Sociodemographic differences in linkage error: An examination of four large-scale datasets

© 2018 The Author(s). Background: Record linkage is an important tool for epidemiologists and health planners. Record linkage studies will generally contain some level of residual record linkage error, where individual records are either incorrectly marked as belonging to the same individual, or incorrectly marked as belonging to separate individuals. A key question is whether errors in linkage quality are distributed evenly throughout the population, or whether certain subgroups will exhibit higher rates of error. Previous investigations of this issue have typically compared linked and un-linked records, which can conflate bias caused by record linkage error, with bias caused by missing records (data capture errors). Methods: Four large administrative datasets were individually de-duplicated, with results compared to an available 'gold-standard' benchmark, allowing us to avoid methodological issues with comparing linked and un-linked records. Results were compared by gender, age, geographic remoteness (major cities, regional or remote) and socioeconomic status. Results: Results varied between datasets, and by sociodemographic characteristic. The most consistent findings were worse linkage quality for younger individuals (seen in all four datasets) and worse linkage quality for those living in remote areas (seen in three of four datasets). The linkage quality within sociodemographic categories varied between datasets, with the associations with linkage error reversed across different datasets due to quirks of the specific data collection mechanisms and data sharing practices. Conclusions: These results suggest caution should be taken both when linking younger individuals and those in remote areas, and when analysing linked data from these subgroups. Further research is required to determine the ramifications of worse linkage quality in these subpopulations on research outcomes

Data Linkage: A powerful research tool with potential problems

Background: Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes

Interactions between Predation and Resources Shape Zooplankton Population Dynamics

Identifying the relative importance of predation and resources in population dynamics has a long tradition in ecology, while interactions between them have been studied less intensively. In order to disentangle the effects of predation by juvenile fish, algal resource availability and their interactive effects on zooplankton population dynamics, we conducted an enclosure experiment where zooplankton were exposed to a gradient of predation of roach (Rutilus rutilus) at different algal concentrations. We show that zooplankton populations collapse under high predation pressure irrespective of resource availability, confirming that juvenile fish are able to severely reduce zooplankton prey when occurring in high densities. At lower predation pressure, however, the effect of predation depended on algal resource availability since high algal resource supply buffered against predation. Hence, we suggest that interactions between mass-hatching of fish, and the strong fluctuations in algal resources in spring have the potential to regulate zooplankton population dynamics. In a broader perspective, increasing spring temperatures due to global warming will most likely affect the timing of these processes and have consequences for the spring and summer zooplankton dynamics

Smoking during pregnancy: Childbirth and Health Study in Primary Care in Iceland

Objective. To study the prevalence and possible predictors for smoking during pregnancy in Iceland. Design. A cross-sectional study. Setting. Twenty-six primary health care centres in Iceland 2009–2010. Subjects. Women attending antenatal care in the 11th–16th week of pregnancy were invited to participate by convenient consecutive manner, stratified according to residency. A total of 1111 women provided data in this first phase of the cohort study. Main outcome measures. Smoking habits before and during early pregnancy were assessed with a postal questionnaire, which also included questions about socio-demographic background, physical and emotional well-being, and use of medications. Results. The prevalence of smoking prior to pregnancy was 20% (223/1111). During early pregnancy, it was 5% (53/1111). In comparison with women who stopped smoking during early pregnancy, those who continued to smoke had on average a significantly lower level of education, had smoked more cigarettes per day before pregnancy, and were more likely to use nicotine replacement therapy in addition to smoking during pregnancy. A higher number of cigarettes consumed per day before pregnancy and a lower level of education were the strongest predictors for continued smoking during pregnancy. Conclusion. The majority of Icelandic women who smoke stop when they become pregnant, and the prevalence of smoking during pregnancy in Iceland is still about 5%. Our results indicate stronger nicotine dependence in women who do not stop smoking during pregnancy. Awareness of this can help general practitioners (GPs) and others providing antenatal care to approach these women with more insight and empathy, which might theoretically help them to quit