HYDROCARBON EXPOSURE & CHRONIC RENAL DISEASE

将进行一项正在进行的病例对照研究，调查 慢性接触碳氢化合物导致慢性肾功能衰竭。 因为越来越多的终末期肾病患者 (ESRD)，那些需要透析或进行了肾移植的人，他们 已经成为医疗保险支出的一个主要因素。澄清 导致ESRD并找到控制它们的方法的过程是最多的 减少个人、社会和经济困难的有效途径 在这种情况下。 在俄克拉荷马州的俄克拉何马州和塔尔萨县地区 患有慢性肾功能衰竭(包括患有终末期肾病和尚未发生终末期肾病的患者 那个阶段)确诊为慢性肾小球肾炎 (CGN)将被确定为案例，并作为案例进入研究。全新的 未来三年符合这些要求的案件也将是 包括在内。 从一般人群中选择年龄-性别匹配的对照组 使用随机数字拨号技术的人口。目标数量 病例和对照总数将分别为500例和500例。每个案件和控制都将 执行了一份调查问卷，以获取有关职业的信息 病史、病史和家族病史以及相关 人口统计数据。 碳氢化合物暴露指数将被计算并用于 血吸虫病危险因素的单因素和多因素统计分析 慢性肾功能衰竭。单变量分析将使用赔率比 多变量分析将采用考克斯的线性Logistic 回归模型。

An on-going case-control study will be conducted investigate the role of chronic exposure to hydrocarbons in causing chronic renal failure. Because of an ever increasing number of end-stage renal disease patients (ESRD), those requiring dialysis or having had a renal transplant, they have become a major factor in Medicare expenditures. Elucidating the processes leading to ESRD and finding ways to control them are the most valid ways to reduce the personal, social and economic hardships produced by this condition. In the Oklahoma and Tulsa county areas of Oklahoma all adult patients with chronic renal failure (both those with ESRD and those not yet at that stage) having a confirmed diagnosis of chronic glomerulonephritis (CGN) will be identified and entered into the study as cases. All new cases over the next three years meeting these requirements will also be included. An age-sex matched control group will be selected from the general population using a random digit dialing technique. The target number of total cases and controls will be 500 of each. Each case and control will be administered a questionnaire which will elicit information on occupational hisotry, medical history and family medical history as well as pertinent demographic data. An exposure index to hydrocarbons will be calculated and used in an univariate and multivariate statistical analysis of risk factors for chronic renal failure. The univariate analysis will employ the Odds Ratio and the multivariate analysis will employ Cox's Linear Logistic Regression Model.