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Hospital Spending Intensity and Readmission 20130806 Speaker: Chih-Yuan Huang Corresponding Author: Ching-Chih Lee.

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Presentation on theme: "Hospital Spending Intensity and Readmission 20130806 Speaker: Chih-Yuan Huang Corresponding Author: Ching-Chih Lee."— Presentation transcript:

1 Hospital Spending Intensity and Readmission 20130806 Speaker: Chih-Yuan Huang Corresponding Author: Ching-Chih Lee

2 Introduction(1) It is believed that good hospital quality lead to good outcomes. And hospital quality depends on effective measures, more measures require more cost or spending. Thus we expected that hospital spending intensity is proportional to hospital quality and then, the most important, good outcomes. However, the results are conflicting. For example, those favor high spending is associated with better outcomes: Silber JH, Health Serv Res. 2010;45(6, pt 2):1872-1892 Barnato AE, Med Care. 2010;48(2):125-132 And those do not: Fisher ES, Ann Intern Med. 2003;138(4):273-287 Fisher ES Ann Intern Med. 2003;138(4):288-298 Kaplan RM,J Palliat Med. 2011 Feb;14(2):215-20. Yasaitis L, Health Aff (Millwood). 2009 Jul-Aug;28(4):w566-72.

3 Introduction(2) “Reverse causality”( 倒因為果 ): Sicker patients use more services, higher-spending hospitals may appear to have worse outcomes, in part because patients are more severely ill. To avoid this: 1. Selected acute conditions who were likely to present with similar mean illness severity, rather than all admissions. 2. The exposure of interest was spending at the hospital level rather than the patient level. 3. Estimates of a hospital’s spending intensity, were based on individuals in their last year of life, to further remove potential reverse causality between study cohort illness and spending. Disease severity Spending intensity outcome More spending, better outcome More severity, poorer outcome More severity, more spending

4 EOL spending intensity-hospital attribute Hospitals' end-of-life intensity varies in the use of specific life-sustaining treatments that are somewhat emblematic of aggressive end- of-life care. End-of-life intensity is a relatively stable hospital attribute that is robust to multiple measurement approaches. Barnato AE, Med Care. 2009 Oct;47(10):1098-105.

5 Hospital Spending Index The primary exposure measure was the hospital end-of-life expenditure index (EOL-EI), calculated as the mean adjusted spending per capita on hospital, emergency department (ED), and physician services provided to decedents in their last 6 months in their life in our study.

6 Method(1) We use NHIRD 2002-2004 and choose patients who were admitted due to major diseases such as AMI, Stroke, CHF, Pneumonia, DM, and Liver cirrhosis. Patients were enrolled according to their earliest admission(index admission) and underwent follow-up for 1 year after the index admission date.

7 Methods(2) We divided hospitals into three quatiles of different end-of-life spending intensity by low, moderate and high spending index, as exposure, to explore the outcomes of the selected diseases under different spending intensities.

8 Method(3) The primary outcomes: 30-day and 1-year mortality + 30-day and 1-year readmissions Cox proportional hazards models were used to compare rates of mortality and readmissions across hospital expenditure categories.

