Whitfield Malcolm D, Gillett Michael, Holmes Michael, Ogden Elaine
ScHARR, University of Sheffield, Sheffield S1 4DA, UK.
Public Health. 2006 Dec;120(12):1140-8. doi: 10.1016/j.puhe.2006.10.012. Epub 2006 Nov 3.
The brief for this study was to produce a practical, evidence based, financial planning tool, which could be used to present an economic argument for funding a public health-based prevention programme in coronary heart disease (CHD) related illness on the same basis as treatment interventions.
To explore the possibility of using multivariate risk prediction equations, derived from the Framingham and other studies, to estimate how many people in a population are likely to be admitted to hospital in the next 5-10 years with cardio vascular disease (CVD) related events such as heart attacks, strokes, heart failure and kidney disease. To estimate the potential financial impact of reductions in hospital admissions, on an 'invest to save' basis, if primary care trusts (PCTs) were to invest in public health based interventions to reduce cardiovascular risk at a population level.
The populations of five UK PCTs were entered into a spreadsheet based decision tree model, in terms of age and sex (this equated to around 620,000 adults). An estimation was made to determine how many people, in each age group, were likely to be diabetic. Population risk factors such as smoking rates, mean body mass index (BMI), mean total cholesterol and mean systolic blood pressure were entered by age group. The spreadsheet then used a variant of the Framingham equation to calculate how many non-diabetic people in each age group were likely to have a heart attack or stroke in the next 5 years. In addition heart failure and dialysis admission rates were estimated based upon risk factors for incidence. The United Kingdom Prospective Diabetes Study (UKPDS) risk engines 56 and 60 were used to calculate the risk of CHD and stroke, respectively, in people with type 2 diabetes. The spreadsheet deducted the number of people likely to die before reaching hospital and produced a predicted number of hospital admissions for each category over a 5-year period. The final part of the calculation attached a cost to the hospital activity using the UK Health Resource Grouping (HRG) tariffs. The predicted number of events in each of the primary care trusts was then compared with the actual number of events the previous year (2004/2005).
The study used a decision tree type model, which was populated with data from the research literature. The model applied the risk equations to population data from five primary care trusts to estimate how many people would suffer from an acute CVD related event over the next 5 years. The predicted number of events was then compared with the actual number of acute admissions for heart attacks, strokes, heart failure, acute hypoglycaemic attacks, renal failure and coronary bypass surgery the previous year.
The first outcome of the model was to compare the estimated number of people in each PCT likely to suffer from a heart attack, a stroke, heart failure or chronic kidney failure with the actual number the previous year 2004/2005. The predicted number was remarkably accurate in the case of heart attack and stroke. There was some over-prediction of chronic kidney disease (CKD) which could be accounted for by known under-diagnosis in this illness group and the inability of the model to pick up, at this stage, the fact that many CKD patients die of a CHD related event before they reach the stage of requiring renal replacement. The second outcome of the model was to estimate the financial consequence of risk reduction. Moderate reductions in risk in the order of around 2-4% were estimated to lead to saving in acute admission costs or around pounds sterling 5.4 million over 5 years. More ambitious targets of risk reduction in the order of 5-6% led to estimated savings of around pounds sterling 8.7 million.
This study is not presented as the definitive approach to predicting the economic consequences of investment in public health on the cost of secondary care. It is simply a logical, systematic approach to quantifying these issues in order to present a business case for such investment. The research team do not know if the predicted savings would accrue from such investments; it is theoretical at this stage. The point is, however, that if the predictions are correct then the savings will accrue from over 4000 people, from an adult population of around 185,000 not having a heart attack or a stroke or an acute exacerbation of heart failure.
本研究的任务是制作一个实用的、基于证据的财务规划工具，该工具可用于像提出治疗干预措施那样，为资助一项基于公共卫生的冠心病（CHD）相关疾病预防项目阐述经济论据。
探讨使用源自弗雷明汉研究及其他研究的多变量风险预测方程，来估计在未来5至10年内，某一人群中有多少人可能因心脏病发作、中风、心力衰竭和肾病等心血管疾病（CVD）相关事件而住院。若初级保健信托基金（PCTs）投资基于公共卫生的干预措施以在人群层面降低心血管疾病风险，以“投资以节省”为基础，估计住院人数减少所带来的潜在财务影响。
英国五个初级保健信托基金的人群按年龄和性别录入基于电子表格的决策树模型（这相当于约62万成年人）。估算各年龄组中可能患糖尿病的人数。各年龄组录入吸烟率、平均体重指数（BMI）、平均总胆固醇和平均收缩压等人群风险因素。然后电子表格使用弗雷明汉方程的一个变体，计算各年龄组中在未来5年内可能心脏病发作或中风的非糖尿病患者人数。此外，根据发病风险因素估计心力衰竭和透析住院率。英国前瞻性糖尿病研究（UKPDS）风险引擎56和60分别用于计算2型糖尿病患者患冠心病和中风的风险。电子表格减去可能在住院前死亡的人数，并得出5年内各类别预计的住院人数。计算的最后部分使用英国卫生资源分组（HRG）收费标准为医院活动附加成本。然后将每个初级保健信托基金预计的事件数与上一年（2004/2005年）的实际事件数进行比较。
该研究使用决策树类型模型，模型填充了来自研究文献的数据。该模型将风险方程应用于五个初级保健信托基金的人群数据，以估计未来5年内将有多少人会发生急性心血管疾病相关事件。然后将预计的事件数与上一年心脏病发作、中风、心力衰竭、急性低血糖发作、肾衰竭和冠状动脉搭桥手术的急性住院实际数进行比较。
模型的第一个结果是将每个初级保健信托基金中预计可能患心脏病发作、中风、心力衰竭或慢性肾衰竭的人数与2004/2005年上一年的实际人数进行比较。心脏病发作和中风的预测数非常准确。慢性肾病（CKD）存在一些预测过高的情况，这可能是由于该疾病组已知的诊断不足，以及模型在此阶段无法识别许多慢性肾病患者在达到需要肾脏替代阶段之前死于冠心病相关事件这一事实。模型的第二个结果是估计降低风险的财务后果。估计风险适度降低约2 - 4%会导致急性住院成本节省，或在5年内节省约540万英镑。更宏伟的降低风险目标约为5 - 6%，预计节省约870万英镑。
本研究并非作为预测公共卫生投资对二级医疗成本的经济后果的权威方法呈现。它只是一种逻辑、系统的方法来量化这些问题，以便为这种投资提出商业理由。研究团队不知道这些预计的节省是否会因此类投资而产生；现阶段这只是理论上的。然而，关键在于，如果预测正确，那么节省将来自超过4000人，这4000人来自约18.5万成年人，他们不会发生心脏病发作、中风或心力衰竭急性加重。
