Socioeconomic inequalities in survival and provision of neonatal care: population based study of very preterm infants

Objectives To assess socioeconomic inequalities in survival and provision of neonatal care among very preterm infants

Nature of socioeconomic inequalities in neonatal mortality: population based study

Objective To investigate time trends in socioeconomic inequalities in cause specific neonatal mortality in order to assess changing patterns in mortality due to different causes, particularly prematurity, and identify key areas of focus for future intervention strategies

Prigodom 60-godišnjice zadružne mlekarne u Škofjoj Loki

Background The Parent Report of Children's Abilities–Revised (PARCA-R) can be used to identify preterm born children at risk for developmental delay at age 24 months. However, standardised scores for assessing all children in the general population and quantifying development relative to the norm are unavailable, thus limiting the use of the questionnaire. We aimed to develop scores that are standardised by age and sex for the PARCA-R to assess children's cognitive and language development at age 24–27 months. Methods Anonymised data from PARCA-R questionnaires that were completed by parents of 2-year-old children in three previous studies were obtained to form a standardisation sample that was representative of the UK general population. Anonymised data were obtained from three further studies to assess the external validity and clinical validity of the standardised scores. We used the lambda-mu-sigma (lambda for skewness, mu for median, sigma for the coefficient of variation) method to develop scores that are standardised by age and sex for three scales (non-verbal cognitive development, language development, and total parent report composite [PRC]) for children in four 1-month age bands, spanning age 23·5–27·5 months. Findings We included 6402 children (mean age 25 months and 1 day [range 23 months and 16 days to 27 months and 15 days]) in the standardisation sample and 709 (mean age 24 months and 19 days [23 months and 16 days to 27 months and 15 days]) to test the external validity and 1456 (mean age 24 months and 8·5 days [23 months and 16 days to 27 months and 15 days]) to test the clinical validity of the standardised scores. For all PARCA-R scales, mean standardised scores approximated 100 (SD 15) in both sexes and all age groups. These scores were independent of socioeconomic status. Standardised scores were close to 100 (15) in the external validation sample, showing the validity of the scores. Standardised scores for the total PRC scale for children born very preterm (<32 weeks' gestation) were 0·47 SD lower on average than the normative mean, and for children with neonatal sepsis were 0·73 SD lower on average than the normative mean. These scores were equivalent to a standardised score of 93 (95% CI 91–94) for children born very preterm and 89 (88–91) for children with neonatal sepsis, thus showing clinical validity. Interpretation The PARCA-R provides a norm-referenced, standardised assessment of cognitive and language development at 24–27 months of age. The questionnaire is available non-commercially in English with translations available in 14 other languages, thus providing clinicians and researchers with a cost-effective tool for assessing development and identifying children with delay

Socioeconomic inequalities in the rate of stillbirths by cause: a population-based study.

OBJECTIVE: To assess time trends in socioeconomic inequalities in overall and cause-specific stillbirth rates in England. DESIGN: Population-based retrospective study. SETTING: England. PARTICIPANTS: Stillbirths occurring among singleton infants born between 1 January 2000 and 31 December 2007. MAIN OUTCOME MEASURE: Cause-specific stillbirth rate per 10 000 births by deprivation tenth and year of birth. Deprivation measured using the UK index of multiple deprivation at Super Output Area level. METHODS: Poisson regression models were used to estimate the relative deprivation gap (comparing the most and least deprived tenths) in rates of stillbirths (overall and cause-specific). Excess mortality was calculated by applying the rates seen in the least deprived tenth to the entire population at risk. Discussions with our local NHS multicentre ethics committee deemed that this analysis of national non-identifiable data did not require separate ethics approval. RESULTS: There were 44 stillbirths per 10 000 births, with no evidence of a change in rates over time. Rates were twice as high in the most deprived tenth compared with the least (rate ratio (RR) 2.1, 95% CI 2.0 to 2.2) with no evidence of a change over time. There was a significant deprivation gap for all specific causes except mechanical events (RR 1.2, 95% CI 0.9 to 1.5). The widest gap was seen for stillbirths due to antepartum haemorrhages (RR 3.1, 95% CI 2.8 to 3.5). No evidence of a change in the rate of stillbirth or deprivation gap over time was seen for any specific cause. CONCLUSION: A wide deprivation gap exists in stillbirth rates for most causes and is not diminishing. Unexplained antepartum stillbirths accounted for 50% of the deprivation gap, and a better understanding of these stillbirths is necessary to reduce socioeconomic inequalities

