Adaptation of AHRQ patient safety indicators for use in ICD-10 administrative data by an international consortium.

Objective: The Agency for Healthcare Research and Quality (AHRQ) developed Patient Safety Indicators (PSIs) for use with ICD-9-CM data. Many countries have adopted ICD-10 for coding hospital diagnoses. We conducted this study to develop an internationally harmonized ICD-10 coding algorithm for the AHRQ PSIs. Methods: The AHRQ PSI Version 2.1 has been translated into ICD-10-AM (Australian Modification), and PSI Version 3.0a has been independently translated into ICD-10-GM (German Modification). We converted these two country-specific coding algorithms into ICD-10-WHO (World Health Organization version) and combined them to form one master list. Members of an international expert panel-including physicians, professional medical coders, disease classification specialists, health services researchers, epidemiologists, and users of the PSI-independently evaluated this master list and rated each code as either "include," "exclude," or "uncertain," following the AHRQ PSI definitions. After summarizing the independent rating results, we held a face-to-face meeting to discuss codes for which there was no unanimous consensus and newly proposed codes. A modified Delphi method was employed to generate a final ICD-10 WHO coding list. Results: Of 20 PSIs, 15 that were based mainly on diagnosis codes were selected for translation. At the meeting, panelists discussed 794 codes for which consensus had not been achieved and 2,541 additional codes that were proposed by individual panelists for consideration prior to the meeting. Three documents were generated: a PSI ICD-10-WHO version-coding list, a list of issues for consideration on certain AHRQ PSIs and ICD-9-CM codes, and a recommendation to WHO to improve specification of some disease classifications. Conclusion: An ICD-10-WHO PSI coding list has been developed and structured in a manner similar to the AHRQ manual. Although face validity of the list has been ensured through a rigorous expert panel assessment, its true validity and applicability should be assessed internationally.

Adaptation au codage CIM-10 de 15 indicateurs de la sécurité des patients proposés par l'Agence étasunienne pour la recherche et la qualité des soins de santé (AHRQ) [ICD-10 adaptation of 15 Agency for Healthcare Research and Quality patient safety indicators].

BACKGROUND: In the United States, the Agency for Healthcare Research and Quality (AHRQ) has developed 20 Patient Safety Indicators (PSIs) to measure the occurrence of hospital adverse events from medico-administrative data coded according to the ninth revision of the international classification of disease (ICD-9-CM). The adaptation of these PSIs to the WHO version of ICD-10 was carried out by an international consortium. METHODS: Two independent teams transcoded ICD-9-CM diagnosis codes proposed by the AHRQ into ICD-10-WHO. Using a Delphi process, experts from six countries evaluated each code independently, stating whether it was "included", "excluded" or "uncertain". During a two-day meeting, the experts then discussed the codes that had not obtained a consensus, and the additional codes proposed. RESULTS: Fifteen PSIs were adapted. Among the 2569 proposed diagnosis codes, 1775 were unanimously adopted straightaway. The 794 remaining codes and 2541 additional codes were discussed. Three documents were prepared: (1) a list of ICD-10-WHO codes for the 15 adapted PSIs; (2) recommendations to the AHRQ for the improvement of the nosological frame and the coding of PSI with ICD-9-CM; (3) recommendations to the WHO to improve ICD-10. CONCLUSIONS: This work allows international comparisons of PSIs among the countries using ICD-10. Nevertheless, these PSIs must still be evaluated further before being broadly used.

Evaluating thermal treeline indicators based on air and soil temperature using an air-to-soil temperature transfer model

In recent research, both soil (root-zone) and air temperature have been used as predictors for the treeline position worldwide. In this study, we intended to (a) test the proposed temperature limitation at the treeline, and (b) investigate effects of season length for both heat sum and mean temperature variables in the Swiss Alps. As soil temperature data are available for a limited number of sites only, we developed an air-to-soil transfer model (ASTRAMO). The air-to-soil transfer model predicts daily mean root-zone temperatures (10cm below the surface) at the treeline exclusively from daily mean air temperatures. The model using calibrated air and root-zone temperature measurements at nine treeline sites in the Swiss Alps incorporates time lags to account for the damping effect between air and soil temperatures as well as the temporal autocorrelations typical for such chronological data sets. Based on the measured and modeled root-zone temperatures we analyzed. the suitability of the thermal treeline indicators seasonal mean and degree-days to describe the Alpine treeline position. The root-zone indicators were then compared to the respective indicators based on measured air temperatures, with all indicators calculated for two different indicator period lengths. For both temperature types (root-zone and air) and both indicator periods, seasonal mean temperature was the indicator with the lowest variation across all treeline sites. The resulting indicator values were 7.0 degrees C +/- 0.4 SD (short indicator period), respectively 7.1 degrees C +/- 0.5 SD (long indicator period) for root-zone temperature, and 8.0 degrees C +/- 0.6 SD (short indicator period), respectively 8.8 degrees C +/- 0.8 SD (long indicator period) for air temperature. Generally, a higher variation was found for all air based treeline indicators when compared to the root-zone temperature indicators. Despite this, we showed that treeline indicators calculated from both air and root-zone temperatures can be used to describe the Alpine treeline position.

Strategic Plan Performance Indicators, 2002-2003

Stragtegic plan for Iowa State University

Strategic Plan Performance Indicators 2003-2004

Stragtegic plan for Iowa State University

Strategic Plan Performance Indicators, 2001-2002

Stragtegic plan for Iowa State University

Clinical indicators for the initiation of endotracheal suction in children: An integrative review.

