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  • 1. Bijlsma, Maarten J.
    et al.
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics and Political Science, UK.
    Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula2020In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 183, no 2, p. 493-513Article in journal (Refereed)
    Abstract [en]

    Theories predict that the timing of childbearing and number of children born are determined by multiple socio-economic factors. Despite this, many methods cannot investigate the interrelationships between these determinants, including the direct and indirect influence that they have on fertility over the life course. Here we use the parametric g-formula to examine the interdependent influences of time-varying socio-economic processes-education, employment status and partnership status-on fertility. To demonstrate this approach, we study a cohort of women who were born in the UK in 1970. Our results show that socio-economic processes play an important role in determining fertility, not only directly but also indirectly. We show that increasing attendance in higher education has a largely direct effect on early childbearing up to age 25 years, resulting in a substantial increase in childlessness. However, childbearing at later ages is dominated by an indirect effect of education on fertility, via partnership status and employment status, that is twice as large as the direct effect. We also use the g-formula to examine bias due to unobserved heterogeneity, and we demonstrate that our results appear to be robust. We conclude that the method provides a valuable tool for mediation analysis in studies of interdependent life course processes.

  • 2. Bijlsma, Maarten J.
    et al.
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics and Political Science, United Kingdom.
    Tarkiainen, Lasse
    Myrskylä, Mikko
    Martikainen, Pekka
    Stockholm University, Faculty of Social Sciences, Centre for Health Equity Studies (CHESS). Max Planck Institute for Demographic Research, Germany; University of Helsinki, Finland.
    The Impact of Unemployment on Antidepressant Purchasing: Adjusting for Unobserved Time-constant Confounding in the g-Formula2019In: Epidemiology, ISSN 1044-3983, E-ISSN 1531-5487, Vol. 30, no 3, p. 388-395Article in journal (Refereed)
    Abstract [en]

    Background: The estimated effect of unemployment on depression may be biased by time-varying, intermediate, and time-constant confounding. One of the few methods that can account for these sources of bias is the parametric g-formula, but until now this method has required that all relevant confounders be measured.

    Methods: We combine the g-formula with methods to adjust for unmeasured time-constant confounding. We use this method to estimate how antidepressant purchasing is affected by a hypothetical intervention that provides employment to the unemployed. The analyses are based on an 11% random sample of the Finnish population who were 30–35 years of age in 1995 (n = 49,753) and followed until 2012. We compare estimates that adjust for measured baseline confounders and time-varying socioeconomic covariates (confounders and mediators) with estimates that also include individual-level fixed-effect intercepts.

    Results: In the empirical data, around 10% of person-years are unemployed. Setting these person-years to employed, the g-formula without individual intercepts found a 5% (95% confidence interval [CI] = 2.5%, 7.4%) reduction in antidepressant purchasing at the population level. However, when also adjusting for individual intercepts, we find no association (−0.1%; 95% CI = −1.8%, 1.5%).

    Conclusions: The results indicate that the relationship between unemployment and antidepressants is confounded by residual time-constant confounding (selection). However, restrictions on the effective sample when using individual intercepts can compromise the validity of the results. Overall our approach highlights the potential importance of adjusting for unobserved time-constant confounding in epidemiologic studies and demonstrates one way that this can be done.

  • 3. Busetta, Annalisa
    et al.
    Cetorelli, Valeria
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics, UK.
    A Universal Health Care System? Unmet Need for Medical Care Among Regular and Irregular Immigrants in Italy2018In: Journal of Immigrant and Minority Health, ISSN 1557-1912, E-ISSN 1557-1920, Vol. 20, no 2, p. 416-421Article in journal (Refereed)
    Abstract [en]

    Italy has a universal health care system that covers, in principle, the whole resident population, irrespective of citizenship and legal status. This study calculates the prevalence of unmet need for medical care among Italian citizens, regular and irregular immigrants and estimates logistic regression models to assess whether differences by citizenship and legal status hold true once adjusting for potential confounders. The analysis is based on two Surveys on Income and Living Conditions of Italian households and households with foreigners. Controlling for various factors, the odds of experiencing unmet need for medical care are 27% higher for regular immigrants than for Italian citizens and 59% higher for irregular immigrants. The gaps by citizenship and legal status are even more striking among those with chronic illnesses. These results reveal the high vulnerability of immigrants in Italy and the need to develop more effective policies to achieve health care access for all residents.