Potencijalna korist meteoroloških informacija u prometu

U uvodu se ukazuje na ulogu i značaj meteorološke službe u valorizaciji prometa Republike Hrvatske. U drugom poglavlju daju se ciljevi i nabrajaju korisnici meteoroloških informacija u prometu, objašnjava priroda korisničkih zahtjeva i njihova identifikacija, vrste i sadržaj meteoroloških informacija, odnos korisnika prema ovim informacijama, te načini ostvarenja potencijalnih koristi u prometu. U trećem poglavlju obrazlaže se potreba upoznavanja meteorologa s djelatnostima korisnika informacija te osposobljavanje korisnika, i svih onih koji po prirodi posla dolaze u dodir s meteorološkim informacijama na putu od meteorologa do korisnika, kako bi se one optimalno koristile. U četvrtom poglavlju prikazan je sastav simulacijskog modela za istraživanje i primjenu potencijalnih koristi meteoroloških informacija u prometu, te se ukratko opisuje metodologija za njihovo efikasno korištenje od strane korisnika primjenom cost/benefit analize na jednostavnom primjeru

The Preterm Clinical Network (PCN) Database: a web-based systematic method of collecting data on the care of women at risk of preterm birth

Background: Despite much research effort, there is a paucity of conclusive evidence in the field of preterm birth prediction and prevention. The methods of monitoring and prevention strategies offered to women at risk vary considerably around the UK and depend on local maternity care provision. It is becoming increasingly recognised that this experience and knowledge, if captured on a larger scale, could be a utilized as a valuable source of evidence for others. The UK Preterm Clinical Network (UKPCN) was established with the aim of improving care and outcomes for women at risk of preterm birth through the sharing of a wealth of experience and knowledge, as well as the building of clinical and research collaboration. The design and development of a bespoke internet-based database was fundamental to achieving this aim. Method: Following consultation with UKPCN members and agreement on a minimal dataset, the Preterm Clinical Network (PCN) Database was constructed to collect data from women at risk of preterm birth and their children. Information Governance and research ethics committee approval was given for the storage of historical as well as prospectively collected data. Collaborating centres have instant access to their own records, while use of pooled data is governed by the PCN Database Access Committee. Applications are welcomed from UKPCN members and other established research groups. The results of investigations using the data are expected to provide insights into the effectiveness of current surveillance practices and preterm birth interventions on a national and international scale, as well as the generation of ideas for innovation and research. To date, 31 sites are registered as Data Collection Centres, four of which are outside the UK. Conclusion: This paper outlines the aims of the PCN Database along with the development process undertaken from the initial idea to live launch

The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio.

Background: Emphasis is increasingly being placed on the monitoring of clinical outcomes for health care providers. Funnel plots have become an increasingly popular graphical methodology used to identify potential outliers. It is assumed that a provider only displaying expected random variation (i.e. ‘in-control’) will fall outside a control limit with a known probability. In reality, the discrete count nature of these data, and the differing methods, can lead to true probabilities quite different from the nominal value. This paper investigates the true probability of an ‘in control’ provider falling outside control limits for the Standardised Mortality Ratio (SMR). Methods: The true probabilities of an ‘in control’ provider falling outside control limits for the SMR were calculated and compared for three commonly used limits: Wald confidence interval; ‘exact’ confidence interval; probability-based prediction interval. Results: The probability of falling above the upper limit, or below the lower limit, often varied greatly from the nominal value. This was particularly apparent when there were a small number of expected events: for expected events ≤50 the median probability of an ‘in-control’ provider falling above the upper 95% limit was 0.0301 (Wald), 0.0121 (‘exact’), 0.0201 (prediction). Conclusions: It is important to understand the properties and probability of being identified as an outlier by each of these different methods to aid the correct identification of poorly performing health care providers. The limits obtained using probability-based prediction limits have the most intuitive interpretation and their properties can be defined a priori. Funnel plot control limits for the SMR should not be based on confidence intervals

Example using data from the Oxford John Radcliffe Hospital NHS Trust.

<p>Mortality following paediatric cardiac surgery at the Oxford John Radcliffe Hospital NHS Trust 2000–2008 by surgical procedure group, with 95% control limits calculated using three interpolation methods: Method 1 (blue); Method 2 (green) and Method 3 (red).</p

Probability of falling outside control limits.

<p>Probability of identification of falling above the upper limit or below the lower limit for three different interpolation methods.</p