Maternal and early life exposure to phthalates: The Plastics and Personal-care Products use in Pregnancy (P4) study.

Phthalates are a group of chemicals found in a number of consumer products; some of these phthalates have been shown to possess estrogenic activity and display anti-androgenic effects. While a number of biomonitoring studies of phthalates in pregnant women and infants have been published, there is a paucity of data based on both multiple sampling periods and in different matrices. Phthalate metabolites were measured in 80 pregnant women and their infants in Ottawa Canada (2009-2010) in urine, meconium and breast milk collected at various time periods pre- and post-parturition. At least 50% of the women had at least one urine sample greater than the limit of detection (LOD) for the various phthalate metabolites, with the exception of mono-n-octyl phthalate (MnOP), mono-isononyl phthalate (MiNP) and mono(carboxy-isooctyl) phthalate (MCiOP). Four major clusters of maternal urinary metabolites were identified. Among infants (n=61), the following metabolites were rarely (< 10%) detected: mono-cyclohexyl phthalate (MCHP), mono-isononyl phthalate (MiNP), mono-methyl phthalate (MMP), and mono-n-octyl phthalate (MnOP). While mono-benzyl phthalate (MBzP), mono-3-carboxypropyl phthalate (MCPP), MEHHP, and MEOHP were frequently detected in maternal urines at any time point, these metabolites were rarely detected in breast milk. Maternal urinary concentrations of MEP and the DEHP metabolites were higher in samples collected during pregnancy than postnatally. No statistically significant differences were observed in infant's urinary phthalate concentrations between breast-fed and bottle-fed infants. Significant correlations were observed between maternal urinary MEHHP (r=0.35), MEOHP (r=0.35) and MEP (r=0.37) collected at <20weeks gestation with levels in meconium and between MBzP (r=0.78) and MEP (r=0.56) in maternal and infant urine collected 2-3months after birth. These results suggest at least some maternal-fetal-infant transfer of phthalates and that meconium may be a useful matrix for measuring in utero exposure to phthalates.

Personal Care Product Use in Pregnancy and the Postpartum Period: Implications for Exposure Assessment.

Concern regarding the potential for developmental health risks associated with certain chemicals (e.g., phthalates, antibacterials) used in personal care products is well documented; however, current exposure data for pregnant women are limited. The objective of this study was to describe the pattern of personal care product use in pregnancy and the post-partum period. Usage patterns of personal care products were collected at six different time points during pregnancy and once in the postpartum period for a cohort of 80 pregnant women in Ottawa, Canada. The pattern of use was then described and groups of personal care product groups commonly used together were identified using hierarchical cluster analysis. The results showed that product use varied by income and country of birth. General hygiene products were the most commonly used products and were consistently used over time while cosmetic product use declined with advancing pregnancy and post-delivery. Hand soaps and baby products were reported as used more frequently after birth. This study is the first to track personal care product use across pregnancy and into the postpartum period, and suggests that pregnant populations may be a unique group of personal care product users. This information will be useful for exposure assessments.

Maternal and infant exposure to environmental phenols as measured in multiple biological matrices.

BACKGROUND: Results of recent national surveys have shown the high prevalence of exposure to bisphenol A (BPA) and triclosan (TCS) among the general population; however biomonitoring data for pregnant women and infants are limited. METHODS: Women (n=80) were recruited from early prenatal clinics and asked to collect urine samples multiple times during pregnancy and once 2-3 months post-partum. Samples of infant urine and meconium as well as breast milk and infant formula were also collected. Biospecimens were analyzed by GC-MS/MS for BPA, TCS and triclocarban (TCC). RESULTS: Triclosan was detected in over 80% of the maternal urines (geometric mean (GM): 21.61 µg/L), 60% of the infant urines (GM: 2.8 µg/L), 46% of the breast milk and 80% of the meconium samples. Triclocarban was rarely detected in any of the biospecimens. Median total BPA concentrations were 1.21 and 0.24 µg/L in maternal and infant urines, respectively. Free BPA was detected in only 11% of infant urine samples. The meconium of female infants had significantly higher concentrations of total BPA and TCS than those of males, while no differences were observed in infant urine concentrations by sex. CONCLUSIONS: We found widespread exposure among pregnant women and infants to environmental phenols, with large inter-individual variability in exposure to triclosan. These data will contribute to the risk assessment of these chemicals, especially in susceptible sub-populations.

