An evaluation of the personal response system program for the elderly in Harris County, Texas

The Personal Response System Program at Huffington Center on Aging, Baylor College of Medicine, provides emergency call systems for elderly people living independently in Houston, Texas. The goal of the project was to complete a formative evaluation of the Personal Response System Program. The specific aims of the evaluation were three-fold. One aim was to evaluate participant health status and level of disability. The second aim was to develop a health care cost estimation strategy. Finally, a preliminary cost-effectiveness analysis was completed to evaluate the program's impact on health care costs and health status among the elderly target population. ^ The evaluation was a longitudinal, randomized experimental design. After the screening of 120 volunteers for eligibility, clients were asked to complete a written questionnaire and a monthly health service contact diary. Volunteers were contacted by telephone interviewers to collect health status information from 100 eligible clients (83%) on three occasions during the six months of follow-up. ^ Initially, volunteers were randomized to two experimental groups. The two groups were found to be comparable at the beginning of the study. No significant differences were detected related to health status, level of disability, or history of physician visits at baseline. However, the group with the Personal Response System (PRS) device had more adverse health events, higher IADL scores, more frequent use of walkers, lower average health status scores, and fewer community volunteers hours than the usual care comparison group. ^ The health care costs were estimated based on an algorithm adapted from the American Medical Association guidelines. Average total health care costs for the group with the PRS device ($912) were greater than the usual care group ($464). However, median health care values for the PRS group ($263) were similar to the usual care comparison group ($234). The preliminary findings indicated that the use of the PRS device was not associated with health care cost savings. ^ In the preliminary cost-effectiveness analysis, use of the personal response system was found to be associated with increased mental health status among elderly clients. The cost-effectiveness evaluation indicated that the associated cost for six months was $710 per unit increase in mental component score when the $150 PRS subscription was included. However, clients with the PRS device were found to have a greater decline in physical health status during the six-month follow-up. The beneficial effect on mental health status was found to be in contrast to negative findings associated with changes in physical health status. The implications for future research relate to the need to identify risk factors among geriatric populations to better target groups that would most likely benefit from PRS Program enrollment. ^

Cost-benefit analysis: The ``gold standard'' re-examined

The dissertation reviews the recommendations of the Panel on Cost Effectiveness in Health and Medicine (Panel) convened by the US Public Health Service in 1993 in four areas: aggregation of costs and benefits, methods of estimating resources used, definition of population impacted and perspective used in cost benefit analysis. Financial data from a clinical trial was used to test whether different approaches in each of the above four areas would change the net benefit resulting from a cost benefit analysis. Differences in aggregation of cost and benefit resulted in the same net benefit, but not the same cost/benefit ratios. Differences in resource use estimation methods, population subgroups definitions and perspectives all produced different net benefits. Difference in perspective resulted in different and often opposing decisions as to whether the proposed intervention from the clinical trial should be implemented. ^

Quantifying uncertainty in cost effectiveness analyses

This study compared four alternative approaches (Taylor, Fieller, percentile bootstrap, and bias-corrected bootstrap methods) to estimating confidence intervals (CIs) around cost-effectiveness (CE) ratio. The study consisted of two components: (1) Monte Carlo simulation was conducted to identify characteristics of hypothetical cost-effectiveness data sets which might lead one CI estimation technique to outperform another. These results were matched to the characteristics of an (2) extant data set derived from the National AIDS Demonstration Research (NADR) project. The methods were used to calculate (CIs) for data set. These results were then compared. The main performance criterion in the simulation study was the percentage of times the estimated (CIs) contained the “true” CE. A secondary criterion was the average width of the confidence intervals. For the bootstrap methods, bias was estimated. ^ Simulation results for Taylor and Fieller methods indicated that the CIs estimated using the Taylor series method contained the true CE more often than did those obtained using the Fieller method, but the opposite was true when the correlation was positive and the CV of effectiveness was high for each value of CV of costs. Similarly, the CIs obtained by applying the Taylor series method to the NADR data set were wider than those obtained using the Fieller method for positive correlation values and for values for which the CV of effectiveness were not equal to 30% for each value of the CV of costs. ^ The general trend for the bootstrap methods was that the percentage of times the true CE ratio was contained in CIs was higher for the percentile method for higher values of the CV of effectiveness, given the correlation between average costs and effects and the CV of effectiveness. The results for the data set indicated that the bias corrected CIs were wider than the percentile method CIs. This result was in accordance with the prediction derived from the simulation experiment. ^ Generally, the bootstrap methods are more favorable for parameter specifications investigated in this study. However, the Taylor method is preferred for low CV of effect, and the percentile method is more favorable for higher CV of effect. ^

The determinants of improvements in health outcomes and of cost reduction in hospital inpatient care

This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^