title: Functional status decline as a measure of adverse events in home health care: an observational study.
abstract: BACKGROUND
  Research that examines the quality of home health care is complex because no gold standard exists for measuring adverse outcomes, and because the patient and clinician populations are highly heterogeneous. The objectives in this study are to develop models to predict functional decline for three indices of functional status as measures of adverse events in home health care and determine which index is most appropriate for risk-adjusting for future quality research.
  
  
  METHODS
  Data come from the Outcomes and Assessment Information Set (OASIS) from a large urban home health care agency and other agency data. Prognostic data yields 49,437 episodes, while follow-up data yields 47,684 episodes. We tested three indices defined as substantial decline in three or more (gt3_ADLs), two or more (gt2_ADLs), and one or more (gt1_ADLs) ADLs. Multivariate logistic regression determines the performance of the models for each index as measured by the c-statistic and Hosmer-Lemeshow chi square (chi2).
  
  
  RESULTS
  Frequencies for gt3_ADLs, gt2_ADLs, and gt1_ADLs are 212 (0.43%), 783 (1.58%), and 4,271 (8.64%) respectively. Follow-up results are comparable with frequencies of 218 (0.46%), 763 (1.60%), and 3,949 (8.28%) for each index. Gt3_ADLs does not produce valid models. The model for gt2_ADLs consistently yields a higher c-statistic compared to gt1_ADLs (0.754 vs. 0.679, respectively). Both indices' models yield non-significant Hosmer-Lemeshow chi square indicating reasonable model fit. Findings for gt2_ADLs and gt1_ADLs are consistent over time as indicated by follow-up data results.
  
  
  CONCLUSIONS
  Gt2_ADLs yields the best models as indicated by a high c-statistic and a non-significant Hosmer-Lemeshow chi2, both of which exhibit exceptional consistency. We conclude that gt2_ADLs may be preferable in defining ADL adverse events in the context of home health care.
authors.affiliation: Case Western Reserve University, Department of Epidemiology and Biostatistics, 10900 Euclid Avenue, Cleveland, OH 44106, USA. tanyascharpf@hotmail.com
authors.forename: Tanya Pollack
authors.initials: TP
authors.lastname: Scharpf
authors.forename: Natalie
authors.initials: N
authors.lastname: Colabianchi
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Madigan
authors.forename: Duncan
authors.initials: D
authors.lastname: Neuhauser
authors.forename: Timothy
authors.initials: T
authors.lastname: Peng
authors.forename: Penny H
authors.initials: PH
authors.lastname: Feldman
authors.forename: John F P
authors.initials: JF
authors.lastname: Bridges
mesh_terms: D000203:Activities of Daily Living / Q000145:classification*; D000368:Aged; D014487:Centers for Medicare and Medicaid Services, U.S.; D003710:Demography; D017050:Episode of Care; D005260:Female; D006699:Home Care Services / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D011379:Prognosis; D015397:Program Evaluation; D011787:Quality of Health Care / Q000706:statistics & numerical data; D012307:Risk Factors; D014481:United States; D019037:Urban Health Services / Q000592:standards* / Q000706:statistics & numerical data