Evaluating the risk of patient re-identification from adverse drug event reports.

BACKGROUND: Our objective was to develop a model for measuring re-identification risk that more closely mimics the behaviour of an adversary by accounting for repeated attempts at matching and verification of matches, and apply it to evaluate the risk of re-identification for Canada's post-marketing adverse drug event database (ADE).Re-identification is only demonstrably plausible for deaths in ADE. A matching experiment between ADE records and virtual obituaries constructed from Statistics Canada vital statistics was simulated. A new re-identification risk is considered, it assumes that after gathering all the potential matches for a patient record (all records in the obituaries that are potential matches for an ADE record), an adversary tries to verify these potential matches. Two adversary scenarios were considered: (a) a mildly motivated adversary who will stop after one verification attempt, and (b) a highly motivated adversary who will attempt to verify all the potential matches and is only limited by practical or financial considerations. METHODS: The mean percentage of records in ADE that had a high probability of being re-identified was computed. RESULTS: Under scenario (a), the risk of re-identification from disclosing the province, age at death, gender, and exact date of the report is quite high, but the removal of province brings down the risk significantly. By only generalizing the date of reporting to month and year and including all other variables, the risk is always low. All ADE records have a high risk of re-identification under scenario (b), but the plausibility of that scenario is limited because of the financial and practical deterrent even for highly motivated adversaries. CONCLUSIONS: It is possible to disclose Canada's adverse drug event database while ensuring that plausible re-identification risks are acceptably low. Our new re-identification risk model is suitable for such risk assessments.

Estimating the re-identification risk of clinical data sets.

BACKGROUND: De-identification is a common way to protect patient privacy when disclosing clinical data for secondary purposes, such as research. One type of attack that de-identification protects against is linking the disclosed patient data with public and semi-public registries. Uniqueness is a commonly used measure of re-identification risk under this attack. If uniqueness can be measured accurately then the risk from this kind of attack can be managed. In practice, it is often not possible to measure uniqueness directly, therefore it must be estimated. METHODS: We evaluated the accuracy of uniqueness estimators on clinically relevant data sets. Four candidate estimators were identified because they were evaluated in the past and found to have good accuracy or because they were new and not evaluated comparatively before: the Zayatz estimator, slide negative binomial estimator, Pitman's estimator, and mu-argus. A Monte Carlo simulation was performed to evaluate the uniqueness estimators on six clinically relevant data sets. We varied the sampling fraction and the uniqueness in the population (the value being estimated). The median relative error and inter-quartile range of the uniqueness estimates was measured across 1000 runs. RESULTS: There was no single estimator that performed well across all of the conditions. We developed a decision rule which selected between the Pitman, slide negative binomial and Zayatz estimators depending on the sampling fraction and the difference between estimates. This decision rule had the best consistent median relative error across multiple conditions and data sets. CONCLUSION: This study identified an accurate decision rule that can be used by health privacy researchers and disclosure control professionals to estimate uniqueness in clinical data sets. The decision rule provides a reliable way to measure re-identification risk.

The re-identification risk of Canadians from longitudinal demographics.

