Recent cross‐national comparative studies have found no effect of countries’ macroeconomic performances on trust in national political institutions, once political explanations (most notably corruption) are taken into account. Although political trust is not determined by the comparison of national economic performance to other countries, it is argued in this article that it is affected by comparisons to their own past performance. In a multilevel, fixed effects analysis of Eurobarometer data (21 waves in 15 European Union Member States between 1999 and 2011) the extent to which within‐country variations in economic performance affect political trust longitudinally is tested. Three major conclusions are reached. First, within‐country, longitudinal changes in performance (growth, deficits, unemployment and inflation) affect political trust. Second, the impact of macroeconomic performance is stronger among the lower educated. Third, even in times of economic duress, budgetary deficits tend to undermine political trust.

Attitudes towards social spending and the welfare state have been characterised by one of the longest standing and widest gender gaps. Past research suggests that parenthood deepens this divide further. Yet, the exact relationship between parenthood and support for social policies – and the gendered nature of this process – has been difficult to establish because it can vary across welfare policy areas and the age of the children, which past studies, relying on cross‐sectional data, has found difficult to unravel. Using panel data from the Swiss Household Panel, we examine individual level changes in fathers’ and mothers’ views towards specific welfare state policies. We find that individuals’ support for social spending fluctuates at different stages of parenthood, and that mothers’ demands differ from fathers’ in relation to care related but not in terms of educational spending. This implies that parents are not a homogeneous group that parties could target with uniform electoral pledges. As a result, building widespread electoral support for expanding a broad range of social investment policies is likely to be challenging in a context where, first and foremost, self‐interest appears to drive (or depress) individuals’ support for specific welfare state policies.

The European Union (EU) has laboured hard to gain the right to make oral interventions in the United Nations General Assembly (UNGA) in pursuit of a more active international role. At the same time, though, EU member-states continue to take the floor to make their own interventions, thus differentiating – but not necessarily distancing – their stance from the officially expressed EU position. In that respect, it is important to examine the drivers behind the differentiating activity of EU member-states and their engagement in UNGA deliberations. We identify structural, institutional, political and thematic drivers. They relate to resources, the EU system of external representation in the form of the EU rotating Council Presidency and the opportunities that it provides during each country's period in office, national political aspirations for greater influence, as well as issue-specific assertiveness. We operationalize and control for these drivers by looking at the size and economic resources of EU member-states, their individual statements while holding the EU rotating Council Presidency, their membership in the UN Security Council (UNSC) or candidacy for it, and the issue specificity of each UNGA Main Committee. Our analysis is based on a three-level longitudinal multilevel random intercept model and relies upon a new dataset that compiles the oral interventions made by representatives of EU member-states and by EU officials in UNGA through an automated content analysis of the verbatim records of the UNGA meetings from 1998 to 2017.

Family planning programmes in sub-Saharan Africa (SSA) often disseminate the proposition that birth spacing improves child survival. Yet, there are few examinations of this hypothesis that benefit from longitudinal data. This paper addresses this gap using 15 years of prospective data from three rural districts of Tanzania. The effect of birth interval durations on the risk of childhood mortality was estimated by fitting Weibull parametric hazard regression models with shared frailties to a dataset that comprised records of reproductive events and their succeeding survival trajectories of 25,762 mother-child dyads that lived in the sentinel areas of the Ifakara and Rufiji Health and Demographic Surveillance Systems from 2000 to 2015. The analysis was motivated by two hypotheses: First, that relatively short subsequent and preceding birth intervals would be associated with heightened risks of child mortality; however, that the effects of short subsequent birth intervals would be most pronounced among children between 12 and 59 months of age; and second, that the effects of short preceding birth intervals would be most acute during the neonatal and post-neonatal period. Results, which were adjusted for confounder effects at the individual, household, and contextual levels, demonstrated significant associations between subsequent and preceding birth interval durations and childhood mortality risk. Regarding subsequent birth intervals, relative to birth spacing of less than 18 months, durations 24–35 and ≥36 months were associated with 1–5-year-old mortality risks that were 0.29 and 0.21 times lower. Relative to preceding birth intervals of less than 18 months, those of 24–35 months were associated with a neonatal mortality risk that was 0.48 lower. Compared to the same referent group, preceding birth intervals of 18–23, 24–35, and ≥36 months were significantly associated with 12–23-month-old mortality risks that were 0.20, 0.39, and 0.33 times lower. The findings are compared with those from similar studies held in SSA, and the potential for family planning programmes to contribute to improved child survival in settings, such as Tanzania, is discussed.

In modeling change over time, developmental theories often emphasize meaningful quantities like peaks, inflections, timing, and tempo. However, longitudinal analyses typically rely on simple polynomial models that estimate powered terms of time in a linear, additive form which are disconnected from these meaningful quantities. While these linear parameterizations are computationally efficient and produce stable results, the quantities estimated in these models are difficult to directly connect to theoretical hypotheses. To address this disconnect between estimation and theory development, I propose several approaches for linear estimation with nonlinear inference (LENI), a framework that transforms results from stable, easily-estimated linear models into nonlinear estimates which align with theoretical quantities of interest through a set of principled transformation functions. I first outline derivations for the interpretable nonlinear parameters, and transform the results of the corresponding linear model—including fixed and random effects as well as conditional covariates effects —into the results we would have obtained by fitting a nonlinear version of the model. I conclude by summarizing a linearized structural equation model approach which can flexibly accommodate any known nonlinear target function within a linearly-estimable framework. I conclude with recommendations for applied researchers and directions for fruitful future work in this area.

