Publications by authors named "Jim Bottomley"

Objective: to assess the association between neighbourhood family income and adverse birth outcomes.

Methods: we conducted a retrospective cohort study of 334 231 singleton births during 2004 and 2006 based on the Niday Perinatal Database from Ontario. Median neighbourhood family incomes from the 2001 Canadian census were linked with the Niday Perinatal Database by dissemination areas.

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Background: A common disclosure control practice for health datasets is to identify small geographic areas and either suppress records from these small areas or aggregate them into larger ones. A recent study provided a method for deciding when an area is too small based on the uniqueness criterion. The uniqueness criterion stipulates that an the area is no longer too small when the proportion of unique individuals on the relevant variables (the quasi-identifiers) approaches zero.

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Objective: We sought to examine the difference in use of labor epidural analgesia among women from different neighborhood socioeconomic groups.

Study Design: Neighborhood socioeconomic variables from the 2001 Canadian Census were linked to singleton vaginal births from the Niday perinatal database (2004-2006) in Ontario, Canada. Births were divided into income and education groups by quintiles.

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Objective: To ascertain differences in pregnancy outcomes between women with diabetes subtypes (type 1 [DM1], type 2 [DM2], women with gestational [GDM])] and non-diabetic women within a large Canadian population.

Methods: We performed a retrospective multi-cohort analysis of all obstetrical deliveries that occurred in the province of Ontario between April 1, 2005, and March 31, 2006. Data were extracted from the Ontario Niday Perinatal Database.

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Article Synopsis
  • Explicit patient consent requirements in privacy laws can hurt health research by causing selection bias and making it harder to recruit participants; de-identifying data can circumvent these issues.
  • The authors created and tested a new de-identification algorithm, OLA (Optimal Lattice Anonymization), to ensure it meets k-anonymity standards for health datasets.
  • In comparisons with other k-anonymity algorithms, OLA showed less information loss and better performance speed, making it a superior choice for de-identifying health data.
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Objective: To assess the utilization of health care services by pregnant women affected by preeclampsia (PE).

Design: Population-based study.

Setting: Perinatal partnership hospitals in Ontario.

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Objective: To assess the association of intrauterine insemination, in vitro fertilization (IVF) and ovulation induction with the risk of preeclampsia.

Methods: We conducted a population based retrospective cohort study of pregnancies conceived by assisted reproductive technology (1357 exposure subjects, 5190 controls) based on 2005 Niday Perinatal Database for Ontario, Canada. All pregnancies conceived by assisted reproductive technology were identified as exposure group.

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Objective: To assess the risk of birth defects in infants born after assisted human reproduction (AHR).

Design: Retrospective cohort study.

Setting: Niday Perinatal Database for the province of Ontario, 82 sites, both primary and tertiary centers.

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