Sumner Walton, Schootman Mario, Asaro Philip, Yan Yan, Hagen Michael D
Washington University School of Medicine, St Louis, MO 63110, USA.
J Contin Educ Health Prof. 2008 Fall;28(4):197-204. doi: 10.1002/chp.186.
Medical education topics might be locally prioritized using public health data on health outcomes and risk factors unrelated to quality of care.
The Missouri Information for Community Assessment (MICA) supplied preventable hospitalization rates (PHRs) for asthma, chronic obstructive pulmonary disease (COPD), diabetes, heart failure, and hypertension in 114 counties from 1998 to 2002. For each disease, a linear regression model predicted PHR from behavior, access, and disease prevalence data from MICA and other public data sources. For each disease in each county, the residual, unexplained PHR should include effects of local medical practices. Variation in relative priority of diseases between counties was estimated from raw PHR and unexplained PHR.
The raw values of the five PHRs varied geographically in different patterns. Regression models explained between 46% and 83% of the variability. The medical education priorities implied by unexplained PHR values differ from priorities inferred from unadjusted PHR or disease prevalence.
Patient behavior and poor health care access contribute to PHR but do not fully explain variation in PHR. If county-level unexplained PHR values identify high priority medical education topics, then other measures of importance, notably disease prevalence and PHR, are poor identifiers of high value topics. Although available predictor and outcome variables constrain the current analysis, unexplained variation in health outcome measures might identify educational opportunities. These observations suggest strategies for balancing and evaluating controlled trials of knowledge dissemination efforts and eventually for deploying educational activities.
医学教育主题可利用与医疗质量无关的健康结果和风险因素的公共卫生数据按地区确定优先次序。
密苏里州社区评估信息（MICA）提供了1998年至2002年114个县哮喘、慢性阻塞性肺疾病（COPD）、糖尿病、心力衰竭和高血压的可预防住院率（PHR）。对于每种疾病，线性回归模型根据MICA和其他公共数据源的行为、可及性和疾病患病率数据预测PHR。对于每个县的每种疾病，剩余的、无法解释的PHR应包括当地医疗实践的影响。根据原始PHR和无法解释的PHR估计各县之间疾病相对优先级的差异。
五个PHR的原始值在地理上呈现不同的变化模式。回归模型解释了46%至83%的变异性。无法解释的PHR值所暗示的医学教育优先事项与从未调整的PHR或疾病患病率推断出的优先事项不同。
患者行为和医疗服务可及性差导致了PHR，但不能完全解释PHR的变化。如果县级无法解释的PHR值确定了高度优先的医学教育主题，那么其他重要性衡量指标，尤其是疾病患病率和PHR，对于高价值主题的识别效果不佳。尽管现有的预测变量和结果变量限制了当前的分析，但健康结果指标中无法解释的变异可能会识别出教育机会。这些观察结果为平衡和评估知识传播努力的对照试验以及最终开展教育活动提供了策略。
