摘要
目的了解陕西省育龄妇女产前检查现状及卫生保健意识,探讨其影响因素,为进一步制定干预措施提供基线资料和依据。方法运用方差分析和多元线性回归分析对孕妇产前检查状况及其影响因素进行分析。结果本地区孕产妇产前检查率为97.5%,有87.3%的孕产妇选择在县级或县级以上医疗卫生机构进行产前检查,接受过5次及以上产前检查的占67.7%,而孕12周内接受第一次产前检查的妇女占83.93%,产前检查频次受妇女的社会人口学特征、婚育史和妊娠期基本状况等诸多因素的影响。结论本地区居民产前保健意识已经初步形成,但仍需进一步提高,有针对性地筛选并对高危孕妇进行产前健康管理和教育具有重要意义。
OBJECTIVE To study the current status of antenatal examination and health care consciousness of Shaanxi Province, explor-ing the determinants of antenatal examination to provide baseline data and basis for the development of interventions. METHODS Ac-cording to the population density and fertility levels, it selected 16~50 years-old women of childbearing age as the research subjectusing multistage random sampling method in Shaanxi province during 2010~2013, and a questionnaire investigation was carried out tocollect social and demographic information, marriage and birth history, basic situation during pregnancy etc. Variance analysis andmultiple linear regression were used to analyze the antenatal examination and its determinants. RESULTS The antenatal examinationrate in this region was 97.5%, higher than the national average of 94.1% in 2010. 87.3% of the total people chose to do antenatal ex-amination mainly in medical and health institutions at the county level health organization or above. More than 83.9% of pregnantwomen underwent their first examinations at the early stage of pregnancy. CONCLUSION Awareness of prenatal care among mothershas been formed in this region, but still further improvement was well needed for prenatal health care. The frequency of antenatal ex-amination was influenced by social demographic characteristics, women's obstetrical history, pregnancy status and other factors. Ithad great significance to screen and manage high-risk pregnant women who should be put more attention in prenatal health care andeducation.
出处
《中国初级卫生保健》
2018年第5期20-23,共4页
Chinese Primary Health Care
基金
国家自然科学基金资助项目(No.81230016)
关键词
产前检查
现况调查
影响因素
方差分析
多元线性回归分析
antenatal examination
field investigation
influential factors
variance analysis
multiple linear regression
