Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the ‘black box’ of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i) the data linkage environment and privacy preservation; (ii) the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods) and (iii) linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.

With the exception of laparoscopic sterilizations, open and laparoscopic gynaecological surgery are associated with comparable risks of adhesion-related readmissions.

Aims: To investigate the lagged effects of cold temperature on cardiorespiratory mortality and to determine whether ''wind chill'' is a better predictor of these effects than ''dry bulb'' temperature. Methods: Generalised linear Poisson regression models were used to investigate the relation between mortality and ''dry bulb'' and ''wind chill'' temperatures in the three largest Scottish cities (Glasgow, Edinburgh, and Aberdeen) between January 1981 and December 2001. Effects of temperature on mortality (lags up to one month) were quantified. Analyses were conducted for the whole year and by season (cool and warm seasons). Main results: Temperature was a significant predictor of mortality with the strongest association observed between temperature and respiratory mortality. There was a non-linear association between mortality and temperature. Mortality increased as temperatures fell throughout the range, but the rate of increase was steeper at temperatures below 11˚C. The association between temperature and mortality persisted at lag periods beyond two weeks but the effect size generally decreased with increasing lag. For temperatures below 11˚C, a 1˚C drop in the daytime mean temperature on any one day was associated with an increase in mortality of 2.9% (95% CI 2.5 to 3.4), 3.4% (95% CI 2.6 to 4.1), 4.8% (95% CI 3.5 to 6.2) and 1.7% (95% CI 1.0 to 2.4) over the following month for all cause, cardiovascular, respiratory, and ''other'' cause mortality respectively. The effect of temperature on mortality was not observed to be significantly modified by season. There was little indication that ''wind chill'' temperature was a better predictor of mortality than ''dry bulb'' temperature. Conclusions: Exposure to cold temperature is an important public health problem in Scotland, particularly for those dying from respiratory disease. M ortality rates for cardiovascular and respiratory disease typically exhibit distinct seasonal variation with the highest rates occurring in the winter months.1 For Scotland, the percentage summer to winter difference in weekly all cause mortality rates is estimated to be in the order of 30%.2 The main factor considered to be influencing the observed seasonal pattern is the relation between mortality and temperature. The association between low temperature and increased morbidity and mortality is well recognised.3 4 What is less clear is the exact nature of the relation. Research has shown that the effect of temperature on mortality can exhibit significant variation from region to region. 5 6 For example, some studies have reported a U or V-shaped relation between temperature and mortality with the maximum number of deaths occurring at each end of the temperature scale 7 8 whereas others have reported a more linear or reverse J-shaped relation, with mortality typically increasing as temperature drops.

Hematopoietic progenitor cell levels were monitored in the peripheral blood and bone marrow of 30 cancer patients receiving recombinant human granulocyte-colony stimulating-factor (rG-CSF) in a phase I/II clinical trial. The absolute number of circulating progenitor cells of granulocyte-macrophage, erythroid, and megakaryocyte lineages showed a dose-related increase up to 100-fold after four days of treatment with rG-CSF and often remained elevated two days after the cessation of therapy. The relative frequency of different types of progenitor cells in peripheral blood remained unchanged. The frequency of progenitor cells in the marrow was variable after rG-CSF treatment but in most patients was slightly decreased. The responsiveness of bone marrow progenitor cells to stimulation in vitro by rG-CSF and granulocyte- macrophage colony-stimulating factor did not change significantly during rG-CSF treatment. In patients nine days after treatment with melphalan and then rG-CSF, progenitor cell levels were very low with doses of rG-CSF at or below 10 micrograms/kg/d, but equaled or exceeded pretreatment values when 30 or 60 micrograms/kg/d of rG-CSF was given.