Privacy of study participants in open-access Health and Demographic Surveillance System data: A requirements analysis for data anonymisation (Preprint)

BACKGROUND Sharing and anonymising data have become hot topics for individuals, organisations, and countries around the world. Open-access sharing of anonymised data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. OBJECTIVE The goal of this paper is to highlight solutions and requirements for sharing longitudinal health and surveillance event history data in form of open-access data. The challenges lie in the anonymisation of multiple event dates and the time-varying variables. A sequential approach that adds noise to the event dates is proposed. This approach maintains the event order and preserves the average time between events. Additionally, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding dealing with the key variables that change over time such as educational level or occupation, we make two proposals, one based on limiting the intermediate status of a person (e.g. on education), and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga Health and Demographic Surveillance System (HDSS) core dataset, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 event records with time-varying, socio-economic variables and demographic information on individuals. The proposed anonymisation strategy lowers the risk of disclosure to acceptable levels thus allowing sharing of the data. METHODS statistical disclosure control, k-anonymity, adding noise, disclosure risk measurement, event history data anonymization, longitudinal data anonymization, data utility by visual comparisons. RESULTS Anonymized version of event history data including longitudinal information on individuals over time with high data utility. CONCLUSIONS The proposed anonymisation of study participants in event history data including static and time-varying status variables, specifically applied to longitudinal health and demographic surveillance system data, led to an anonymized data set with very low disclosure risk and high data utility ready to be shared to the public in form of an open-access data set. Different level of noise for event history dates were evaluated for disclosure risk and data utility. It turned out that high utility had been achieved even with the highest level of noise. Details matters to ensure consistency/credibility. Most important, the sequential noise approach presented in this paper maintains the event order. It has been shown that not even the event order is preserved but also the time between events is well maintained in comparison to the original data. We also proposed an anonymization strategy to handle the information of time-varying status of educational, occupational level of a person, year of death, year of birth, and number of events of a person. We proposed an approach that preserves the data utility well but limit the number of educational and occupational levels of a person. Using distance-based neighborhood matching we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers has full information on the original data. It could be shown that the disclosure risk is very low even by assuming that the attacker’s data base and information is optimal. The HDSS and medical science research communities in LMIC settings will be the primary beneficiaries of the results and methods presented in this science article, but the results will be useful for anyone working on anonymising longitudinal datasets possibly including also time-varying information and event history data for purposes of sharing. In other words, the proposed approaches can be applied to almost any event history data, and, additionally, to event history data including static and/or status variables that changes its entries in time.

Estimation of the Transition Probabilities in Multi-state Survival Data: New Developments and Practical Recommendations

Multi-state models can be successfully used for describing complicated event history data, for example, describing stages in the disease progression of a patient. In these models one important goal is the estimation of the transition probabilities since they allow for long term prediction of the process. Traditionally these quantities have been estimated by the Aalen-Johansen estimator which is consistent if the process is Markovian. Recently, estimators have been proposed that outperform the Aalen-Johansen estimators in non-Markov situations. This paper considers a new proposal for the estimation of the transition probabilities in a multi-state system that is not necessarily Markovian. The proposed product-limit nonparametric estimator is defined in the form of a counting process, counting the number of transitions between states and the risk sets for leaving each state with an inverse probability of censoring weighted form. Advantages and limitations of the different methods and some practical recommendations are presented. We also introduce a graphical local test for the Markov assumption. Several simulation studies were conducted under different data scenarios. The proposed methods are illustrated with a real data set on colon cancer.

Semiparametric analysis of complex longitudinal data

Event history data consist of the longitudinal records of event occurrence times. Recurrent event data and panel count data are two common types of event history data that occur in many areas, such as medical studies and social sciences. A great deal of literature has been established for their analyses. Nevertheless, only limited research exists on the variable selection for recurrent event data and panel count data. The existing methods can be seen as direct generalizations of the available penalized procedures for linear models, but may not perform as well as expected due to the complex structure of event history data. The first and second parts of this dissertation then discuss simultaneous parameter estimation and variable selection for event history data. We present a new variable selection method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed variable selection method has the clustering or grouping effect when covariates are highly correlated. Furthermore, the numerical studies are performed and indicate that the method works well for practical situations and can outperform the existing methods. Applications to real data are provided. Most of the existing studies of longitudinal data assume that covariates can be observed at the same observation times for the response variable, and the observation process is independent of the response variable completely or given covariates. In practice, the response variables and covariates are sometimes observed intermittently at different time points, leading to sparse asynchronous longitudinal data. The observation process may also be related to the response variable even given covariates and sometimes both issues can even occur at the same time. Although each of the two issues has been developed to address in literature, it does not seem to exist an established approach that can deal with both together. To address both issues simultaneously, the third part of this dissertation proposes a flexible semiparametric transformation conditional model and a kernel-weighted estimating equation based approach. The proposed estimators of regression parameters are shown to be consistent and asymptotically follow the normal distribution. For the assessment of the finite sample performance of the proposed method, an extensive simulation study is carried out and suggests that it performs well for practical situations. The approach is applied to a prospective HIV study that motivated this investigation.

Semiparametric analysis of correlated and interval-censored event-history data

We propose a semiparametric multi-state frailty model to analyze clustered event-history data subject to interval censoring. The proposed model is motivated by an attempt to study the life course of dental caries at the tooth level, taking into account the multiplicity of caries states and the intra-oral clustering of observations made at periodic time points. Of particular interest is the study of the intra-oral distribution of processes leading to carious lesions, and whether this distribution varies with gender. The model assumes, in view of the covariate profile, a proportionality of the transition intensities conditional on subject-level frailties, coupled with a linear spline approximation of the log baseline intensities. The model estimation is conducted using a penalized likelihood where the smoothing parameters are estimated as reciprocal variance components under a mixed-model representation. A Bayesian method is proposed to predict tooth-level caries transition probabilities, which can be used for tailoring tooth-level caries treatment and prevention plans. Intensive simulation studies indicate that the model fitting and prediction perform reasonably well under realistic sample sizes. The practical utility of the methods is illustrated using data from a longitudinal study on oral health among children from low-income families residing in the city of Detroit, Michigan.

Family Relations and Remarriage Postdivorce and Postwidowhood in China

Analyzing event history data from the 2010 China Family Panel Studies and 13 qualitative interviews, we examine the complex and gendered relationship between family relations and remarriage in China. Distinct roles are played by the presence of preschool, school-age, and adult children in configuring the remarriage of women and men after divorce and after widowhood. The remarriage of widows but not divorcées is positively associated with the presence of parents and siblings. Remarriage is more likely in the presence of large extended families. Whereas single and remarried divorcé(e)s equally provide care to their children, such care provision is less likely among remarried than single widow(er)s. Compared with their single counterparts, remarried divorcé(e)s and particularly widow(er)s are less likely to receive care from their children. We underline the importance of considering the “linked lives” of family members and comparing distinct life course circumstances in the study of remarriage. We demonstrate that remarriage is far from an “individualized” institution and that the state’s privatization of marriage seems to reinforce the “familialization” of remarriage practices in China.