  • 4. Busetta, Annalisa
    et al.
    Mendola, Daria
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics, UK.
    Cetorelli, Valeria
    Measuring vulnerability of asylum seekers and refugees in Italy2019In: Journal of ethnic and migration studies, ISSN 1369-183X, E-ISSN 1469-9451Article in journal (Refereed)
    Abstract [en]

    In recent years, a growing number of forced migrants have travelled to Italy across the Mediterranean Sea. While the number of new arrivals has diminished since 2017, the Italian reception system still struggles to process the high number of applications for international protection. A significant proportion of forced migrants who arrive in Italy end up living in informal settlements, such as occupied buildings, shacks, containers and tented camps. In this study, we assess the vulnerability of asylum seekers and refugees living in informal settlements in Italy, using data from the first nationally representative survey of this population. We compare a count measure of vulnerability with a new approach based on latent trait analysis, which accounts for measurement error and correlation between indicators. This analysis shows that forced migrants from Asia are more vulnerable than those from Africa, and that vulnerability is consistently lower in informal settlements in the regions of Lazio and Piedmont, and consistently higher in Apulia. However, other factors predicting vulnerability often change depending upon the way in which vulnerability is measured. Our findings have implications for the design of social protection and inclusion policies, as well as future research that measures vulnerability.

  • 5. Naci, Huseyin
    et al.
    Salcher-Konrad, Maximilian
    Mcguire, Alistair
    Berger, Felix
    Kuehne, Titus
    Goubergrits, Leonid
    Muthurangu, Vivek
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics and Political Science, UK.
    Kelm, Marcus
    Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease2019In: Npj Digital Medicine, ISSN 2398-6352, Vol. 2, article id 17Article in journal (Refereed)
    Abstract [en]

    Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, intervention) or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14-1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17-8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes.

  • 6.
    Wilson, Ben
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics and Political Science, United Kingdom.
    The Intergenerational Assimilation of Completed Fertility: Comparing the Convergence of Different Origin Groups2019In: The international migration review, ISSN 0197-9183, E-ISSN 1747-7379, Vol. 53, no 2, p. 429-457Article in journal (Refereed)
    Abstract [en]

    Recent studies have highlighted the need for new research on intergenerational assimilation and how it varies for different origin groups. This article responds by studying the intergenerational assimilation of completed fertility in the United Kingdom. The results provide evidence of assimilation for some origins, in particular for women from Ireland and Jamaica. Yet results also show evidence against assimilation for second-generation Pakistanis and Bangladeshis. The ability of the method used to distinguish between origin groups highlights the importance of a robust statistical approach that takes account of heterogeneity, an approach that can also be used to study outcomes other than fertility.

  • 7.
    Wilson, Ben
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Sociology. London School of Economics, UK.
    Kuha, Jouni
    Residential segregation and the fertility of immigrants and their descendants2018In: Population, Space and Place, ISSN 1544-8444, E-ISSN 1544-8452, Vol. 24, no 3, article id e2098Article in journal (Refereed)
    Abstract [en]

    Measures of community population composition, such as residential segregation, are important theoretical mechanisms that have the potential to explain differences in fertility between immigrants, their descendants, and destination natives. However, only a handful of studies explore these mechanisms, and most are limited by the fact that they carry out cross-sectional analysis. This study proposes a new approach, which focuses on community composition in childhood. It uses longitudinal census data and registered births in England and Wales to investigate the relationship between completed fertility and multiple measures of community composition, including residential segregation. The results show that the fertility of immigrants is closer to native fertility if they grow up in less segregated areas. This provides evidence in support of the childhood socialisation hypothesis. Furthermore, residential segregation explains some of the variation in completed fertility for second-generation women from Pakistan and Bangladesh, the only second-generation group to have significantly higher completed fertility than natives. This suggests one reason why the fertility of some South Asians in England and Wales may remain culturally entrenched. All of these findings are consistent for different measures of community composition. They are also easier to interpret than the results of previous research because exposure is measured before childbearing has commenced, therefore avoiding many issues relating to selection, simultaneity, and conditioning on the future.

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