9 Table 1. Baseline characteristics, According to Hospital Expenditure Index. (n=890431) Characteristics Hospital Expenditure Index P value High (n=299109) Moderate (n=288456) Low (n=302866) N% N% N% Disease <0.001 AMI219357.3 220367.6 202346.7 Stroke5714619.1 5833020.2 5935619.6 Congestive heart failure214857.2 202527.0 221577.3 Pneumonia10127633.9 9401732.6 10015033.1 DM7761125.9 7387125.6 8065226.6 Liver cirrhosis196566.6 199506.9 203176.7 Age, years (mean ±SD)58±26 57±26 58±25<0.001 Age group <0.001 <446648822.2 6375022.1 6747322.3 45-543267110.9 3238911.2 3120510.3 55-644453414.9 4446715.4 4226214.0 65-747206524.1 7000524.3 7204723.8 ≧ 75 8335127.9 7784527.0 8987929.7 Gender <0.001 Male17425658.3 16524957.3 51432857.8 Female12485341.7 12320742.7 37610342.2 CCIS (mean ±SD) <0.001 0 10723535.9 10125535.1 10762935.5<0.001 ≧ 1 19187464.1 18720164.9 19523764.5 Socioeconomic status <0.001 High 10081733.7 8855930.7 8382927.7 Moderate 11341437.9 12548643.5 12754442.1 Low 8487828.4 7441125.8 9149330.2 Urbanization <0.001 Urban9258731.0 6133821.3 5065116.7 Suburban12514941.8 1247443.2 13566444.8 Rural8137327.2 10237135.5 11655138.5 Geographic region <0.001 Northern 19395664.8 12968145.0 13738645.4 Central 3622912.1 5392818.7 6196120.5 Southern 6510021.8 8831130.6 8822429.1 Eastern 38241.3 165365.7 152955.1 Caseload (mean ±SD) 797±1040 698±1178 170±291<0.001 Hospital characteristics Hospital ownership <0.001 Public9551631.9 6204821.5 9751632.2 Nonprofit14776449.4 15379453.3 9492131.3 Profit5582918.7 7261425.2 11042936.5 Hospital accreditation level <0.001 Medical center16480855.1 14302349.6 4146713.7 Regional hospital11603338.8 10752037.3 14465147.8 District hospital182686.1 3791313.1 11674838.5

10 Table 2. Outcomes for the disease patients, According to Hospital expenditure index Cohort Outcomes Hospital Expenditure Index P value High (n=299109) Moderate (n=288456) Low (n=302866) n% n% n% AMI2193534.2 2203634.3 2023431.5 Death Within 30 days of admission5452.5 4472.0 4492.2<0.001 Within 1year of admission14576.6 12105.5 13256.5<0.001 Major cardiac event Within 30 days of readmission16707.6 17117.8 203410.1<0.001 Within 1year of readmission610227.8 595627.0 589429.1<0.001 Stroke5714632.7 5833033.4 5935634.0 Death Within 30 days of admission12112.1 9621.6 9651.6<0.001 Within 1year of admission34786.1 30895.3 34105.7<0.001 Stroke event Within 30 days of readmission56119.8 56539.7 749412.6<0.001 Within 1year of readmission1979434.6 2004234.4 2266438.2<0.001 Congestive heart failure2148533.6 2025231.7 2215734.7 Death Within 30 days of admission4272.0 9821.9 4412.00.684 Within 1year of admission19889.3 17018.4 20179.10.005 Major cardiac event Within 30 days of readmission21289.9 220310.9 254211.5<0.001 Within 1year of readmission924443.0 872943.1 937042.30.169 Pneumonia10127634.3 9401731.8 10015033.9 Death Within 30 days of admission30113.0 20662.2 25212.5<0.001 Within 1year of admission89588.8 61946.6 76117.6<0.001 Pneumonia event Within 30 days of readmission43154.3 39994.3 54175.4<0.001 Within 1year of readmission1947019.2 1812519.3 2286022.8<0.001 DM7761133.4 7387131.8 8065234.7 Death Within 30 days of admission5780.7 5070.7 7000.9<0.001 Within 1year of admission33034.3 28123.8 36334.5<0.001 DM event Within 30 days of readmission49686.4 52917.2 69038.6<0.001 Within 1year of readmission2620533.8 2605835.3 3031537.6<0.001 Liver cirrhosis1965632.8 1995033.3 2031733.9 Death Within 30 days of admission6433.3 5362.7 6032.70.003 Within 1year of admission265113.5 19849.9 217210.7<0.001 Liver cirrhosis event Within 30 days of readmission223411.4 236711.9 272013.4<0.001 Within 1year of readmission1016851.7 1032151.7 1062052.30.457

