Appendix Identification of Study Cohorts

Transcription

1 Appendix Identification of Study Cohorts Because the models were run with the 2010 SAS Packs from Centers for Medicare and Medicaid Services (CMS)/Yale, the eligibility criteria described in "2010 Measures Maintenance Technical Report: Acute Myocardial Infarction, Heart Failure, and Pneumonia 30- Day Risk- Standardized Readmission Measures" were used to identify study cohorts. We used the ICD- 9 codes identified by CMS/Yale to identify index cohorts (Appendix Exhibit 1). Heart failure (HF) patients discharged to hospice were not excluded as the method did not call for the exclusion at the time. Similar to the CMS models, our data were restricted to Medicare Fee- For- Service patients aged 65 and older. Observation stays were excluded as non- acute patients (Appendix Exhibit 2). Appendix Exhibit 1. Diagnosis Codes Used to Define the AMI, HF, and PN Readmission Cohorts Acute Myocardial Infarction ICD- 9- CM codes: Any 410.xx excluding 410.x2 Heart Failure ICD- 9- CM codes: 40201, 40211, 40291, 40401, 40403, 40411, 40413, 40491, or 428.xx Pneumonia ICD- 9- CM codes: 4800, 4801, 4802, 4803, 4808, 4809, 481, 4820, 4821, 4822, 48230, 48231, 48232, 48239, 48240, 48241, 48249, 48281, 48282, 48283, 48284, 48289, 4829, 4830, 4831, 4838, 485, 486, 4870, or Appendix Exhibit 2. Inclusion & exclusion criteria Medicare fee for service discharges for Missouri patients age 65+: 6/1/2009 5/31/2012 Total number of discharges 666,674 Exclusion criteria missing unique patient identifier 16,383 (2.5%) died in hospital 25,333 (3.8%) transfer to acute care hospital 16,217 (2.4%) same- day readmission 51,998 (7.8%) discharged against medical advice 1,604 (0.2%) non- acute patients 49,207 (7.4%) invalid address (missing census tract) 16,878 (2.5%) Final sample 545,999 (81.9%) Inclusion criteria Acute myocardial infarction (AMI) primary diagnosis 12,070 (1.8%) Heart failure (HF) primary diagnosis 29,874 (4.5%) Pneumonia (PN) primary diagnosis 29,849 (4.5%) Exclusion categories are not mutually exclusive Note: The CMS methodology also excludes patients without complete Medicare fee for service enrollment records 12 months prior to an index admission and 30 days following an index discharge. The replicated models assume completeness of the patient s Medicare enrollment prior to and following the study period. 1

2 Description of Data Linkage and Census Tract Data Hospital discharge records were linked with Truven Health Analytics- Nielsen census tract data by geocoding the patient's current address given during the admission intake process to the census tract- level with Nielsen- Claritas PrecisionCode 3.8 which is based on the September 2012 street address files produced by Pitney Bowes for the TomTom (formerly TANA) address file databases. 2.5% of observations were excluded because of missing or incomplete patient residential address. Missing or incomplete addresses occurred either through patient refusal to disclose or failure to report on the part of the hospital. The majority of records with missing address are also missing other key information such as race (86% in fiscal year 2012). For records without an address but with race present during FY2012, 77% were white, 22% were black or African American, and 1% were another race. Truven Health Analytics and Nielsen Pop- Facts census tract data are estimated with proprietary projection methods that employ numerous data sources. The data used in this analysis are based on the version demographic update. This version of Pop- Facts data provide intercensal estimates based on 2010 Census and one, three, and five- year American Community Survey data. Version data are converted to geographic areas defined by the 2000 census, as geocoding based on the 2010 census was not widely used or available at the time of its release. Pop- Facts data are estimated using both top- down and bottom- up methodologies. The bottom- up method incorporates small area data sources that are updated more frequently than are Census data sources such as U.S. Postal Service records and data on new housing starts. The top- down method imposes controls on small area estimates based on larger area data reported by the Census Bureau. While Nielsen does not provide estimates of reliability, limitations undoubtedly exist in estimate- based projections, particularly for small areas. However, we assume the margin of error to be smallest for intercensal small- area projections closest to a decennial census. Missing Data from Out- of- State Hospitalizations This analysis does not include data from out- of- state hospitalizations of Missouri residents. However, based on information from the Missouri Hospital Association Hospital Industry Data Institute (MHA/HIDI) resident data exchange program with 16 other states, during the study period, only 3.9% of hospital admissions for Missouri Medicare Fee- For- Service patients aged 65 and older occurred in another state. The data exchange program includes all geographically- contiguous neighboring states except Oklahoma (Appendix Exhibit 3). Note that because the MHA/HIDI discharge collection program serves all hospitals in the state, the data does capture readmissions occurring at Missouri hospitals other than the original index admitting hospital. 2

