Handling Covariates in Markovian Models with a Mixture Transition Distribution Based Approach

This paper presents and discusses the use of a Mixture Transition Distribution-like model (MTD) to account for covariates in Markovian models. The MTD was introduced in 1985 by Raftery as an approximation of higher order Markov chains. In the MTD, each lag is estimated separately using an additive model, which introduces a kind of symmetrical relationship between the past and the present. Here, using an MTD-based approach, we consider each covariate separately, and we combine the effects of the lags and of the covariates by means of a mixture model. This approach has three main advantages. First, no modification of the estimation procedure is needed. Second, it is parsimonious in terms of freely estimated parameters. Third, the weight parameters of the mixture can be used as an indication of the relevance of the covariate in explaining the time dependence between states. An illustrative example taken from life course studies using a 3-state hidden Markov model and a covariate with three levels shows how to interpret the results of such models

Child disability as a family issue: a study on mothers' and fathers' health in Italy

Background Disability does not simply affect the health status of the individual who directly experiences that condition, but it has important consequences on the health and well-being of the other family members as well. Focusing on Italy, an extremely interesting test-bed due to its strong familialist welfare regime, we show significant spillover effects of children's disability on parental health and well-being.Methods We use data from a nationally representative household survey on almost 13 000 mothers and fathers and adopt a multivariate regression setting providing evidence that the disability of a child is negatively associated with parents' health and life satisfaction.Results Parents of a disabled child report lower levels of general and mental health, as well as lower levels of well-being compared with parents with a healthy child. Strong heterogeneity by gender and socio-economic characteristics is observed, with mothers being more affected by the disability status of the child than fathers. The estimated coefficients suggest that education remains an important protective factor even for parents of a disabled child.Conclusion This study claims and documents that child disability is an overlooked source of health disadvantage for parents. Such disadvantage is especially relevant for mothers and lower-educated parents, evidence that suggests the importance of taking an intersectional approach to study health disparities

Constraints and Explanation

For the past 40 years, causal-mechanical approaches to explanation in science have been the received view. In this paper, I will argue that causal-mechanical approaches to explanation are not the whole story; there is a notable class of explanations that I call constraining explanation. Constraining explanation do not work by describing some causal structure; rather they work by highlighting mathematical constraints on what kinds of structure there can be. Constraining explanations are different that causal explanations because they give a kind of modal knowledge that causal-mechanical explanation alone cannot give

The link between previous life trajectories and a later life outcome: A feature selection approach

Several studies have investigated the link between a previous trajectory and a given later-life outcome. Trajectories are complex objects. Identifying which aspects of the trajectories are relevant is of primary interest in terms both of prediction and testing specific theories. In this work, we propose an innovative approach based on data mining feature selection algorithms. The approach is in two steps. We start by automatically extracting several properties of the sequences. Using a life course approach, we focus here on features related to three key aspects of the life course: sequencing, timing and duration of life events. Then, in a second step, we use feature selection algorithms to identify the most relevant properties associated with the outcome. We discuss the use of two features selection approaches a random forest approach (Boruta) and a LASSO method (Stability Selection). We also discuss the inclusion of control variable such as socio-demographic characteristics of the respondent in this selection process. The proposed approach is illustrated through a study of the effects of family and work trajectories between age 20 and 40 on health and income conditions in midlife

Psychotropics prescription in Primary Care

Background: The study aimed to measure the prevalence of psychotropic prescriptions in primary care at four UTRGV clinical sites. Based on the results, this paper serves to inform and educate primary care providers of their current practices and treatment options when diagnosing mental health disorders and prescribing psychotropics. Methods: This study used EMR data from January to March 2021 in four primary care clinics affiliated with the UT Health RGV system. Primary care patients from January to March 2021 and who were diagnosed with mood disorders (including Depression, Anxiety, and Bipolar Disorder) were included in this quality improvement study. Results: A total of 1,436 patients were seen between January and March 2021 and of those, there were 361 patients who received a mood disorder diagnosis (25% prevalence). Of all the patients diagnosed with a mood disorder, 97% presented with one mental health diagnosis and 3% with two mood disorders. Regarding comorbidity, 70% of patients diagnosed with a mood disorder were also seeking care for chronic and acute health conditions. Regarding the types of mood disorders diagnosed, 54% of mood disorder diagnoses were anxiety related, 40% were depression-related diagnoses, and 3% were Bipolar Disorder. Of all patients diagnosed with a mood disorder 17% were prescribed medication for mental health symptoms. Conclusions: In conclusion, this study found that 1 in 4 patients that visit primary care at UTRGV clinics are diagnosed with a mental health disorder. Of these, 17% received a psychotropic prescription, indicating many patients without medications may first benefit from brief behavioral health interventions before psychopharmacological treatment. The current study highlights how integrated care meets the behavioral health care needs for most patients with mood disorders seeking primary care

Intentions and Childbearing in a Cross-Domain Life Course Approach: The case of Australia

