Patient classification system: An integrated method for measuring nursing intensity and optimizing resource allocation

A patient classification system was developed integrating a patient acuity instrument with a computerized nursing distribution method based on a linear programming model. The system was designed for real-time measurement of patient acuity (workload) and allocation of nursing personnel to optimize the utilization of resources.^ The acuity instrument was a prototype tool with eight categories of patients defined by patient severity and nursing intensity parameters. From this tool, the demand for nursing care was defined in patient points with one point equal to one hour of RN time. Validity and reliability of the instrument was determined as follows: (1) Content validity by a panel of expert nurses; (2) predictive validity through a paired t-test analysis of preshift and postshift categorization of patients; (3) initial reliability by a one month pilot of the instrument in a practice setting; and (4) interrater reliability by the Kappa statistic.^ The nursing distribution system was a linear programming model using a branch and bound technique for obtaining integer solutions. The objective function was to minimize the total number of nursing personnel used by optimally assigning the staff to meet the acuity needs of the units. A penalty weight was used as a coefficient of the objective function variables to define priorities for allocation of staff.^ The demand constraints were requirements to meet the total acuity points needed for each unit and to have a minimum number of RNs on each unit. Supply constraints were: (1) total availability of each type of staff and the value of that staff member (value was determined relative to that type of staff's ability to perform the job function of an RN (i.e., value for eight hours RN = 8 points, LVN = 6 points); (2) number of personnel available for floating between units.^ The capability of the model to assign staff quantitatively and qualitatively equal to the manual method was established by a thirty day comparison. Sensitivity testing demonstrated appropriate adjustment of the optimal solution to changes in penalty coefficients in the objective function and to acuity totals in the demand constraints.^ Further investigation of the model documented: correct adjustment of assignments in response to staff value changes; and cost minimization by an addition of a dollar coefficient to the objective function. ^

Plasma homocysteine levels and Alzheimer's Disease: A systematic review

Objective. To systematically review studies published in English on the relationship between plasma total homocysteine (Hcy) levels and the clinical and/or postmortem diagnosis of Alzheimer's disease (AD) in subjects who are over 60 years old.^ Method. Medline, PubMed, PsycINFO and Academic Search Premier, were searched by using the keywords "homocysteine", "Alzheimer disease" and "dementia", and "cognitive disorders". In addition, relevant articles in PubMed using the "related articles" link and by cross-referencing were identified. The study design, study setting and study population, sample size, the diagnostic criteria of the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) and the Alzheimer's Disease and Related Disorders Association (ADRDA), and description of how Hcy levels were measured or defined had to have been clearly stated. Empirical investigations reporting quantitative data on the epidemiology of the relationship between plasma total Hcy (exposure factor) and AD (outcome) were included in the systematic review.^ Results. A total of 7 studies, which included a total of 2,989 subjects, out of 388 potential articles met the inclusion criteria: four case control and three cohort studies were identified. All 7 studies had association statistics, such as the odds ratio (OR), the relative rates (RR), and the hazard ratio (HR) of AD, examined using multivariate and logistic regression analyses. Three case - comparison studies: Clarke et al. (1998) (OR: 4.5, 95% CI.: 2.2 - 9.2); McIlroy et al. (2002) (OR: 2.9, 95% CI.: 1.00–8.1); Quadri et al. (2004) (OR: 3.7, 95% CI.: 1.1 - 13.1), and two cohort studies: Seshadri et al. (2002) (RR: 1.8, 95% CI.: 1.3 - 2.5); Ravaglia et al. (2005) (HR: 2.1, 95% CI.: 1.7 - 3.8) found a significant association between serum total Hcy and AD. One case-comparison study, Miller et al. (2002) (OR: 2.2, 95% C.I.: 0.3 -16), and one cohort study, Luchsinger et al. (2004) (HR: 1.4, 95% C.I.: 0.7 - 2.3) failed to reject H0.^ Conclusions. The purpose of this review is to provide a thorough analysis of studies that examined the relationship between Hcy levels and AD. Five studies showed a positive statistically significant association between elevated total Hcy values and AD but the association was not statistically significant in two studies. Further research is needed in order to establish evidence of the strong, consistent association between serum total Hcy and AD as well as the presence of the appropriate temporal relationship. To answer these questions, it is important to conduct more prospective studies that examine the occurrence of AD in individuals with and without elevated Hcy values at baseline. In addition, the international standardization of measurements and cut-off points for plasma Hcy levels across laboratories is a critical issue to be addressed for the conduct of future studies on the topic.^

