Injury data in British Columbia: policy maker perspectives on knowledge transfer.

Provincial and regional decision makers in the injury prevention field were interviewed in British Columbia (B.C.) to obtain their views about best processes for the transfer or dissemination of relevant data. These decision makers (n = 13) indicated that data should provide them with a holistic and comprehensive picture to support their decision processes. In addition, they felt information about injury types and rates should be linked backward to determinants or causes and forward to consequences or outcomes. This complete chain of data is needed for planning and evaluating health promotion interventions. It was also felt that data providers needed to devote more effort to fostering effective receptor capacity, so that injury prevention professionals will be better able to understand, interpret and apply the data. These findings can likely be generalized to other jurisdictions and policy areas, and offer additional insight into the practicalities of knowledge transfer and exchange in researcher/decision maker partnerships.

Current CHS and NHBPEP criteria for severe preeclampsia do not uniformly predict adverse maternal or perinatal outcomes.

OBJECTIVE: To determine the association between adverse maternal/perinatal outcomes and Canadian and U.S. preeclampsia severity criteria. METHODS: Using PIERS data (Preeclampsia Integrated Estimate of RiSk), an international continuous quality improvement project for women hospitalized with preeclampsia, we examined the association between preeclampsia severity criteria and adverse maternal and perinatal outcomes (univariable analysis, Fisher's exact test). Not evaluated were variables performed in <80% of pregnancies (e.g., 24-hour proteinuria). RESULTS: Few of the evaluated variables were associated with adverse maternal (chest pain/dyspnea, thrombocytopenia, 'elevated liver enzymes', HELLP syndrome, and creatinine >110 microM) or perinatal outcomes (dBP >110 mm Hg and suspected abruption) (at p < 0.01). CONCLUSIONS: In the PIERS cohort, most factors used in the Canadian or American classifications of severe preeclampsia do not predict adverse maternal and/or perinatal outcomes. Future classification systems should take this into account.

Perinatal predictors of outcome in gastroschisis.

OBJECTIVE: To identify perinatal risk variables predictive of outcome in gastroschisis. STUDY DESIGN: Gastroschisis cases were collected over a 3-year period from a national database. Risk variables evaluated included gestational age (GA), birth weight, time of birth, admission illness severity (score for neonatal acute physiology-II, SNAP-II) score, and abdominal closure type. Mortality and survival outcomes were analyzed. Multivariate analyses were performed. RESULT: In all, 239 infants were survived (96%). SNAP-II score predicted mortality (relative risk (RR)=1.07, 95% confidence interval (CI)=1.0 to 1.1). Length of hospital stay (LOS) and ventilation days were predicted by GA and by SNAP-II score. SNAP-II score predicted total parenteral nutrition (TPN) days (P=0.006). Severe cholestasis (conjugated bilirubin of >10 mg per 100 ml) was inversely related to GA (RR=0.77, 95% CI=0.61 to 0.97) and directly to categorical SNAP-II score (RR=3.4, 95% CI=1.2 to 10.1). Urgent closure predicted fewer TPN days (P=0.003) and shorter LOS (P=0.0002). CONCLUSION: SNAP-II scores significantly predict mortality and survival outcomes. Urgent closure favors fewer TPN days and shorter LOS. Our data refute routine preterm delivery in gastroschisis.

A conceptual model of water's role as a reservoir in Helicobacter pylori transmission: a review of the evidence.

Helicobacter pylori infection plays a role in the development of chronic gastritis, peptic ulcer and gastric cancer, yet the route of transmission into susceptible hosts remains unknown. Studies employing microbiological techniques have demonstrated that H. pylori has the ability to survive when introduced into water and that H. pylori is present in water and other environmental samples all over the world. Epidemiological studies have shown that water source and exposures related to water supply, including factors related to sewage disposal and exposure to animals, are risk factors for infection. This review describes the microbiological and epidemiological evidence for, and proposes a model of, waterborne H. pylori transmission outlining important features in the transmission cycle. In the model, humans and animals shed the bacteria in their faeces and the mechanisms for entry into water, and for survival, ingestion and infection are dependent upon a range of environmental influences. Verification of the proposed model pathways has important implications for public-health prevention strategies.

Parametric bootstrap and penalized quasi-likelihood inference in conditional autoregressive models.

This paper discusses a variety of conditional autoregressive (CAR) models for mapping disease rates, beyond the usual first-order intrinsic CAR model. We illustrate the utility and scope of such models for handling different types of data structures. To encourage their routine use for map production at statistical and health agencies, a simple algorithm for fitting such models is presented. This is derived from penalized quasi-likelihood (PQL) inference which uses an analogue of best-linear unbiased estimation for the regional risk ratios and restricted maximum likelihood for the variance components. We offer the practitioner here the use of the parametric bootstrap for inference. It is more reliable than standard maximum likelihood asymptotics for inference purposes since relevant hypotheses for the mapping of rates lie on the boundary of the parameter space. We illustrate the parametric bootstrap test of the practically relevant and important simplifying hypothesis that there is no spatial autocorrelation. Although the parametric bootstrap requires computational effort, it is straightforward to implement and offers a wealth of information relating to the estimators and their properties. The proposed methodology is illustrated by analysing infant mortality in the province of British Columbia in Canada.

Autoregressive spatial smoothing and temporal spline smoothing for mapping rates.

This article proposes generalized additive mixed models for the analysis of geographic and temporal variability of mortality rates. This class of models accommodates random spatial effects and fixed and random temporal components. Spatiotemporal models that use autoregressive local smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are developed. The objective is the identification of temporal treads and the production of a series of smoothed maps from which spatial patterns of mortality risks can be monitored over time. Regions with consistently high rate estimates may be followed for further investigation. The methodology is illustrated by analysis of British Columbia infant mortality data.