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12 Table 3. Odds ratios for the mortality, According to Hospital expenditure index Variable 2002 2003 2004 OR95% CIP value OR95% CIP value OR95% CIP value AMI (n=64205) Within 30 days of admission High1 1 1 Moderate0.770.61-0.990.038 0.990.78-1.260.968 1.240.99-1.550.067 Low0.790.60-1.040.095 1.200.91-1.590.202 1.190.90-1.590.230 Within 1year of admission High1 1 1 Moderate0.810.70-0.940.006 1.090.94-1.270.251 1.191.03-1.380.020 Low0.920.78-1.090.346 1.160.97-1.380.105 1.211.02-1.450.032 Stroke (n=174832) Within 30 days of admission High1 1 1 Moderate0.780.68-0.900.001 0.830.71-0.980.026 0.960.82-1.120.577 Low0.730.62-0.85<0.001 0.940.79-1.120.493 0.940.78-1.130.499 Within 1year of admission High1 1 1 Moderate0.850.78-0.93<0.001 0.970.88-1.070.572 0.970.88-1.060.499 Low0.810.74-0.89<0.001 0.930.84-1.030.179 0.970.88-1.080.974 Congestive heart failure (n=63894) Within 30 days of admission High1 1 1 Moderate0.710.56-0.900.005 0.830.63-1.100.201 1.421.06-1.890.018 Low0.710.56-0.910.006 0.750.56-1.020.062 1.180.87-1.610.284 Within 1year of admission High1 1 1 Moderate0.810.71-0.91<0.001 0.900.78-1.040.157 1.080.94-1.240.277 Low0.800.70-0.900.001 0.960.83-1.110.578 1.010.87-1.170.889 Pneumonia (n=295443) Within 30 days of admission High1 1 1 Moderate0.800.73-0.89<0.001 0.910.81-1.020.090 0.850.77-0.950.003 Low0.850.76-0.950.003 1.000.89-1.130.999 0.970.86-1.090.553 Within 1year of admission High1 1 1 Moderate0.790.4-0.84<0.001 0.820.77-0.88<0.001 0.860.81-0.92<0.001 Low0.890.83-0.960.002 0.860.80-0.93<0.001 0.890.82-0.950.001 DM (n=232134) Within 30 days of admission High1 1 1 Moderate0.820.68-0.990.034 0.980.78-1.240.857 0.810.62-1.040.098 Low0.770.64-0.940.008 1.080.85-1.350.570 0.930.73-1.200.592 Within 1year of admission High1 1 1 Moderate0.880.81-0.960.002 0.920.83-1.020.106 1.010.91-1.130.807 Low0.830.76-0.90<0.001 0.970.87-1.070.517 1.030.93-1.160.560 Liver cirrhosis (n=59923) Within 30 days of admission High1 1 1 Moderate0.790.65-0.950.015 0.950.74-1.220.708 1.100.88-1.390.404 Low0.760.62-0.830.007 0.940.73-1.210.607 1.130.89-1.440.317 Within 1year of admission High1 1 1 Moderate0.710.64-0.79<0.001 0.880.77-1.010.062 0.990.87-1.120.848 Low0.700.63-0.78<0.001 0.850.74-0.970.018 0.960.84-1.100.575 * Adjusted for patient age, gender, Charlson Comorbidity Index Score, socioeconomic status, urbanization level, geographic region, hospital characteristics and caseload.

13 Results The high expenditure hospital is associated with higher caseload and medical center. The expenditure index is not associated regularly with survival and readmission rates of those diseases we choose to look. This is against some of the papers published. Romley JA, Annals of Internal Medicine 154:160-167.

14 Results If we inspect the data year by year, the lower expenditure index hospital is associated with lower mortality in 2002, but seldom in 2003 and 2004. And Moderate expenditure index is associated with less readmission rate in 2004. Conclusion: In contrast with previous study, higher spending intensity is not always associated with lower readmission and mortality rates in selected major diseases in Taiwan.