BACKGROUND: Critical decisions and interpretation of observations by the nurse caring for the paediatric intensive care (PIC) patient can have dramatic and potential adverse impact on the clinical stability of the patient. A common PIC procedure is endotracheal tube (ETT) suction, however there is inconsistent evidence regarding the clinical indicators to guide and support nursing action. Justification for performing this procedure is not clearly defined within the literature. Further, a review of the literature has failed to establish clear standards for determining if the procedure is warranted, especially for paediatric patients. OBJECTIVE: The objective of the review is to identify current clinical indicators used in practice to determine why ETT suction should be performed. METHOD: An integrative review using a systematic approach to summarise the empirical and theoretical evidence within the literature as it relates to clinical practice was used. RESULTS: Consensus of opinion indicates that ETT suctioning should only be performed when clinically indicated. There is no general consensus regarding which clinical indicators should be measured and used to guide the decision to perform ETT suctioning. CONCLUSION: Research is required to identify the clinical indicators that could be used to design a valid and clinically appropriate tool to use to assist in the decision making process to perform ETT suction.

High incidence of childhood hemolytic uremic syndrome in Switzerland is associated with indicators of livestock farming intensity

Background: Hemolytic-uremic syndrome (HUS) is a multisystem disorder associated with significant morbidity and mortality. Typically, HUS is preceded by an episode of (bloody) diarrhea mostly due to Shiga-toxin (Stx) producing Escherichia coli (STEC). The main reservoir for STEC is the intestine of healthy ruminants, mostly cattle, and recent studies have revealed an association between indicators of livestock density and human STEC infection or HUS, respectively. Nationwide data on HUS in Switzerland have been established through the Swiss Pediatric Surveillance Unit (SPSU) [Schifferli et al. Eur J Pediatr. 2010; 169:591-8]. Aims: Analysis of age-specific incidence rate of childhood HUS and possible association of Shiga-toxin associated HUS (Stx-HUS) with indicators of livestock farming intensity. Methods: Epidemiological and ecological analysis based on the SPSU data (1997-2003) and the database of the Swiss Federal Statistical Office (data on population and agriculture). Results: One hundred-fourteen cases were registered, 88% were ≤5 years old. The overall annual incidence rate was 1.42 (0.60-1.91) and 4.23 (1.76-6.19) per 100000 children ≤5 and ≤16 years, respectively (P = 0.005). Stx-HUS was more frequent compared to cases not associated with STEC (P = 0.002). The incidence rate for Stx-HUS was 3.85 (1.76-5.65) in children ≤5, compared to 0.27 (0.00-0.54) per 100'000 children 5-16 years (P = 0.002), respectively. The incidence rate of cases not associated with STEC infection did not significantly vary with age (P = 0.107). Compared to data from Scotland, Canada, Ireland, Germany, England, Australia, Italy, and Austria the annual incidence rate of HUS in young children is highest in Switzerland. Ecological analysis revealed strong association between the incidence rate of Stx-HUS and indicators of rural occupation (agricultural labourer / population, P = 0.030), farming intensity (livestock breeding farms / population, P = 0.027) and cattle density (cattle / cultivated area, P = 0.013). Conclusions: Alike in other countries, HUS in Switzerland is mostly associated with STEC infection and affects predominantly young children. However, the incidence rate is higher compared to countries abroad and is significantly correlated with indicators of livestock farming intensity. The present data support the impact of direct and indirect contact with animals or fecal contaminants in transmission of STEC to humans.

Panel index VAR models: Specification, Estimation, Testing and Leading Indicators

This paper proposes a method to conduct inference in panel VAR models with cross unit interdependencies and time variations in the coefficients. The approach can be used to obtain multi-unit forecasts and leading indicators and to conduct policy analysis in a multiunit setups. The framework of analysis is Bayesian and MCMC methods are used to estimate the posterior distribution of the features of interest. The model is reparametrized to resemble an observable index model and specification searches are discussed. As an example, we construct leading indicators for inflation and GDP growth in the Euro area using G-7 information.

Health indicators associated with poor sleep quality among university students

Objective To associate the sleep quality of Brazilian undergraduate students with health indicators. Method A cross-sectional study was developed with a random sample of 662 undergraduate students from Fortaleza, Brazil. The demographic data, Pittsburgh Sleep Quality Index and health data indicators (smoking, alcoholism, sedentary lifestyle, nutritional condition and serum cholesterol) were collected through a self-administered questionnaire. Blood was collected at a clinical laboratory. In order to estimate the size of the associations, a Poisson Regression was used. Results For students who are daily smokers, the occurrence of poor sleep was higher than in non-smokers (p<0.001). Prevalence rate values were nevertheless close to 1. Conclusion The likelihood of poor sleep is almost the same in smokers and in alcoholics.


Pleasure-suffering indicators of nursing work in a hemodialysis nursing service

OBJECTIVE To measure the pleasure and suffering indicators at work and relate them to the socio-demographic and employment characteristics of the nursing staff in a hemodialysis center in southern Brazil. METHOD Quantitative research, with 46 workers. We used a self-completed form with demographic and labor data and the Pleasure and Suffering Indicators at Work Scale (PSIWS). We conducted a bivariate and correlation descriptive analysis with significance levels of 5% using the Epi-Info® and PredictiveAnalytics Software programs. RESULTS Freedom of Speech was considered critical; other factors were evaluated as satisfactory. The results revealed a possible association between sociodemographic characteristics and work, and pleasure and suffering indicators. There was a correlation between the factors evaluated. CONCLUSION Despite the satisfactory evaluation, suffering is present in the studied context, expressed mainly by a lack of Freedom of Speech, with the need for interventions to prevent injury to the health of workers.

Iowa Leading Indicators Index Report, July 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, August 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index Report, September 2006

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.