The Box-Cox power transformation on nursing sensitive indicators: does it matter if structural effects are omitted during the estimation of the transformation parameter?

Eliciting the Monetary Value of a Quality-Adjusted Life Year in a Greek Outpatient Department in Times of Economic Austerity

BACKGROUND AND OBJECTIVE

Contingent valuation is widely used to determine individuals' willingness to pay (WTP) for a health gain. Our study aimed to elicit an empirical estimate of the monetary value of a quality-adjusted life year (QALY) in a Greek outpatient setting in times of economic austerity and assess the impact of patients' characteristics on their valuations.

METHODS

We used a questionnaire as a survey tool to determine the maximum WTP for a health gain of a hypothetical therapy and to evaluate patients' health-related quality of life (EuroQoL-5D-3L) and demographic and socioeconomic characteristics. EuroQoL tariffs were used to estimate health utilities. Mean WTP values were computed and ordinary least squares regressions performed on transformed Box-Cox and logarithmic dependent WTP per QALY variables to remedy observed skewness problems.

RESULTS

Analyses were performed for 167 patients with utility values less than unity. Mean WTP per QALY reported was similar for both payment vehicles examined: payments made out-of-pocket (€2629) and payments made through new tax imposition (€2407). Regression results showed that higher net monthly family income was associated with higher WTP per QALY for both payment vehicles. Moreover, the presence of a chronic condition and higher level of education were associated with higher out-of-pocket WTP per QALY and WTP per QALY through taxes, respectively.

CONCLUSION

The very low WTP per QALY estimates could be explained by the recent severe economic depression and austerity in Greece. In fact, family income was found to be a significant predictor of WTP per QALY. Since these estimates deviate significantly from the cost-effectiveness thresholds still employed in economic evaluations in this country, research should be undertaken promptly to further examine this important issue using a nationwide representative sample of the general population along with WTP and other methodologies.

Use of Electronic Health-Related Datasets in Nursing and Health-Related Research

Datasets of gigabyte size are common in medical sciences. There is increasing consensus that significant untapped knowledge lies hidden in these large datasets. This review article aims to discuss Electronic Health-Related Datasets (EHRDs) in terms of types, features, advantages, limitations, and possible use in nursing and health-related research. Major scientific databases, MEDLINE, ScienceDirect, and Scopus, were searched for studies or review articles regarding using EHRDs in research. A total number of 442 articles were located. After application of study inclusion criteria, 113 articles were included in the final review. EHRDs were categorized into Electronic Administrative Health-Related Datasets and Electronic Clinical Health-Related Datasets. Subcategories of each major category were identified. EHRDs are invaluable assets for nursing the health-related research. Advanced research skills such as using analytical softwares, advanced statistical procedures, dealing with missing data and missing variables will maximize the efficient utilization of EHRDs in research.

Input data quality control for NDNQI national comparative statistics and quarterly reports: a contrast of three robust scale estimators for multiple outlier detection

Background

To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks), approximately one in every three major healthcare institutions (over 1,800 hospitals) across the United States, have joined the National Database for Nursing Quality Indicators® (NDNQI®). With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports.

Methods

With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR) and Median Absolute Deviation from the Median (MAD), to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD) approach, for initial univariate outlier detection.

Results

While the theoretically based FAST-MCD used in one dimension can be sensitive and is better suited for identifying groups of outliers because of its high breakdown point, the ad-hoc IQR and MAD approaches are fast, easy to implement, and could be more robust and efficient, depending on the distributional property of the underlying measure of interest.

Conclusion

With highly skewed distributions for most NDNQI indicators within a short data screen window, the FAST-MCD approach, when used in one dimensional raw data setting, could overestimate the false alarm rates for potential outliers than the IQR and MAD with the same pre-set of critical value, thus, overburden data quality control at both the data entry and administrative ends in our setting.