Condição microbiológica dos leitos hospitalares antes e depois de sua limpeza

INTRODUÇÃO: O meio ambiente hospitalar guarda uma íntima relação com as infecções hospitalares, podendo proporcionar focos de contato e de transmissão. Como a higiene representa uma das formas de controlar a contaminação ambiental, realizou-se estudo para avaliar as condições microbiológicas dos colchões hospitalares antes e depois de sua limpeza. MÉTODOS: Utilizaram-se, para a colheita dos espécimes, placas de contato -- Rodac-plate ¾ preparadas com meio de cultura ágar-sangue. Selecionaram-se os leitos de acordo com critérios previamente estabelecidos, e os locais de colheita sob o colchão foram escolhidos por sorteio aleatório. Aplicou-se o teste estatístico de Goodman para o estudo das alterações numéricas quanto a positividade das placas. RESULTADOS: Foram investigados 52 colchões, totalizando 520 placas, das quais 514 (98,8%) resultaram em culturas positivas, sendo que 259 corresponderam ao período anterior à limpeza e 255 ao período posterior ao procedimento. Houve redução de culturas positivas em apenas 4 placas. CONCLUSÕES: Os resultados obtidos sugerem que a limpeza, da forma como vem sendo conduzida, provoca o deslocamento da carga microbiana para outros pontos do colchão em vez de diminuí-la, resultando na manutenção da quantidade de microorganismos que existia anteriormente à limpeza.

Healthcare-associated infections: challenges to public health in Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Nomenclature pertaining to environmental sanitation /

Item 0507-B-10

Dimensions and reliability of a hospital safety climate questionnaire in Chinese health-care practice

The aim of the current study was to examine the dimensions and reliability of a hospital safety climate questionnaire in Chinese health-care practice. To achieve this, a cross-sectional survey of health-care professionals was undertaken at a university teaching hospital in Shandong province, China. Our survey instrument demonstrated very high internal consistency, comparing well with previous research in this field conducted in other countries. Factor analysis highlighted four key dimensions of safety climate, which centred on employee personal protection, employee interactions, safetyrelated housekeeping and time pressures. Overall, this study suggests that hospital safety climate represents an important aspect of health-care practice in contemporary China.

A trilocus sequence typing scheme for hospital epidemiology and subspecies differentiation of an important nosocomial pathogen, Enterococcus faecalis.

In this study, we present a trilocus sequence typing (TLST) scheme based on intragenic regions of two antigenic genes, ace and salA (encoding a collagen/laminin adhesin and a cell wall-associated antigen, respectively), and a gene associated with antibiotic resistance, lsa (encoding a putative ABC transporter), for subspecies differentiation of Enterococcus faecalis. Each of the alleles was analyzed using 50 E. faecalis isolates representing 42 diverse multilocus sequence types (ST(M); based on seven housekeeping genes) and four groups of clonally linked (by pulsed-field gel electrophoresis [PFGE]) isolates. The allelic profiles and/or concatenated sequences of the three genes agreed with multilocus sequence typing (MLST) results for typing of 49 of the 50 isolates; in addition to the one exception, two isolates were found to have identical TLST types but were single-locus variants (differing by a single nucleotide) by MLST and were therefore also classified as clonally related by MLST. TLST was also comparable to PFGE for establishing short-term epidemiological relationships, typing all isolates classified as clonally related by PFGE with the same type. TLST was then applied to representative isolates (of each PFGE subtype and isolation year) of a collection of 48 hospital isolates and demonstrated the same relationships between isolates of an outbreak strain as those found by MLST and PFGE. In conclusion, the TLST scheme described here was shown to be successful for investigating short-term epidemiology in a hospital setting and may provide an alternative to MLST for discriminating isolates.

Correcting for bias when estimating the cost of hospital-acquired infection : an analysis of lower respiratory tract infections in non-surgical patients

Hospital acquired infections (HAI) are costly but many are avoidable. Evaluating prevention programmes requires data on their costs and benefits. Estimating the actual costs of HAI (a measure of the cost savings due to prevention) is difficult as HAI changes cost by extending patient length of stay, yet, length of stay is a major risk factor for HAI. This endogeneity bias can confound attempts to measure accurately the cost of HAI. We propose a two-stage instrumental variables estimation strategy that explicitly controls for the endogeneity between risk of HAI and length of stay. We find that a 10% reduction in ex ante risk of HAI results in an expected savings of £693 ($US 984).

