Cost-effectiveness of intensified interventions to control healthcare-associated MRSA infections -- A systematic literature review

Methicillin Resistant Staphylococcus aureus healthcare-associated infections (MRSA HAIs) are a major cause of morbidity in hospitalized patients. They pose great economic burden to hospitals caring for these patients. Intensified Interventions aim to control MRSA HAIs. Cost-effectiveness of Intensified Interventions is largely unclear. We performed a review of cost-effectiveness literature on Intensified Interventions , and provide a summary of study findings, the status of economic research in the area, and information that will help decision-makers at regional level and guide future research.^ We conducted literature search using electronic database PubMed, EBSCO, and The Cochrane Library. We limited our search to English articles published after 1999. We reviewed a total of 1,356 titles, and after applying our inclusion and exclusion criteria selected seven articles for our final review. We modified the Economic Evaluation Abstraction Form provided by CDC, and used this form to abstract data from studies.^ Of the seven selected articles two were cohort studies and the remaining five were modeling studies. They were done in various countries, in different study settings, and with different variations of the Intensified Intervention . Overall, six of the seven studies reported that Intensified Interventions were dominant or at least cost-effective in their study setting. This effect persisted on sensitivity testing.^ We identified many gaps in research in this field. The cost-effectiveness research in the field is mostly composed of modeling studies. The studies do not always clearly describe the intervention. The intervention and infection costs and the sources for these costs are not always explicit or are missing. In modeling studies, there is uncertainty associated with some key model inputs, but these inputs are not always identified. The models utilized in the modeling studies are not always tested for internal consistency or validity. Studies usually test the short term cost-effectiveness of Intensified Interventions but not the long results.^ Our study limitation was the inability to adjust for differences in study settings, intervention costs, disease costs, or effectiveness measures. Our study strength is the presentation of a focused literature review of Intensified Interventions in hospital settings. Through this study we provide information that will help decision makers at regional level, help guide future research, and might change clinical care and policies. ^

STUDIES ON ENERGY METABOLISM IN THE HIPPOCAMPUS AFTER ISCHEMIA

The gerbil model of ischemia was used to determine the effect of carotid occlusion on energy metabolites in cellular layers of discrete regions of the hippocampus and dentate gyrus. Levels of glucose, glycogen, ATP and phosphocreatine (PCr) were unchanged after 1 minute of ischemia. However, 3 minutes of ischemia produced a dramatic decrease in net levels of all metabolites. No additional decrease was observed after 15 minutes of ischemia. Re-establishment of the blood flow for 5 minutes after a 15 minute ischemic episode returned all metabolites to pre-ischemia levels. Concentrations of glucose and glycogen were elevated in sham-operated animals as a function of the pentobarbital anesthetic employed. In other studies, elevated GABA levels (produced by inhibiting GABA-transaminase with (gamma)-vinyl-GABA (GVG)) were found to decrease the rate of utilization of the high-energy phosphate metabolites ATP and PCr in the mouse cortex. In addition, glucose and glycogen levels were increased. Thus, tonic inhibition by GABA produced decreased cellular activity. Additional experiments demonstrated the attenuation of ischemia-induced metabolite depletion in cellular layers of regions of the hippocampus, dentate gyrus and cortex after GVG administration. Under ether, 1 minute of bilateral carotid occlusion produced a dramatic decrease in metabolite levels. After GVG treatment, the decrease was blocked completely for glucose, glycogen and ATP, and partially for PCr. Therefore, GABA-transaminase inhibition produced increased levels of GABA which subsequently decreased cellular activity. The protection against ischemia may have been due to (a)decreased metabolic rate; the available energy stores were utilized at a slower rate, and (b)increased levels of energy substrates; additional supplies available to maintain viability. These data suggest that the functional state of neural tissue can determine the response to metabolic stress. ^

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^