In Australia and other affluent societies people tend to report a number of desired children which is clearly higher than the number of children they eventually bear. In the effort to explain such an inconsistency, demographers have studied the correlates of the link between pregnancy intentions and births. Drawing on data from the “Household Income and Labour Dynamics in Australia” (HILDA) survey, we situate, for the first time, intentions and events in a unified and multidimensional life course framework. We examine the intention-outcome fertility link across a plurality of life course domains and in a genuine couple approach. Education, work, and residence are selected as domains closely related to the family formation process. Results show that pregnancy intentions are often part of a multidimensional life course plan and that the cross-domain effects are gendered and parity specific. Moreover, cross-domain events have stronger influence than cross-domain intentions. A change of residence is directly correlated with a childbirth if it is the outcome of a previous plan and the couple has already made the transition to parenthood. Resumption of studies is inversely correlated with the birth of a child irrespective of whether the event was planned or not by either one of the partners. Finally, a change of job decreases the chance of having a first child but only if experienced by the female partner while it decreases the chance of an additional child only if previously planned or experienced by both partners. Such results confirm the relevance of work-family conflict as one of the drivers of low fertility and outline the usefulness of a holistic life course approach in the analysis of reproductive decision-making

Union Formation under Conditions of Uncertainty: The Objective and Subjective Sides of Employment Uncertainty

BACKGROUND The link between economic forces and family dynamics has received renewed attention in the present era of heightened uncertainty. Economic uncertainty has usually been linked to unfavorable labor market circumstances, such as unemployment and short-term contracts. Nonetheless, union formation may also be affected by subjective appraisals of employment conditions, including employment security and - acknowledging the prospective nature of uncertainty itself expectations of future employment. OBJECTIVE This study seeks to empirically disentangle the effects of the objective and subjective sides of individual employment uncertainty on the entry into union. METHODS We apply event history techniques to longitudinal data taken from the Household, Income and Labour Dynamics in Australia (HILDA) survey to examine whether and how objective measures of employment uncertainty (labor market status and contract type) and subjective measures (employment security and employment expectations) are associated with entry into a first union. RESULTS Our results show that objective markers of employment uncertainty - unemployment or temporary (casual) jobs - inhibit entry into a union for both men and women. Furthermore, different appraisals of employment uncertainty affect union formation across employment conditions. When individuals face objective employment uncertainty while still expecting their employment situation to improve, either by exiting unemployment (in particular among men) or retaining their jobs (among both sexes), union formation is not necessarily postponed. CONTRIBUTIO NWe stress the importance of considering how different future expectations influence family formation across different levels of objective uncertainty. The sole use of objective markers of employment uncertainty provides only a partial, and possibly inaccurate, perspective on union formation: the specter of the future also matters

When partners' disagreement prevents childbearing: a couple-level analysis in Australia

BACKGROUND Studies investigating the correspondence of birth intentions and birth outcomes focus mainly on women's and men's intentions separately and disregard the fact that reproductive decision-making is dyadic. OBJECTIVE We examine the intention-outcome link for fertility taking a genuine couple-level approach. We aim to understand whether a heterosexual couple's conflict is solved in favour or against childbirth and whether the male or the female partner prevails in the decision-making. METHODS Drawing on data from the survey Household, Income and Labour Dynamics in Australia (HILDA), we perform logistic regressions in which couples are the unit of analysis and the variables are computed by combining both partners' characteristics. RESULTS Results show that disagreement about having a first child is located between 'agreement on yes' and 'agreement on not,' with half of disagreeing couples having a child. By contrast, disagreement about having another child is shifted more towards `agreement on not' and most often prevents the birth of a child. Women prevail in the decision of having a first child, irrespective of gender equity within the couple, while a symmetric double-veto model is at work if the decision concerns a second or additional child. CONCLUSION Couple disagreement is not always sufficient to prevent the birth of a child in a low fertility country such as Australia, and the increasing level of gender equity within the couple does not necessarily imply increasing female decision-making power on childbearing issues. CONTRIBUTION The predictive power of fertility intentions is more accurate in models including both partners' views. Fertility-related policies should consider the dyadic nature of fertility decisions

A discussion on hidden Markov models for life course data

This is an introduction on discrete-time Hidden Markov models (HMM) for longitudinal data analysis in population and life course studies. In the Markovian perspective, life trajectories are considered as the result of a stochastic process in which the probability of occurrence of a particular state or event depends on the sequence of states observed so far. Markovian models are used to analyze the transition process between successive states. Starting from the traditional formulation of a first-order discrete-time Markov chain where each state is liked to the next one, we present the hidden Markov models where the current response is driven by a latent variable that follows a Markov process. The paper presents also a simple way of handling categorical covariates to capture the effect of external factors on the transition probabilities and existing software are briefly overviewed. Empirical illustrations using data on self reported health demonstrate the relevance of the different extensions for life course analysis