The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting

Objective: To compare the effectiveness of the STRATIFY falls tool with nurses’ clinical judgments in predicting patient falls. Study Design and Setting: A prospective cohort study was conducted among the inpatients of an acute tertiary hospital. Participants were patients over 65 years of age admitted to any hospital unit. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive values (NPV) of the instrument and nurses’ clinical judgments in predicting falls were calculated. Results: Seven hundred and eighty-eight patients were screened and followed up during the study period. The fall prevalence was 9.2%. Of the 335 patients classified as being ‘‘at risk’’ for falling using the STRATIFY tool, 59 (17.6%) did sustain a fall (sensitivity50.82, specificity50.61, PPV50.18, NPV50.97). Nurses judged that 501 patients were at risk of falling and, of these, 60 (12.0%) fell (sensitivity50.84, specificity50.38, PPV50.12, NPV50.96). The STRATIFY tool correctly identified significantly more patients as either fallers or nonfallers than the nurses (P50.027). Conclusion: Considering the poor specificity and high rates of false-positive results for both the STRATIFY tool and nurses’ clinical judgments, we conclude that neither of these approaches are useful for screening of falls in acute hospital settings.

Determinants of pre-hospital care non-usage for patients with emergency care needs [including commentary by Snooks H.]

Objective-To establish the demographic, health status and insurance determinants of pre-hospital ambulance non-usage for patients with emergency medical needs. Methods-Triage category, date of birth, sex, marital status, country of origin, method and time of arrival, ambulance insurance status, diagnosis, and disposal were collected for all patients who presented over a four month period (n=10 229) to the emergency department of a major provincial hospital. Data for patients with urgent (n=678) or critical care needs (n=332) who did not use pre-hospital care were analysed using Poisson regression. Results-Only a small percentage (6.6%) of the total sample were triaged as having urgent medical needs or critical care needs (3.2%). Predictors of usage for those with urgent care needs included age greater than 65 years (prevalence ratio (PR)=0.54; 95% confidence interval (CI)= 0.35 to 0.83), being admitted to intensive care or transferred to another hospital (PR=0.62; 95% CI=0.44 to 0.89) or ward (PR=0.72; 95% CI=0.56 to 0.93) and ambulance insurance status (PR=0.67; 95% CI=052 to 0.86). Sex, marital status, time of day and country of origin were not predictive of usage and non-usage. Predictors of usage for those with critical care needs included age 65 years or greater (PR=0.45; 95% CI=0.25 to 0.81) and a diagnosis of trauma (PR=0.49; 95% CI=0.26 to 0.92). A non-English speaking background was predictive of non-usage (PR=1.98; 95% CI=1.06 to 3.70). Sex, marital status, time of day, triage and ambulance insurance status were not predictive of non-usage. Conclusions-Socioeconomic and medical factors variously influence ambulance usage depending on the severity or urgency of the medical condition. Ambulance insurance status was less of an influence as severity of condition increased suggesting that, at a critical level of urgency, patients without insurance are willing to pay for a pre-hospital ambulance service.

Using Trauma Injury Severity Score (TRISS) variables to predict length of hospital stay following trauma in New Zealand

Aim – To develop and assess the predictive capabilities of a statistical model that relates routinely collected Trauma Injury Severity Score (TRISS) variables to length of hospital stay (LOS) in survivors of traumatic injury. Method – Retrospective cohort study of adults who sustained a serious traumatic injury, and who survived until discharge from Auckland City, Middlemore, Waikato, or North Shore Hospitals between 2002 and 2006. Cubic-root transformed LOS was analysed using two-level mixed-effects regression models. Results – 1498 eligible patients were identified, 1446 (97%) injured from a blunt mechanism and 52 (3%) from a penetrating mechanism. For blunt mechanism trauma, 1096 (76%) were male, average age was 37 years (range: 15-94 years), and LOS and TRISS score information was available for 1362 patients. Spearman’s correlation and the median absolute prediction error between LOS and the original TRISS model was ρ=0.31 and 10.8 days, respectively, and between LOS and the final multivariable two-level mixed-effects regression model was ρ=0.38 and 6.0 days, respectively. Insufficient data were available for the analysis of penetrating mechanism models. Conclusions – Neither the original TRISS model nor the refined model has sufficient ability to accurately or reliably predict LOS. Additional predictor variables for LOS and other indicators for morbidity need to be considered.