3 Appendix Exhibit 3. Discharges for Missouri Patients Ages 65+ with Medicare FFS by State of Admitting Hospital*: June 1, May 31, 2012 State Discharges Percent MO 666, % KS 13, % AR 4, % IL 3, % IA 1, % TN 1, % NE % MN % CO % MI % GA % KY % VA % Total 693, % *For states participating in the MHA/HIDI resident data exchange program with >50 discharges. Participating states with less than 50 discharges include South Dakota, Wyoming, North Dakota, and Montana. 3

4 Methods Hierarchical generalized logistic regression models (HGLMs) were used to fit the baseline and socioeconomic- factor- enriched models. The models were run in SAS using modified 2010 SAS Packs for AMI, HF and PN as supplied by CMS/Yale. The 2010 SAS Packs were chosen over the 2011 version because they do not include variables and statements designed to incorporate Veteran s Administration data that are unavailable for the purpose of this study. We used diagnosis and procedure codes during the 12 months prior to the index admission for clinical risk adjustment. Hierarchical generalized logistic regression is used to control for clustered data by modeling patient- and hospital- level effects that may contribute to the patient s probability of being readmitted during the 30 days following an index admission. The models estimate the patient s log- odds of being readmitted as explained by the patient- level covariates of risk (fixed effects) alongside a hospital- specific intercept term (random effect) that estimates the impact each hospital has on the log odds a patient will be readmitted. For each hospital the HGLMs derive estimates of the predicted readmission rate, the expected readmission rate, the risk standardized readmission ratio (SRR), and the risk standardized readmission rate (RSRR). The predicted rate estimates the hospital readmission rate controlling for case mix, holding the random effect constant. The expected rate estimates the hospital readmission rate holding case mix constant and can be interpreted as the average expected performance of all hospitals in the study with the individual hospital s unique case mix. The SRR is the ratio of predicted to expected readmission rates. A ratio below one indicates lower than expected readmissions while a ratio higher than one indicates above- expected readmissions. The hospital RSRR is the product of the SRR and the observed readmission rate for the study sample. Description of the socioeconomic- factor- enriched (SES- enriched) model specification process Generalized linear mixed model methods were used to extend CMS base readmission models to include census- tract level socioeconomic effects. Preliminary analyses explored individual and bi- variable effects of race, discharge location and socioeconomic variables using contingency tables and binary logistic regression. Continuously measured predictors were tested in both continuous and categorical (sample quintiles) effects to assess assumed linearity; these were subsequently modeled as three- level categorical effects (highest- and lowest- quintiles vs. quintiles two through four) because this provided the best combination of interpretability, simplicity and fit to sample data. Because preliminary analyses suggested effect variation across levels of patient race and discharge status, socioeconomic predictors were specified separately by level of race (white vs. nonwhite) and discharge location (SNF vs. other) using cell- means coding. SES- enriched models were built using a backward stepwise process to successively eliminate non- significant effects (p < 0.05). In all models, patient census tract and hospital (per CMS specifications) were tested as random effects to adjust for clustering among discharges. We observed little variation in the fit or discriminant ability of the SES- enriched models, nor the size, sign or significance of estimated coefficients when comparing hospital- level vs. census tract- level random effects and present results for the SES- enriched models using census tract random effects. We observed significant differences (p<.001) in the variance of hospital- level risk- adjusted performance estimates between CMS and SES- enriched models based on variance comparisons for paired samples. 4

