Predictor Of Health Care Costs Research Articles

Waist Circumference, Visceral Obesity, and Cardiovascular Risk

Waist Circumference, Visceral Obesity, and Cardiovascular Risk

Psychosocial Predictors of Health Status and Health Care Costs among People with Fibromyalgia

The purpose of the present study was to examine the effects of psychosocial characteristics on health status and the relationship between health status and health care use and costs in a group of people with fibromyalgia (FMS) over a 1-year period. The participants were 600 members of a large health maintenance organization. Demographic characteristics (age, education, employment status, ethnicity and income), need variables (comorbid conditions and baseline health status), and psychosocial variables (self-efficacy, helplessness, coping, and depression) were examined. Better health status at baseline, use of emotion-focused coping, and higher self-efficacy predicted better health status the following year. Higher health care use at baseline, being Caucasian, being older, and having more co-morbid conditions predicted higher health care use at 1 year. Only health care costs at baseline predicted health care costs the following year. Our results indicate that health care use and cost tend to be stable. Intense interventions focused on changing patterns of health care use and methods for coping with the change need to be developed and tested.

The relationship between drug therapy noncompliance and patient characteristics, health-related quality of life, and health care costs.

The objectives of this study were to determine the relationship between drug therapy compliance and risk of hospitalization and economic outcomes, and to identify potential indicators of compliance. We used computerized prescription records from 1,054 patients at high risk for drug-related problems. We calculated a compliance ratio for a 12-month period and correlated it with health care use, demographic variables, drug-related variables, and scores for health-related quality of life. Univariate results suggested that increased age (p=0.05), high number of chronic conditions (p<0.001), and high number of concurrent drugs (p<0.001) were positively correlated with compliance. That is, increased values for these variables were associated with better compliance. Using logistic regression, the odds of being noncompliant was 0.665 as the number of chronic conditions increased. Compliance was not a predictor of concurrent or future hospitalizations or mortality, nor was it a significant predictor of health care costs.

Cost function estimation: the choice of a model to apply to dementia.

Statistical analysis of cost data is often difficult because of highly skewed data resulting from a few patients who incur high costs relative to the majority of patients. When the objective is to predict the cost for an individual patient, the literature suggests that one should choose a regression model based on the quality of its predictions. In exploring the econometric issues, the objective of this study was to estimate a cost function in order to estimate the annual health care cost of dementia. Using different models, health care costs were regressed on the degree of dementia, sex, age, marital status and presence of any co-morbidity other than dementia. Models with a log-transformed dependent variable, where predicted health care costs were re-transformed to the unlogged original scale by multiplying the exponential of the expected response on the log-scale with the average of the exponentiated residuals, were part of the considered models. The root mean square error (RMSE), the mean absolute error (MAE) and the Theil U-statistic criteria were used to assess which model best predicted the health care cost. Large values on each criterion indicate that the model performs poorly. Based on these criteria, a two-part model was chosen. In this model, the probability of incurring any costs was estimated using a logistic regression, while the level of the costs was estimated in the second part of the model. The choice of model had a substantial impact on the predicted health care costs, e.g. for a mildly demented patient, the estimated annual health care costs varied from DKK 71 273 to DKK 90 940 (US$ 1 = DKK 7) depending on which model was chosen. For the two-part model, the estimated health care costs ranged from DKK 44714, for a very mildly demented patient, to DKK 197 840, for a severely demented patient.

Age - an unreliable predictor of healthcare costs

Age - an unreliable predictor of healthcare costs

Predicting post-hospital discharge health care costs.

Patients in this study represented an important group for health services research; their postdischarge costs were high, resulted from readmissions (especially for nonelective reasons), and were incurred by a minority of patients. Moreover, intensive interventions delivered by ambulatory care providers has the potential to reduce overall costs if patients at highest risk for readmission could be identified prospectively (Smith et al., 1988; Weinberger et al., 1988). We developed a model to predict post-hospital discharge costs in these patients using strategies to maximize their predictive capability. Although the model appeared to account for more variance in costs than currently available models in the derivation set, its performance in the validation set, albeit statistically significant, was disappointing. Because we considered a broad array of predictors, expanding the number of patient-oriented variables may not be fruitful. Instead, future research may need to consider more homogenous subgroups of patients in whom specific laboratory tests would have clinical significance; variance in providers' behaviors; and studies in health maintenance organizations, where control over resource utilization may make costs more predictable. Finally, empirically derived models must be tested in an independent sample. Without validating predictive models, the models' ability to predict health care costs may be overestimated.