An Aging Society: Opportunity or Challenge?

macroeconomics, Aging Society

Managed Care and the Growth of Medical Expenditures

We use data across states to examine the relation between HMO enrollment and medical spending. We find that increased managed care enrollment significantly reduces hospital cost growth. While some of this effect is offset by increased spending on physicians, we generally find a significant reduction in total spending as well. In analyzing the sources of hospital cost reductions, we find preliminary evidence that managed care has reduced the diffusion of medical technologies. States with high managed care enrollment were technology leaders in the early 1980s; by the early 1990s those states were only average in their acquisition of new technologies. This finding suggests managed care may have a significant effect on the long-run growth of medical spending.

Demographics and Medical Care Spending: Standard and Non-Standard Effects

In this paper, we examine the effects of likely demographic changes on medical spending for the elderly. Standard forecasts highlight the potential for greater life expectancy to increase costs: medical costs generally increase with age, and greater life expectancy means that more of the elderly will be in the older age groups. Two factors work in the other direction, however. First, increases in life expectancy mean that a smaller share of the elderly will be in the last year of life, when medical costs generally are very high. Furthermore, more of the elderly will be dying at older ages, and end-of-life costs typically decline with age at death. Second, disability rates among the surviving population have been declining in recent years by 0.5 to 1.5 percent annually. Reductions in disability, if sustained, will also reduce medical spending. Thus, changes in disability and mortality should, on net, reduce average medical spending on the elderly. However, these effects are not as large as the projected increase in medical spending stemming from increases in overall medical costs. Technological change in medicine at anywhere near its historic rate would still result in a substantial public sector burden for medical costs.

Extensions to the Visual Predictive Check to facilitate model performance evaluation

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example

Recurrence of hepatitis C after liver transplantation is associated with increased systemic IL-10 levels.

BACKGROUND: Recurrence of hepatitis C after liver transplantation is an almost universal occurrence. T-cell derived cytokines have an important role in the development of liver damage associated with chronic hepatitis C, their post-transplant levels, however, have not been correlated with histologic recurrence of the disease. AIMS: We sought to analyze levels of TNF-alpha, soluble IL-2 receptor, IL-4 and IL-10 at 1 month, 6 months and 1 year after transplantation in 27 patients undergoing transplantation for hepatitis C related end-stage liver disease. METHODS: HCV RNA levels were monitored by a branched-chain DNA signal amplification assay. Diagnosis of recurrent hepatitis was based on 1-year protocol biopsies and on biopsies performed for liver enzyme elevations. RESULTS: Recurrent hepatitis C was detected in 52% (n=14) of the 27 patients. HCV RNA levels rose over time in all patients regardless of histologic recurrence. TNF-alpha, and IL-4 levels, although elevated, did not show specific patterns over time or in correlation with recurrence. Similarly, the early elevation followed by a gradual decrease over the first year in the amount of soluble IL-2 receptor was not related to histologic recurrence. We observed a significant increase in circulating IL-10 levels over the first year in patients with biopsy-proven recurrence, while patients with no signs of histologic recurrence displayed increased, but steady levels. CONCLUSIONS: These results suggest that while these cytokines are associated with post-transplant recurrence of hepatitis C, their production may be altered by additional factors

Policy Options for Long-Term Care

This paper examines the effect of government nursing home policies on institutionalization rates and support for the elderly in the community. We combine data from the National Long Term Care Survey with information on state policies to estimate these effects. We examine two state policies for nursing home care: the ability of some high income elderly to receive Medicaid support, and the price differential between Medicaid and the private market. Both policies strongly affect aggregate nursing home utilization. as well as the composition of nursing home residents. In states with more liberal Medicaid rules. the high income elderly are more likely to use a nursing home. while in states with larger underpayments. the poor suffer reduced access. The marginal source of community care for the institutionalized elderly appears to be support from children or other helpers, rather than living alone. Almost all of the elderly in nursing homes would have lived with children or others had they been in the community. In addition, as the ease of acquiring Medicaid increases or Medicaid payments become more generous, fewer elderly receive substantial day-to-day help from their children.

Th1/Th2 cytokines and ICAM-1 levels post-liver transplant do not predict early rejection.

Th1 derived cytokines IFN-gamma and IL-2, Th2 cytokine IL-4, and ICAM-1 have been implicated in liver allograft rejection. In order to determine whether monitoring of cytokine profiles during the first days post-liver transplant can predict early rejection we measured IFN-gg, IL-2, sIL-2 receptor, IL-4 and ICAM-1 in 22 patients, in plasma samples obtained within 4 h after liver perfusion (baseline) and between postoperative days (POD) 3-6. ICAM-1 and sIL-2R levels at POD 3-6 were significantly higher than at baseline but did not differ in presence or absence of rejection. Mean percentage increase of ICAM-1 levels was significantly lower in patients with Muromonab-C3 Orthoclone OKT3 (J.C. Health Care) (OKT3) whereas percentage increase of sIL-2R levels was higher in OKT3-treated patients. IFN-gamma levels at POD 3-6 increased from baseline while IL-4 levels were unchanged. Levels of IFN-gamma, IL-4 and their ratios did not correlate with rejection or immunosuppressive therapy. Thus, Th1/Th2 cytokine monitoring during the first week post-transplant does not predict early rejection and immunosuppressive therapy is the predominant factor affecting ICAM and sIL-2R levels after liver transplantation

Model-Based Interpretation of Time-Varying Medical Data

Temporal concepts are critical is medical therapy-planning. If given early enough, specific therapeutic choices may abort or suppress evolving undesired changes in a patient’s clinical status. Effective medical decision making demands recognition and interpretation of complex temporal changes that permeate the medical record. This paper presents a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinically relevant events, and describes a program, called TOPAZ, that uses this methodology to generate a narrative summary of such events. A unique feature of TOPAZ is the use of numeric and symbolic modeling techniques to perform temporal reasoning tasks that would be difficult to encode and perform using only one modeling methodology