BACKGROUND: The public is less willing to allow their personal health information to be disclosed for research purposes if they do not trust researchers and how researchers manage their data. However, the public is more comfortable with their data being used for research if the risk of re-identification is low. There are few studies on the risk of re-identification of Canadians from their basic demographics, and no studies on their risk from their longitudinal data. Our objective was to estimate the risk of re-identification from the basic cross-sectional and longitudinal demographics of Canadians. METHODS: Uniqueness is a common measure of re-identification risk. Demographic data on a 25% random sample of the population of Montreal were analyzed to estimate population uniqueness on postal code, date of birth, and gender as well as their generalizations, for periods ranging from 1 year to 11 years. RESULTS: Almost 98% of the population was unique on full postal code, date of birth and gender: these three variables are effectively a unique identifier for Montrealers. Uniqueness increased for longitudinal data. Considerable generalization was required to reach acceptably low uniqueness levels, especially for longitudinal data. Detailed guidelines and disclosure policies on how to ensure that the re-identification risk is low are provided. CONCLUSIONS: A large percentage of Montreal residents are unique on basic demographics. For non-longitudinal data sets, the three character postal code, gender, and month/year of birth represent sufficiently low re-identification risk. Data custodians need to generalize their demographic information further for longitudinal data sets.

A method for managing re-identification risk from small geographic areas in Canada.

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. However, using a uniqueness value of zero is quite a stringent threshold, and is only suitable when the risks from data disclosure are quite high. Other uniqueness thresholds that have been proposed for health data are 5% and 20%. METHODS: We estimated uniqueness for urban Forward Sortation Areas (FSAs) by using the 2001 long form Canadian census data representing 20% of the population. We then constructed two logistic regression models to predict when the uniqueness is greater than the 5% and 20% thresholds, and validated their predictive accuracy using 10-fold cross-validation. Predictor variables included the population size of the FSA and the maximum number of possible values on the quasi-identifiers (the number of equivalence classes). RESULTS: All model parameters were significant and the models had very high prediction accuracy, with specificity above 0.9, and sensitivity at 0.87 and 0.74 for the 5% and 20% threshold models respectively. The application of the models was illustrated with an analysis of the Ontario newborn registry and an emergency department dataset. At the higher thresholds considerably fewer records compared to the 0% threshold would be considered to be in small areas and therefore undergo disclosure control actions. We have also included concrete guidance for data custodians in deciding which one of the three uniqueness thresholds to use (0%, 5%, 20%), depending on the mitigating controls that the data recipients have in place, the potential invasion of privacy if the data is disclosed, and the motives and capacity of the data recipient to re-identify the data. CONCLUSION: The models we developed can be used to manage the re-identification risk from small geographic areas. Being able to choose among three possible thresholds, a data custodian can adjust the definition of "small geographic area" to the nature of the data and recipient.

The inadvertent disclosure of personal health information through peer-to-peer file sharing programs.

OBJECTIVE: There has been a consistent concern about the inadvertent disclosure of personal information through peer-to-peer file sharing applications, such as Limewire and Morpheus. Examples of personal health and financial information being exposed have been published. We wanted to estimate the extent to which personal health information (PHI) is being disclosed in this way, and compare that to the extent of disclosure of personal financial information (PFI). DESIGN: After careful review and approval of our protocol by our institutional research ethics board, files were downloaded from peer-to-peer file sharing networks and manually analyzed for the presence of PHI and PFI. The geographic region of the IP addresses was determined, and classified as either USA or Canada. MEASUREMENT: We estimated the proportion of files that contain personal health and financial information for each region. We also estimated the proportion of search terms that return files with personal health and financial information. We ascertained and discuss the ethical issues related to this study. RESULTS: Approximately 0.4% of Canadian IP addresses had PHI, as did 0.5% of US IP addresses. There was more disclosure of financial information, at 1.7% of Canadian IP addresses and 4.7% of US IP addresses. An analysis of search terms used in these file sharing networks showed that a small percentage of the terms would return PHI and PFI files (ie, there are people successfully searching for PFI and PHI on the peer-to-peer file sharing networks). CONCLUSION: There is a real risk of inadvertent disclosure of PHI through peer-to-peer file sharing networks, although the risk is not as large as for PFI. Anyone keeping PHI on their computers should avoid installing file sharing applications on their computers, or if they have to use such tools, actively manage the risks of inadvertent disclosure of their, their family's, their clients', or patients' PHI.