5 Appendix Exhibit 4. Candidate Variables for the SES- Enriched Models Percent of Families Below Poverty Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Percent of Families w/ Children Below Poverty Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Median Income Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Housing Unit Vacancy Rate Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Unemployment Rate Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Percent of the Population Age 25+ w/ < High School Education Nonwhite & Quintile 1 White & Quintile 1 Nonwhite & Quintile 5 White & Quintile 5 Patient Discharged Home & Percent of Families Below Poverty: Quintile 5 & Percent of Families w/ Children Below Poverty: Quintile 5 & Median Income: Quintile 1 & Housing Unit Vacancy Rate: Quintile 5 & Unemployment Rate: Quintile 5 & Percent of the Population Age 25+ w/ < High School Education: Quintile 5 & Nonwhite 5

6 Appendix Exhibit 5. Description of study population Discharge Characteristics AMI HF PN Number of Hospitals (w/ denominator 30) Mean Risk Standardized Readmission Rate Enriched Model 16.3% 19.5% 15.1% CMS Model 16.4% 19.3% 15.1% Between Hospital Variation (RSRR Range) Enriched Model (%) CMS Model (%) Patient Cohorts Unique Patients 11,392 22,433 25,729 Index Admissions 12,070 29,874 29, Day Readmissions 1,960 5,781 4,490 Crude Readmission Rate 16.2% 19.3% 15.0% Demographic Median Age Male 51.2% 43.1% 43.6% Female 48.8% 56.9% 56.4% White 92.3% 89.4% 93.8% Non- White 7.7% 10.6% 6.2% Length of Stay Mean Median Appendix Exhibit 6. Description of census tracts Census Tract Characteristics (n = 1,320) Percentile 10th 25th Median 75th 90th Families Below Poverty 2.4% 5.1% 10.1% 16.9% 27.9% Families w/ Children Below Poverty 3.8% 8.4% 16.8% 27.0% 41.4% Median Income $25,533 $31,623 $39,798 $52,462 $70,111 Housing Unit Vacancy Rate 3.5% 5.8% 9.4% 15.6% 23.9% Unemployment Rate 3.2% 4.6% 6.9% 10.5% 18.0% Population Age 25+ w/ < High School Edu. 4.5% 8.8% 14.5% 20.7% 27.5% Families with Children w/ Single Parent 18.5% 24.8% 33.8% 48.6% 69.8% 6

7 Appendix Exhibit 7. Univariate Analysis: Readmission Rates by Patient Demographic Characteristics Demographic Group AMI HF PN Gender Male** 14.7%** 19.7% 15.6%* Female 17.8% 19.1% 14.6% Race White 15.8%** 19.0%** 14.7%** Non- White 21.2% 22.2% 20.2% Age %** 20.7%** 15.5%** % 19.4% 15.5% % 18.3% 14.1% *Significant at.05 level **Significant at.01 level Appendix Exhibit 8. Univariate Analysis: Readmission Rates by SES of Patient's Census Tract Census Tract SES Variable Families Below Poverty Families w/ Children Below Poverty Median Income Housing Unit Vacancy Rate Unemployment Rate Population Age 25+ w/ < High School Education Quintile First Second Third Fourth Fifth AMI* 17.5% 15.6% 15.8% 15.3% 18.4% HF** 19.6% 19.6% 17.9% 19.7% 20.7% PN 15.4% 15.1% 14.4% 15.4% 15.0% AMI 17.3% 16.1% 15.7% 14.8% 18.4% HF 19.5% 19.7% 18.2% 19.4% 20.4% PN 15.6% 15.2% 14.2% 15.2% 15.1% AMI 17.3% 15.3% 16.6% 15.8% 16.7% HF 19.7% 19.2% 19.4% 18.5% 20.2% PN 15.0% 14.7% 14.4% 15.2% 16.0% AMI 17.0% 16.3% 16.3% 15.0% 16.5% HF 19.5% 19.7% 19.0% 19.2% 19.6% PN* 16.4% 15.0% 14.7% 14.5% 14.6% AMI** 15.6% 15.7% 16.1% 15.6% 19.7% HF* 18.6% 18.9% 19.4% 19.2% 21.2% PN 14.7% 14.5% 15.6% 14.9% 16.0% AMI 16.4% 16.5% 16.1% 15.6% 16.8% HF 19.0% 19.9% 18.9% 19.2% 19.8% PN 15.1% 15.9% 14.5% 14.4% 15.6% Families with AMI** 15.1% 15.7% 15.6% 17.2% 19.8% Children w/ HF** 18.5% 19.1% 19.5% 18.9% 21.7% Single Parent PN 14.8% 14.7% 14.7% 15.3% 17.0% *Significant at.05 level **Significant at.01 level 7

