A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age, based on data from the Spanish FLIP study

Background: The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA). Methods: The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. Results: An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth +/- 10 weeks of start of season, birth weight, breast feeding for = 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6). Conclusion: A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe

IBD2020 global forum: results of an international patient survey on quality of care

Background/Aims IBD2020 is a global forum for standards of care in inflammatory bowel disease (IBD). The aim of the IBD2020 survey was to identify and describe variations in quality care of IBD. Methods Patients with IBD from Finland, Italy, France, Canada, Germany, UK, Spain and Sweden were surveyed during 2013 to 2014, covering: disease characteristics; impact on life and work; organization and perceived quality of care. Results Seven thousand five hundred and seven patients participated (median age, 39 years [range, 10–103 years]; 2,354 male [31.4%]), including 4,097 (54.6%) with Crohn’s disease (CD) and 3,410 (45.4%) with ulcerative colitis (UC). Median time from symptom onset to diagnosis was 1 year for both CD (range, 0–47 years) and UC (range, 0–46 years), with no clear evidence of improvement in diagnostic delay over the preceding 24 years. Half of the patients (3,429; 50.0%) rated their care as “excellent” or “very good,” with similar results for CD and UC across countries. Five factors were significantly (P<0.01) associated with perceived good quality of care: quality of specialist communication; review consultation being long enough; failure to share information; no access to a dietician; speed of advice. Conclusions The IBD2020 survey has highlighted areas related to quality of care of IBD from the patients’ perspective, with scope for improvement

The impact of clinical experience on decision-making regarding the treatment and management of mild-to-moderate ulcerative colitis.

peer reviewe

Tax Evasion, Tax Avoidance and Tax Planning in Australia: The participation in mass-marketed tax avoidance schemes in the Pilbara region of Western Australia in the 1990s

This paper will examine the development of mass-marketed tax avoidance schemes in Australia. It will consider changes in approach to tax avoidance from the ‘bottom of the harbour’ schemes of the 1960s and 1970s to the mass-marketed tax avoidance schemes of the 1990s. It will examine the changing structure of tax avoidance from individually crafted tax avoidance structures designed by accountants and lawyers used by high wealth individuals to mass produced structures targeted at highly paid, and therefore highly taxed, blue collar workers in Australia’s mining industry in the 1990s. In the latter half of the twentieth century ‘unacceptable’ tax planning went from highly expensive, individually ‘tailor made’ structures afforded and used only by the very wealthy, to inexpensive replicated structures marketed to skilled and unskilled tradespeople and labourers. By 1998 over 42 000 Australian taxpayers were engaged in tax avoidance schemes with the highest proportion focussed in the mining regions of Western Australia. In the remote and inhospitable mining community of Pannawonica, which has one of the highest paid workforces in Australia, the Australian Taxation Office identified that as many as one in five taxpayers were engaged in a mass-marketed tax avoidance scheme. The paper will identify the causes of these changes, including the advent of the computerised information technology which permitted ‘mass production’ of business structures designed to exploit business incentives in the Australian taxation system in the 1990s. It will also set these developments within the broader context of the tax compliance culture prevailing in Australia and overseas during this period

Identifying the research, advocacy, policy and implementation needs for the prevention and management of respiratory syncytial virus lower respiratory tract infection in low- and middle-income countries

Introduction: The high burden of respiratory syncytial virus (RSV) infection in young children disproportionately occurs in low- and middle-income countries (LMICs). The PROUD (Preventing RespiratOry syncytial virUs in unDerdeveloped countries) Taskforce of 24 RSV worldwide experts assessed key needs for RSV prevention in LMICs, including vaccine and newer preventive measures. Methods: A global, survey-based study was undertaken in 2021. An online questionnaire was developed following three meetings of the Taskforce panellists wherein factors related to RSV infection, its prevention and management were identified using iterative questioning. Each factor was scored, by non-panellists interested in RSV, on a scale of zero (very-low-relevance) to 100 (very-high-relevance) within two scenarios: (1) Current and (2) Future expectations for RSV management. Results: Ninety questionnaires were completed: 70 by respondents (71.4% physicians; 27.1% researchers/scientists) from 16 LMICs and 20 from nine high-income (HI) countries (90.0% physicians; 5.0% researchers/scientists), as a reference group. Within LMICs, RSV awareness was perceived to be low, and management was not prioritised. Of the 100 factors scored, those related to improved diagnosis particularly access to affordable point-of-care diagnostics, disease burden data generation, clinical and general education, prompt access to new interventions, and engagement with policymakers/payers were identified of paramount importance. There was a strong need for clinical education and local data generation in the lowest economies, whereas upper-middle income countries were more closely aligned with HI countries in terms of current RSV service provision. Conclusion: Seven key actions for improving RSV prevention and management in LMICs are proposed

A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age, based on data from the Spanish FLIP study

Abstract Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA). Methods The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. Results An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6). Conclusion A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.</p

European risk factors' model to predict hospitalization of premature infants born 33-35 weeks' gestational age with respiratory syncytial virus: validation with Italian data

OBJECTIVES: A model for predicting respiratory syncytial virus hospitalization in infants born 33-35 weeks' gestational age (wGA) has been developed from the Spanish FLIP study risk factors. The model correctly classified 71% of cases and the area under the receiver operating characteristic (ROC) curve was 0.791. To assess its applicability in Italy, the model was validated against data from the Osservatorio VRS study. METHODS: Discriminant function analysis was used to validate the model by (a) using the predictive variables identified in FLIP to generate a function from the Italian data and (b) applying the coefficients from the FLIP calculations to the Italian data. RESULTS: The function calculated from the Italian data provided 77% accurate classification (ROC: 0.773). Applying the FLIP coefficients to the Italian data resulted in correctly classifying 68% of cases and a ROC of 0.760. The number needed to treat to prevent hospitalization of 80% of at risk infants was 13.4, based on a hospitalization rate of 5% and 80% treatment efficacy. CONCLUSIONS: The Italian data confirm the predictive ability of the model, which could be used to target palivizumab prophylaxis in Italian infants born 33-35 wGA

Validation of a model to predict hospitalization due to RSV of infants born at 33-35 weeks' gestation

BACKGROUND: A model to predict hospitalization due to respiratory syncytial virus (RSV) of infants born at 33- 35 weeks' gestation was developed using seven risk factors from the Spanish FLIP study "birth +/-10 weeks from the beginning of the RSV season", "birth weight", "breast fed or=2 years", "number of family members with atopy", "number of family members with wheezing", and "gender". The aim of this study was to validate the model using French data. METHODS: The FLIP model [predictive accuracy 71%, receiver operating characteristic (ROC) 0.791] was tested against the French data (77 hospitalized infants with RSV born at 33-35 weeks and 154 age-matched controls) using discriminatory function analysis by applying the FLIP coefficients to the French data and by generating the seven variable model from the French data. RESULTS: Applying the FLIP coefficients to the French dataset, the model correctly classified 69% of cases (ROC 0.627). The predictive power increased to 73% (ROC 0.654) when "number of siblings >or=2 years" was substituted for "number of children at school". The number needed to treat (NNT) in order to prevent 70% of hospitalizations was 18. The model derived using French data could correctly classify 62% of cases in the French data (ROC 0.658). CONCLUSIONS: The model was successfully validated and may potentially optimize immunoprophylaxis in French infants born at 33-3