The study of correlates of change is the investigation of systematic individual differences in growth. Our representation of systematic individual differences in growth is built up in two parts: (a) a model for individual growth and, (b) a model for the dependence of parameters in the individual growth models on individual characteristics. First, explicit representations of correlates of change are constructed for various models of individual growth. Second, for the special case of initial status as a correlate of change, properties of collections of growth curves provide new results on the relation between change and initial status. Third, the shortcomings of previous approaches to the assessment of correlates of change are demonstrated. In particular, correlations of residual change measures with exogenous individual characteristics are shown to be poor indicators of systematic individual differences in growth.

As a method for representing development, latent trait theory is presented in terms of a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth. Maximum likelihood parameter estimates and associated asymptotic tests follow directly. These procedures may be viewed as an alternative to standard repeated measures ANOVA and to first-order auto-regressive methods. As formulated, the model encompasses cohort sequential designs and allow for period or practice effects. A numerical illustration using data initially collected by Nesselroade and Baltes is presented.

Neglect remains understudied compared to other forms of maltreatment. While studies have shown that neglect has negative effects on mental health in adolescence, yet unresolved is whether these impacts result from critical period or cumulative effects. In the present article, we use a novel approach to compare these two hypotheses from the impact of two types of neglect, failure to provide (FTP) and lack of supervision (LOS), on adolescent depression and internalizing symptoms. Data derive from the LONGSCAN consortium, a diverse, multi-site, prospective study of children from approximately age 2–16. Despite our hypothesis that the critical period of early childhood would have the greatest impact on adolescent internalizing mental health, exposure to neglect during the critical period of adolescence (ages 12–16) was the best-fitting model for the effects of FTP neglect on depression, and the effects of LOS neglect on both depression and internalizing symptoms. The cumulative model (exposure across all time periods) best explained the effects of FTP neglect on internalizing symptoms. Results were robust to the addition of control variables, including other forms of maltreatment. These findings demonstrate that responding to neglect into adolescence must be considered as urgent for child welfare systems.

Childhood adversities have been linked to psychosocial outcomes, but it remains uncertain whether subtypes of adversity exert different effects on outcomes. Research is also needed to explore the dynamic interplay between adversity and psychosocial outcomes from childhood to mid-adolescence. This study aimed to investigate these relationships and their role in shaping adolescent wellbeing. Data were extracted from three timepoints of the UK Household Longitudinal Survey when participants (n = 646) were aged 10–15. Cross-lagged panel models were used to explore the relationship between cumulative adversities, and separately non-household (i.e., bullying victimization and adverse neighborhood) and household (i.e., sibling victimization, quarrelsome relationship with parents, financial struggles, and maternal psychological distress) adversities, and psychosocial outcomes (i.e., internalizing and externalizing problems, delinquency, and life satisfaction). Our results revealed that heightened cumulative adversity predicted psychosocial outcomes from childhood to mid-adolescence. Increased levels of household adversity predicted psychosocial outcomes throughout early to mid-adolescence, while non-household adversity only predicted psychosocial outcomes in early adolescence. Furthermore, worse psychosocial outcomes predicted higher levels of adversities during adolescence, highlighting bidirectionality between adversity and psychosocial outcomes. These findings underscore the varying impacts of adversity subtypes and the mutually reinforcing effects of adversities and psychosocial functioning from childhood to mid-adolescence.

Negative symptoms remain poorly understood and treated despite their huge impact on patients’ lives and clinical outcomes. This is partly because of ongoing debates about the clinical constructs underlying negative symptoms. A longitudinal analysis of the structure of negative symptoms presented in BJPsych Open reports striking temporal stability of symptom structure, which behaves as a few independent domains. This further underscores the need to address specific symptom domains when considering interventions or pathophysiology studies.

In studies that contain repeated measures of variables, longitudinal analysis accounting for time-varying covariates is one of the options. We aimed to explore longitudinal association between diet quality (DQ) and non-communicable diseases (NCDs). Participants from the 1973–1978 cohort of the Australian Longitudinal Study on Women’s Health (ALSWH) were included, if they; responded to survey 3 (S3, 2003, aged 25–30 years) and at least one survey between survey 4 (S4, 2006) and survey 8 (S8, 2018), were free of NCDs at or before S3, and provided dietary data at S3 or S5. Outcomes were coronary heart disease (CHD), hypertension (HT), asthma, cancer (except skin cancer), diabetes mellitus (DM), depression and/or anxiety, and multimorbidity (MM). Longitudinal modelling using generalised estimation equation (GEE) approach with time-invariant (S4), time-varying (S4–S8) and lagged (S3–S7) covariates were performed. The mean (± standard deviation) of Alternative Healthy Eating Index-2010 (AHEI-2010) of participants (n = 8022) was 51·6 ± 11·0 (range: 19–91). Compared to women with the lowest DQ (AHEI-2010 quintile 1), those in quintile 5 had reduced odds of NCDs in time-invariant model (asthma: OR (95 % CI): 0·77 (0·62–0·96), time-varying model (HT: 0·71 (0·50–0·99); asthma: 0·62 (0·51–0·76); and MM: 0·75 (0·58–0·97) and lagged model (HT: 0·67 (0·49–0·91); and asthma: 0·70 (0·57–0·85). Temporal associations between diet and some NCDs were more prominent in lagged GEE analyses. Evidence of diet as NCD prevention in women aged 25–45 years is evolving, and more studies that consider different longitudinal analyses are needed.