Use of light intensity, temperature, and humidity to verify exposure location

Research studies on the association between exposures to air contaminants and disease frequently use worn dosimeters to measure the concentration of the contaminant of interest. But investigation of exposure determinants requires additional knowledge beyond concentration, i.e., knowledge about personal activity such as whether the exposure occurred in a building or outdoors. Current studies frequently depend upon manual activity logging to record location. This study's purpose was to evaluate the use of a worn data logger recording three environmental parameters—temperature, humidity, and light intensity—as well as time of day, to determine indoor or outdoor location, with an ultimate aim of eliminating the need to manually log location or at least providing a method to verify such logs. For this study, data collection was limited to a single geographical area (Houston, Texas metropolitan area) during a single season (winter) using a HOBO H8 four-channel data logger. Data for development of a Location Model were collected using the logger for deliberate sampling of programmed activities in outdoor, building, and vehicle locations at various times of day. The Model was developed by analyzing the distributions of environmental parameters by location and time to establish a prioritized set of cut points for assessing locations. The final Model consisted of four "processors" that varied these priorities and cut points. Data to evaluate the Model were collected by wearing the logger during "typical days" while maintaining a location log. The Model was tested by feeding the typical day data into each processor and generating assessed locations for each record. These assessed locations were then compared with true locations recorded in the manual log to determine accurate versus erroneous assessments. The utility of each processor was evaluated by calculating overall error rates across all times of day, and calculating individual error rates by time of day. Unfortunately, the error rates were large, such that there would be no benefit in using the Model. Another analysis in which assessed locations were classified as either indoor (including both building and vehicle) or outdoor yielded slightly lower error rates that still precluded any benefit of the Model's use.^

THE APPLICATION OF PATH ANALYSIS TO ASSESSMENT OF IRON NUTRITIONAL STATUS IN MAN

Path analysis has been applied to components of the iron metabolic system with the intent of suggesting an integrated procedure for better evaluating iron nutritional status at the community level. The primary variables of interest in this study were (1) iron stores, (2) total iron-binding capacity, (3) serum ferritin, (4) serum iron, (5) transferrin saturation, and (6) hemoglobin concentration. Correlation coefficients for relationships among these variables were obtained from published literature and postulated in a series of models using measures of those variables that are feasible to include in a community nutritional survey. Models were built upon known information about the metabolism of iron and were limited by what had been reported in the literature in terms of correlation coefficients or quantitative relationships. Data were pooled from various studies and correlations of the same bivariate relationships were averaged after z- transformations. Correlation matrices were then constructed by transforming the average values back into correlation coefficients. The results of path analysis in this study indicate that hemoglobin is not a good indicator of early iron deficiency. It does not account for variance in iron stores. On the other hand, 91% of the variance in iron stores is explained by serum ferritin and total iron-binding capacity. In addition, the magnitude of the path coefficient (.78) of the serum ferritin-iron stores relationship signifies that serum ferritin is the most important predictor of iron stores in the proposed model. Finally, drawing upon known relations among variables and the amount of variance explained in path models, it is suggested that the following blood measures should be made in assessing community iron deficiency: (1) serum ferritin, (2) total iron-binding capacity, (3) serum iron, (4) transferrin saturation, and (5) hemoglobin concentration. These measures (with acceptable ranges and cut-off points) could make possible the complete evaluation of all three stages of iron deficiency in those persons surveyed at the community level. ^