The use of electronic data capture tools in clinical trials: Web-survey of 259 Canadian trials.

BACKGROUND: Electronic data capture (EDC) tools provide automated support for data collection, reporting, query resolution, randomization, and validation, among other features, for clinical trials. There is a trend toward greater adoption of EDC tools in clinical trials, but there is also uncertainty about how many trials are actually using this technology in practice. A systematic review of EDC adoption surveys conducted up to 2007 concluded that only 20% of trials are using EDC systems, but previous surveys had weaknesses. OBJECTIVES: Our primary objective was to estimate the proportion of phase II/III/IV Canadian clinical trials that used an EDC system in 2006 and 2007. The secondary objectives were to investigate the factors that can have an impact on adoption and to develop a scale to assess the extent of sophistication of EDC systems. METHODS: We conducted a Web survey to estimate the proportion of trials that were using an EDC system. The survey was sent to the Canadian site coordinators for 331 trials. We also developed and validated a scale using Guttman scaling to assess the extent of sophistication of EDC systems. Trials using EDC were compared by the level of sophistication of their systems. RESULTS: We had a 78.2% response rate (259/331) for the survey. It is estimated that 41% (95% CI 37.5%-44%) of clinical trials were using an EDC system. Trials funded by academic institutions, government, and foundations were less likely to use an EDC system compared to those sponsored by industry. Also, larger trials tended to be more likely to adopt EDC. The EDC sophistication scale had six levels and a coefficient of reproducibility of 0.901 (P< .001) and a coefficient of scalability of 0.79. There was no difference in sophistication based on the funding source, but pediatric trials were likely to use a more sophisticated EDC system. CONCLUSION: The adoption of EDC systems in clinical trials in Canada is higher than the literature indicated: a large proportion of clinical trials in Canada use some form of automated data capture system. To inform future adoption, research should gather stronger evidence on the costs and benefits of using different EDC systems.

A survey of quality assurance practices in biomedical open source software projects.

BACKGROUND: Open source (OS) software is continuously gaining recognition and use in the biomedical domain, for example, in health informatics and bioinformatics. OBJECTIVES: Given the mission critical nature of applications in this domain and their potential impact on patient safety, it is important to understand to what degree and how effectively biomedical OS developers perform standard quality assurance (QA) activities such as peer reviews and testing. This would allow the users of biomedical OS software to better understand the quality risks, if any, and the developers to identify process improvement opportunities to produce higher quality software. METHODS: A survey of developers working on biomedical OS projects was conducted to examine the QA activities that are performed. We took a descriptive approach to summarize the implementation of QA activities and then examined some of the factors that may be related to the implementation of such practices. RESULTS: Our descriptive results show that 63% (95% CI, 54-72) of projects did not include peer reviews in their development process, while 82% (95% CI, 75-89) did include testing. Approximately 74% (95% CI, 67-81) of developers did not have a background in computing, 80% (95% CI, 74-87) were paid for their contributions to the project, and 52% (95% CI, 43-60) had PhDs. A multivariate logistic regression model to predict the implementation of peer reviews was not significant (likelihood ratio test = 16.86, 9 df, P = .051) and neither was a model to predict the implementation of testing (likelihood ratio test = 3.34, 9 df, P = .95). CONCLUSIONS: Less attention is paid to peer review than testing. However, the former is a complementary, and necessary, QA practice rather than an alternative. Therefore, one can argue that there are quality risks, at least at this point in time, in transitioning biomedical OS software into any critical settings that may have operational, financial, or safety implications. Developers of biomedical OS applications should invest more effort in implementing systemic peer review practices throughout the development and maintenance processes.