.The finalized model indicated that in addition to the impact of individual-level variables,the level of individual HB discrimination was also affected by village-level variables.Among them,the micro factors that affected the level of individual HB discrimination include family economic level,HB awareness,and HB vaccination;macro factors were also village-level factors,including night light intensity,road network density,and the proportion of construction land.On the other hand,taking the regional average of HB discrimination as the research object,this study proposed the hypothesis that there may be a non-linear relationship between the regional average of HB discrimination and influencing factors based on the contradiction between the linear relationship results and previous studies,and employed the geographic detection to explore the relationship between the mean value of regional HB discrimination and influencing factors under the condition of not being limited to linear assumptions.The results of the risk detector showed that there were various nonlinearities in the relationship between the respective variables and HB discrimination.These nonlinear relationships further indicated that economic development and accelerated urbanization cannot automatically eliminate HB discrimination.In addition to the publicity campaigns carried out by medical institutions,the education of students must be strengthened so that they have a clear and correct understanding of HB from an early age.When newborns receive mandatory HB vaccination in the hospital,adult education should be carried out.In order to prevent people from mistakenly thinking that the cause and consequences of HBV,government regulatory agencies need to more strictly supervise illegal medical institutions and false exaggerations.Finally,it is necessary to strengthen personal privacy education at the village level,especially to protect personal medical records,to ensure the anonymity and confidentiality of people infected with HBV,which limits the possibility of discrimination.In addition,this study employed a neural network model to quantitatively analyze the relationship between the mean value of HB discrimination and influencing factors.The~2 of the model validation set is 0.85,indicating that the model fitted well.更多还原