Depression is a mental disorder triggered by the interaction of social, psychological and biological factors that have an important impact on an individual’s life. Despite being a well-studied disease with several established forms of treatment, its prevalence is increasing, especially among older adults. New forms of treatment and prevention are encouraged, and some researchers have been discussing the effects of vitamin D (VitD) on depression; however, the exact mechanism by which VitD exerts its effects is not yet conclusive. In this study, we aimed to discuss the possible mechanisms underlying the association between VitD and depression in older adults. Therefore, we conducted a systematic search of databases for indexed articles published until 30 April 2021. The primary focus was on both observational studies documenting the association between VitD and depression/depressive symptoms, and clinical trials documenting the effects of VitD supplementation on depression/depressive symptoms, especially in older adults. Based on pre-clinical, clinical and observational studies, it is suggested that the maintenance of adequate VitD concentrations is an important issue, especially in older adults, which are a risk population for both VitD deficiency and depression. Nevertheless, it is necessary to carry out more studies using longitudinal approaches in low- and middle-income countries to develop a strong source of evidence to formulate guidelines and interventions.

Numerous animal models and epidemiological and observational studies have demonstrated that enterovirus (EV) infection could be involved in the development of clinical type 1 diabetes mellitus (T1DM), but its aetiology is not fully understood. Therefore, we reviewed the association between EV infection and clinical T1DM. We searched PubMed and Embase from inception to April 2021 and reference lists of included studies without any language restrictions in only human studies. The correlation between EV infection and clinical T1DM was calculated as the pooled odds ratio (OR) and 95% confidence intervals (CIs), analysed using random-effects models. Subgroup and sensitivity analyses were performed to evaluate the robustness of the associations. A total of 25 articles (22 case–control studies and three nested case–control studies) met the inclusion criterion including 4854 participants (2948 cases and 1906 controls) with a high level of statistical heterogeneity (I2 = 80%, P < 0.001) mainly attributable to methods of EV detection, study type, age distribution, source of EV sample and control subjects. Meta-analysis showed a significant association between EV infection and clinical T1DM (OR 5.75, 95% CI 3.61–9.61). There is a clinically significant association between clinical T1DM and EV infection.

The democratic peace—the idea that democracies rarely fight one another—has been called “the closest thing we have to an empirical law in the study of international relations.” Yet, some contend that this relationship is spurious and suggest alternative explanations. Unfortunately, in the absence of randomized experiments, we can never rule out the possible existence of such confounding biases. Rather than commonly used regression-based approaches, we apply a nonparametric sensitivity analysis. We show that overturning the negative association between democracy and conflict would require a confounder that is forty-seven times more prevalent in democratic dyads than in other dyads. To put this number in context, the relationship between democracy and peace is at least five times as robust as that between smoking and lung cancer. To explain away the democratic peace, therefore, scholars would have to find far more powerful confounders than those already identified in the literature.

This article offers a description and discussion of “shadowing” as a data collection and analytic tool, highlighting potential research opportunities related to the direct observation of individuals—principally political elites—in their normal daily routine for an extended period of time, often between one day and one week. In contrast with large-scale data collection methods, including surveys, shadowing enables researchers to develop detailed observations of political behavior that are not limited by the availability of administrative data or the constraints of a questionnaire or an interview guide. Unlike more in-depth qualitative methods, such as ethnography, shadowing is scalable in a manner that allows for larger sample sizes and the potential for medium-N inference. I provide a detailed account of how to design and conduct a shadowing study, including sampling strategies, techniques for coding shadowing data, and processes for drawing inferences about the behavior of shadowed subjects, drawing on examples from a completed shadowing-based study. I also discuss ways to mitigate selection and observer biases, presenting results that suggest these can be no more pronounced when shadowing political elites than in other forms of observational research.

The Paleolithic diet (PaleoDiet) is an allegedly healthy dietary pattern inspired by the consumption of wild foods and animals assumed to be consumed in the Paleolithic era. Despite gaining popularity in the media, different operational definitions of this Paleolithic nutritional intake have been used in research. Our hypothesis is that specific components used to define the PaleoDiet may modulate the association of this diet with several health outcomes. We comprehensively reviewed currently applied PaleoDiet scores and suggested a new score based on the food composition of current PaleoDiet definitions and the theoretical food content of a staple dietary pattern in the Paleolithic age. In a PubMed search up to December 2019, fourteen different PaleoDiet definitions were found. We observed some common components of the PaleoDiet among these definitions although we also found high heterogeneity in the list of specific foods that should be encouraged or banned within the PaleoDiet. Most studies suggest that the PaleoDiet may have beneficial effects in the prevention of cardiometabolic diseases (type 2 diabetes, overweight/obesity, CVD and hyperlipidaemias) but the level of evidence is still weak because of the limited number of studies with a large sample size, hard outcomes instead of surrogate outcomes and long-term follow-up. Finally, we propose a new PaleoDiet score composed of eleven food items, based on a high consumption of fruits, nuts, vegetables, fish, eggs and unprocessed meats (lean meats); and a minimum content of dairy products, grains and cereals, and legumes and practical absence of processed (or ultra-processed) foods or culinary ingredients.

Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into (1) the choice of text representation and (2) the choice of distance metric. We investigate how different choices within this framework affect both the quantity and quality of matches identified through a systematic multifactor evaluation experiment using human subjects. Altogether, we evaluate over 100 unique text-matching methods along with 5 comparison methods taken from the literature. Our experimental results identify methods that generate matches with higher subjective match quality than current state-of-the-art techniques. We enhance the precision of these results by developing a predictive model to estimate the match quality of pairs of text documents as a function of our various distance scores. This model, which we find successfully mimics human judgment, also allows for approximate and unsupervised evaluation of new procedures in our context. We then employ the identified best method to illustrate the utility of text matching in two applications. First, we engage with a substantive debate in the study of media bias by using text matching to control for topic selection when comparing news articles from thirteen news sources. We then show how conditioning on text data leads to more precise causal inferences in an observational study examining the effects of a medical intervention.

We investigated how initial conflicts in adolescent romantic relationships escalate into serious forms of conflict, including intimate partner violence (IPV). We focused on whether adolescents’ micro-level interaction patterns, i.e., coercion and positive engagement, mediated between conflict and future IPV. The sample consisted of 91 heterosexual couples, aged 13 to 18 years (M = 16.5, SD = 0.99) from a diverse background (42% Hispanic/Latino, 42% White). Participants completed surveys about conflict at Time 1, and they participated in videotaped conflict and jealousy discussions. At Time 2, participants completed surveys about conflict and IPV, and an average daily conflict score was calculated from ecological momentary assessments. Multilevel hazard models revealed that we did not find support for dyadic coercion as a risk process leading to escalations in conflict. However, a higher likelihood of ending dyadic positive behaviors mediated between earlier levels of conflict and a latent construct of female conflict and IPV. Classic coercive dynamics may not apply to adolescent romantic relationships. Instead, not being able to reinforce levels of positivity during conflict predicted conflict and IPV as reported by females. The implications of these findings for understanding coercion in the escalation from conflict to IPV in adolescent romantic relationships are discussed.