8 Appendix Exhibit 9. SES- Enriched Model Results Heart Failure Cohort Effect Estimate Standard Error Odds Ratio Intercept SNF & SES (Enriched Model Covariates) Discharged to SNF in previous 30 days % CI P- Value (0.0791, ) (1.118, ) Nonwhite race & residing in census tract in fifth quintile of % families below poverty (1.1222, ) White race & residing in census tract in fifth quintile of % families below poverty (0.8875, ) Nonwhite race & residing in census tract in fifth quintile of % housing unit vacancies (1.0504, ) White race & residing in census tract in fifth quintile of housing unit vacancies (0.8582, ) Nonwhite race & residing in census tract in fifth quintile of % adults age 25 or older with less than a high school education (0.5733, ) White race & residing in census tract in fifth quintile of % adults age 25 or older with less than a high school education (0.9611, ) Demographic (Baseline Model Covariates) Age in Years over Gender Male (0.993, ) (0.9365, )

9 Clinical Health History (Baseline Model Covariates) History OF CABG Diabetes or DM complications Disorders of fluid, electrolyte, acid- base (0.8781, ) (1.0395, ) (1.2675, 1.471) Iron deficiency or other anemias and blood disease (0.9745, ) Cardio- respiratory failure or shock Congestive heart failure Vascular or circulatory disease COPD Pneumonia Renal failure Other urinary tract disorders Decubitus ulcer or chronic skin ulcer Other gastrointestinal disorders Acute coronary syndrome Valvular or rheumatic heart disease Specified arrhythmias Asthma Peptic ulcer, hemorrhage, other specified gastrointestinal disorders (1.1959, 1.366) (0.4517, ) (1.1305, ) (1.0514, ) (1.0105, ) (1.914, ) (0.9177, ) (0.9771, ) (0.9382, 1.076) (0.8641, ) (1.0001, ) (1.5697, 1.822) (0.7751, ) (1.0621, )

10 Cancer (0.9457, ) Drug/alcohol abuse/dependence/psychosis (0.8792, ) Major psychiatric disorders End stage renal disease or dialysis Severe hematological disorders Nephritis Liver or biliary disease Metastatic cancer or acute leukemia (0.9221, ) (1.0668, ) (0.8826, ) (0.7553, ) (0.9328, ) (0.9969, ) Stroke (1.047, 1.343) Dementia or other specified brain disorders Coronary atherosclerosis or angina Other or unspecified heart disease Other psychiatric disorders (0.9402, ) (1.0392, ) (0.856, ) (1.1102, ) Hemiplegia, paraplegia, paralysis, functional disability (0.9026, ) Fibrosis of lung or other chronic lung disorders Protein- calorie malnutrition Depression (0.9177, ) (1.0117, ) (0.8882, )

11 Acute Myocardial Infarction Cohort Effect Estimate Standard Error Odds Ratio Intercept SNF & SES (Enriched Model Covariates) Discharged to SNF in previous days 95% CI P- Value (0.0291, ) (0.718, ) Nonwhite race & residing in census tract in first quintile of % families below poverty (1.2403, ) White race & residing in census tract in first quintile of % families below poverty (0.9699, ) Nonwhite race & residing in census tract in fifth quintile of families with children below poverty (1.0932, ) White race & residing in census tract in fifth quintile of % families with children below poverty (1.0777, ) Discharged to home & residing in census tract in fifth quintile of % families with children below poverty (0.4996, ) Demographic (Baseline Model Covariates) Age in Years over Gender Male Clinical Health History (Baseline Model Covariates) History of CABG History of PCI Angina pectoris, old MI Congestive heart failure (1.0027, ) (0.7828, 0.961) (1.5544, ) (2.0298, ) (1.094, ) (1.6491, 2.125)

