Thompson Michael P, Zhao Xin, Bekelis Kimon, Gottlieb Daniel J, Fonarow Gregg C, Schulte Phillip J, Xian Ying, Lytle Barbara L, Schwamm Lee H, Smith Eric E, Reeves Mathew J
From the Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN (M.P.T.); Duke Clinical Research Institute, Durham, NC (X.Z., Y.X., B.L.L.); Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH (K.B., D.J.G.); Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, Los Angeles, CA (G.C.F.); Department of Health Science Research, Mayo Clinic, Rochester, MN (P.J.S.); Department of Neurology, Duke University Medical Center, Durham, NC (Y.X.); Department of Neurology, Massachusetts General Hospital, Boston, MA (L.H.S.); Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada (E.E.S.); and Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI (M.J.R.).
Circ Cardiovasc Qual Outcomes. 2017 Aug;10(8). doi: 10.1161/CIRCOUTCOMES.117.003604.
We explored regional variation in 30-day ischemic stroke mortality and readmission rates and the extent to which regional differences in patients, hospitals, healthcare resources, and a quality of care composite care measure explain the observed variation.
This ecological analysis aggregated patient and hospital characteristics from the Get With The Guidelines-Stroke registry (2007-2011), healthcare resource data from the Dartmouth Atlas of Health Care (2006), and Medicare fee-for-service data on 30-day mortality and readmissions (2007-2011) to the hospital referral region (HRR) level. We used linear regression to estimate adjusted HRR-level 30-day outcomes, to identify HRR-level characteristics associated with 30-day outcomes, and to describe which characteristics explained variation in 30-day outcomes. The mean adjusted HRR-level 30-day mortality and readmission rates were 10.3% (SD=1.1%) and 13.1% (SD=1.1%), respectively; a modest, negative correlation (=-0.17; =0.003) was found between one another. Demographics explained more variation in readmissions than mortality (25% versus 6%), but after accounting for demographics, comorbidities accounted for more variation in mortality compared with readmission rates (17% versus 7%). The combination of hospital characteristics and healthcare resources explained 11% and 16% of the variance in mortality and readmission rates, beyond patient characteristics. Most of the regional variation in mortality (65%) and readmission (50%) rates remained unexplained.
Thirty-day mortality and readmission rates vary substantially across HRRs and exhibit an inverse relationship. While regional variation in 30-day outcomes were explained by patient and hospital factors differently, much of the regional variation in both outcomes remains unexplained.
我们探讨了30天缺血性卒中死亡率和再入院率的地区差异,以及患者、医院、医疗资源和综合护理质量指标方面的地区差异在多大程度上解释了观察到的差异。
这项生态学分析汇总了来自“遵循卒中指南”注册研究(2007 - 2011年)的患者和医院特征、来自《达特茅斯医疗地图集》(2006年)的医疗资源数据,以及医疗保险按服务付费数据中关于30天死亡率和再入院率(2007 - 2011年)至医院转诊区域(HRR)层面的数据。我们使用线性回归来估计调整后的HRR层面的30天结局,识别与30天结局相关的HRR层面特征,并描述哪些特征解释了30天结局的差异。调整后的HRR层面30天死亡率和再入院率的均值分别为10.3%(标准差 = 1.1%)和13.1%(标准差 = 1.1%);两者之间存在适度的负相关(r = -0.17;P = 0.003)。人口统计学因素对再入院差异的解释比对死亡率差异的解释更多(25%对6%),但在考虑人口统计学因素后,合并症对死亡率差异的解释比再入院率差异更多(17%对7%)。医院特征和医疗资源的组合分别解释了超出患者特征的死亡率和再入院率差异的11%和16%。死亡率(65%)和再入院率(50%)的大部分地区差异仍无法解释。
30天死亡率和再入院率在各HRR之间差异很大,且呈负相关。虽然患者和医院因素对30天结局的地区差异解释方式不同,但这两种结局的大部分地区差异仍无法解释。