A Web-based Event History Calendar Approach for Measuring Contraceptive Use Behavior

Event history calendars (EHCs) are frequently used in social measurement to capture important information about the time ordering of events in people’s lives and enable inference about the relationships of the events with other outcomes of interest. To date, EHCs have primarily been designed for face-to-face or telephone survey interviewing, and few calendar tools have been developed for more private, self-administered modes of data collection. Web surveys offer benefits in terms of both self-administration, which can reduce social desirability bias, and timeliness. We developed and tested a web application enabling the calendar-based measurement of contraceptive method use histories. These measures provide valuable information for researchers studying family planning and fertility behaviors. This study describes the development of the web application and presents a comparison of data collected from online panels using the application with data from a benchmark face-to-face survey collecting similar measures (the National Survey of Family Growth).

The Nexus of Physical and Psychological Pain: Consequences for Mortality and Implications for Medical Sociology

Although physical pain lies at the intersection of biology and social conditions, a sociology of pain is still in its infancy. We seek to show how physical and psychological pain are jointly parts of a common expression of despair, particularly in relation to mortality. Using the 2002–2014 National Health Interview Survey Linked Mortality Files (N = 228,098), we explore sociodemographic differences in the intersection of physical and psychological pain (referred to as the “pain–distress nexus”) and its relationship to mortality among adults ages 25 to 64. Results from regression and event history models reveal that differences are large for the combination of the two, pointing to an overlooked aspect of health disparities. The combination of both high distress and high pain is most prevalent and most strongly predictive of mortality among socioeconomically disadvantaged, non-Hispanic whites. These patterns have several implications that medical sociology is well positioned to address.

Survival in a Nazi Concentration Camp: The Spanish Prisoners of Mauthausen

Analysis of the extent to which higher social class (along with other demographic variables) was an advantage for Spanish prisoners at the Mauthausen concentration camp advances the study of the determinants of survival in contexts of indiscriminate violence. Use of Cox event-history models, based on detailed information collected by well-placed Spaniards at the camp, reveals that individuals from higher social classes who filled administrative positions at Mauthausen were prominent in support networks and had a good command of the German language were more likely to survive. The risk of death was highest among unskilled agricultural workers, followed by unskilled non-agricultural workers.

A Joint Model for Longitudinal and Time-to-event Data in Social and Life Course Research: Employment Status and Time to Retirement

Longitudinal studies including a time-to-event outcome in social research often use a form of event history analysis to analyse the influence of time-varying endogenous covariates on the time-to-event outcome. Many standard event history models however assume the covariates of interest to be exogenous and inclusion of an endogenous covariate may lead to bias. Although such bias can be dealt with by using joint models for longitudinal and time-to-event outcomes, these types of models are underused in social research. In order to fill this gap in the social science modelling toolkit, we introduce a novel Bayesian joint model in which a multinomial longitudinal outcome is modelled simultaneously with a time-to-event outcome. The methodological novelty of this model is that it concerns a correlated random effects association structure that includes a multinomial longitudinal outcome. We show the use of the joint model on Danish labour market data and compare the joint model to a standard event history model. The joint model has three advantages over a standard survival model. It decreases bias, allows us to explore the relation between exogenous covariates and the longitudinal outcome and can be flexibly extended with multiple time-to-event and longitudinal outcomes.

Post-Migration Fertility in Southern Europe: Romanian and Moroccan women in Italy and Spain

This paper seeks to analyse migrant women’s reproductive behaviour in two countries with the lowest fertility rates, namely, Italy and Spain. We assess differences in migrant fertility patterns according to country of origin by comparing the post-migration motherhood of Moroccan and Romanian women. We have used data from the “2007 National Immigrant Survey” (INE) and the ”2011-2012 Survey on Social Integration and Condition among Foreign Citizens” (ISTAT) to adopt an event-history approach to the factors that affect the birth of the first child after migration. Specifically, we focus on marital status upon arrival and on the number of previous children, controlling in turn for the women’s socioeconomic circumstances. The results show, firstly, that Moroccan women have a higher fertility rate than Romanians in both countries. Secondly, the risk of the first birth shortly after migration is higher among childless and married women, and this probability remain high even for women from Morocco with children. Thirdly a cross-country comparison reveals that the results related to childbearing patterns are similar.

Critical Summary and Concluding Remarks

This chapter provides a summary and a systematic synopsis of the theoretical approaches and the empirical results. It gives a comparative overview over the temporal and spatial pattern of the diffusion process and critically reflects the theoretical approaches and the applied methods. A basic insight of this comparative conclusion is that the macro-quantitative approach of network diffusion event history analysis has great benefits for global studies on social policy diffusion, but in-depth case studies still remain important for revealing the diffusion mechanisms. Future research should more systematically combine both perspectives.

The South African Families Database

Very little is known about what family life looked like for settlers in colonial South Africa during the 18th or 19th century, nor how events over these centuries might have affected demographic change. The primary reason for this lacuna is a shortage of adequate data. Historians and genealogists have, over the last century, worked to combine the rich administrative records that are available in the Cape Archives in Cape Town and beyond, into a single genealogical volume of all settlers living in the 18th, 19th and early 20th century. Until recently, this valuable resource was not in a format that would enable its use for the type of event-history analyses that have come to dominate the field of contemporary historical demography. This is now changing with the introduction of the South African Families database (SAF). SAF is one of very few databases known to document a full population of immigrants and their families over several generations. This article introduces provides a brief background to, and technical overview of, the construction of the SAF. It discusses both the merits and limitations of its use in longitudinal demographic studies and offers a look into the types of studies it can enable.

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.