12 Coronary atherosclerosis Acute coronary syndrome Specified arrhythmias Valvular or rheumatic heart disease Cerebrovascular disease Stroke Vascular or circulatory disease (0.9927, ) (0.425, ) (1.0846, ) (1.0174, 1.261) (0.8241, ) (0.8143, 1.201) (1.1244, ) Hemiplegia, paraplegia, paralysis, functional disability (0.9915, ) Diabetes mellitus (DM) or DM complications Renal failure End stage renal disease or dialysis Other urinary tract disorders Chronic obstructive pulmonary disease Pneumonia Asthma Disorders of fluid, electrolyte, acid- base History of infection Metastatic cancer or acute leukemia Cancer (0.9335, 1.149) (1.1239, ) (0.7641, ) (0.978, ) (1.0272, ) (1.0318, 1.288) (0.6524, ) (1.3328, ) (1.1153, ) (1.0488, ) (0.9427, ) Iron deficiency or other anemias and blood disease (1.2651, ) Decubitus ulcer or chronic skin ulcer (0.8952, )

13 Dementia or other specified brain disorders Protein- calorie malnutrition Anterior myocardial infarction Other location myocardial infarction (0.9846, ) (1.0624, 1.418) (0.8085, ) (0.7262, ) Pneumonia Cohort Effect Estimate Standard Error Odds Ratio 95% CI P- Value Intercept (0.0718, ) SNF & SES (Enriched Model Covariates) Discharged to SNF in previous 30 days (1.0669, ) Nonwhite race & residing in census tract in first quintile of % housing unit vacancies (1.0085, ) White race & residing in census tract in first quintile of % housing unit vacancies (0.9832, ) Nonwhite race & residing in census tract in fifth quintile of % housing unit vacancies (1.1555, ) White race & residing in census tract in fifth quintile of % housing unit vacancies (0.8054, ) Demographic (Baseline Model Covariates) Age in Years over (0.9905, ) Gender Male (1.0184, ) Clinical Health History (Baseline Model Covariates) History OF CABG (0.7913, ) History of infection (1.1436, 1.334) Septicemia/shock (0.9744, ) Metastatic cancer or acute leukemia (1.1144, ) Lung or other severe cancers (1.0542, )

14 Other major cancers (0.9916, 1.222) Diabetes or DM complications (0.9381, ) Protein- calorie malnutrition (1.1772, ) Disorders of fluid, electrolyte, acid- base Other gastrointestinal disorders Severe hematological disorders Iron deficiency or other anemias and blood disease Dementia or other specified brain disorders Drug/alcohol abuse/dependence/ psychosis (1.2587, ) (1.0665, ) (1.0223, ) (1.2566, 1.469) (0.9735, ) (0.8934, ) Major psychiatric disorders (0.9407, ) Other psychiatric disorders (1.0968, 1.29) Hemiplegia, paraplegia, paralysis, functional disability (0.8646, ) Cardio- respiratory failure or shock (1.311, ) Congestive heart failure (1.38, ) Acute coronary syndrome (1.019, ) Coronary atherosclerosis or angina (0.9732, ) Valvular or rheumatic heart disease (0.9352, ) Specified arrhythmias (1.1292, 1.309) Stroke (1.2191, ) Vascular or circulatory disease (1.1265, 1.304) COPD (1.0797, ) Fibrosis of lung or other chronic lung disorders (1.0257, ) Asthma (0.8676, ) Pneumonia (0.3093, ) 14

15 Pleural effusion/pneumothorax (1.3276, ) Other lung disorders (0.9845, ) End stage renal disease or dialysis (0.7635, ) Renal failure (1.1027, 1.281) Urinary tract infection (0.9617, ) Other urinary tract disorders (1.025, ) Decubitus ulcer or chronic skin ulcer (0.9523, ) Vertebral fractures (0.926, 1.227) Other injuries (0.9847, )