15 Discussion In Taiwan our data may suggest higher spending doesn’t necessarily mean better outcomes. Some suggest that better outcomes is related to effective process measures. But in 2006 two attempts to link process measures with short term mortality failed to explain most of their data. Bradley EH, JAMA 296: 72-78 Werner RM, JAMA 296: 2694-2702

16 Discussion High-spending regions are more likely than other regions to use recommended care but are also more likely to use discretionary and nonrecommended care, the latter of which has adverse outcomes for patients. Landrum MB, Health Aff (Millwood). 2008 Jan-Feb;27(1):159-68

17 Weakness We use DD files(in hospital mortality) which may not include death before arriving hospital. The chosen disease may be to heterogenous in severity.

18 Thank you for your attention!

19 Karen E. Joynt, JAMA. 2013;309(24):2572-2578

20 競爭導致創造需求 ? 醫院密度與 HSI 的關係

21 Physician patient-sharing networks and the cost and intensity of care in US hospitals. Hospital-based physician network structure has a significant relationship with an institution's care patterns for their patients. Hospitals with doctors who have higher numbers of connections have higher costs and more intensive care, and hospitals with primary care-centered networks have lower costs and care intensity Barnett ML, Med Care. 2012 Feb;50(2):152-60.

22 Reference JAMA. 2012 Mar 14;307(10):1037-45. doi: 10.1001/jama.2012.265. Association of hospital spending intensity with mortality and readmission rates in Ontario hospitals. Stukel TA, Fisher ES, Alter DA, Guttmann A, Ko DT, Fung K, Wodchis WP, Baxter NN, Earle CC, Lee DS.

23 Restriction of admission condition to purify the disease To capture incident admissions, we excluded patients with AMI and hip fracture admitted for these conditions during the previous year and patients with CHF having a CHF admission in the previous 3 years. We included patients with a first diagnosis of colon cancer undergoing potentially curative resection within 6 months, excluding those who presented with metastatic cancer or who were diagnosed with any other cancer within the previous 5 years. We excluded patients with AMI having a stay of less than 3 days. Patients were assigned to the cohort corresponding to their earliest admission and underwent follow-up for 1 year after the index admission date. We created an index episode of care beginning at initial admission and ending at the final discharge, incorporating transfers. To ensure stability of the hospital-specific measures, we restricted to 129 hospitals with more than 10 study condition admissions per year, resulting in exclusion of 27% of hospitals but only 3% of patients. Therese A. Stukel, PhD JAMA. 2012 March 14; 307(10): 1037–1045

24 AMI ICD9 code: 410.x, AMI: we excluded patients with AMI admitted for these conditions during the previous year(index admission 往前一年內 AMI 住過院 的不要, 要 fresh case) We excluded patients with AMI having a stay of less than 3 days( 剔除太輕微的 case, 或為 了給付加上去的診斷 )

25 CHF Coding: 428.x Excluding: having a CHF admission in the previous 3 years

26 Stroke Coding: Cerebrovascular disease 430.x–438.x 取 7-21 天之間的住院可能疾病嚴重度較為均 勻. Huang YC, J Stroke Cerebrovasc Dis. 2012 Dec 14. pii: S1052-3057(12)00358-8. The United States had the shortest LOS (6 days) in contrast to Canada with the longest LOS (34-47 days). average LOS was 13.9 ± 14.1 days (range: 1- 129). O'Brien SR, Phys Ther. 2013 Jul 25. [Epub ahead of print] Shorter Length of Stay Is Associated With Worse Functional Outcomes for Medicare Beneficiaries With Stroke.

27 Liver cirrhosis Mild liver disease 571.2, 571.4–571.6 =>filter out Moderate or severe liver disease 456.0– 456.21, 572.2–572.8 => including( 集中在較嚴 重的 case 上 )

28 DM Focus 在有進行過腳的 debridement 的 case 或是有做過眼睛雷射治療的 case 上 Or with CKD/HD diagnosis

29 Pneumonia 有 ventilator support, 或是有 shock 診斷的 case

30 Thinking Turn to do HSI index admission and subsequent emergent department(ED) visits( 因不少費用來自急診 ) 以醫院為單位, Paper 數目與 HSI 的關係


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