At the basis of many important research questions is causality – does X causally impact Y? For behavioural and psychiatric traits, answering such questions can be particularly challenging, as they are highly complex and multifactorial. ‘Triangulation’ refers to prospectively choosing, conducting and integrating several methods to investigate a specific causal question. If different methods, with different sources of bias, all indicate a causal effect, the finding is much less likely to be spurious. While triangulation can be a powerful approach, its interpretation differs across (sub)fields and there are no formal guidelines. Here, we aim to provide clarity and guidance around the process of triangulation for behavioural and psychiatric epidemiology, so that results of existing triangulation studies can be better interpreted, and new triangulation studies better designed.

We first introduce the concept of triangulation and how it is applied in epidemiological investigations of behavioural and psychiatric traits. Next, we put forth a systematic step-by-step guide, that can be used to design a triangulation study (accompanied by a worked example). Finally, we provide important general recommendations for future studies.

While the literature contains varying interpretations, triangulation generally refers to an investigation that assesses the robustness of a potential causal finding by explicitly combining different approaches. This may include multiple types of statistical methods, the same method applied in multiple samples, or multiple different measurements of the variable(s) of interest. In behavioural and psychiatric epidemiology, triangulation commonly includes prospective cohort studies, natural experiments and/or genetically informative designs (including the increasingly popular method of Mendelian randomization). The guide that we propose aids the planning and interpreting of triangulation by prompting crucial considerations. Broadly, its steps are as follows: determine your causal question, draw a directed acyclic graph, identify available resources and samples, identify suitable methodological approaches, further specify the causal question for each method, explicate the effects of potential biases and, pre-specify expected results. We illustrated the guide’s use by considering the question: ‘Does maternal tobacco smoking during pregnancy cause offspring depression?’.

In the current era of big data, and with increasing (public) availability of large-scale datasets, triangulation will become increasingly relevant in identifying robust risk factors for adverse mental health outcomes. Our hope is that this review and guide will provide clarity and direction, as well as stimulate more researchers to apply triangulation to causal questions around behavioural and psychiatric traits.

Depressive disorders are a major public health issue in Western societies, particularly among adolescents, young adults and women. The COVID-19 pandemic has exacerbated mental health challenges, increasing depression and anxiety symptoms, especially in younger people. This study focuses on the hard-hit Emilia-Romagna Region (ERR) in Italy, examining changes in antidepressant (AD) drug use post-COVID-19 to understand the pandemic’s effect on mental health.

A population-based interrupted time series design and a segmented regression analysis was carried out on ERR pharmaceutical data (FED, direct dispensation pharmaceuticals, AFT, territorial pharmaceutical assistance) out to estimate changes in AD use during the three pandemic years (2020, 2021 and 2022) compared to 2017–2019.Analyses were stratified by age, gender, citizenship, population density of the area of residence.

A notable increase in AD consumption compared to what was expected was observed among younger age groups, and especially in females. In the 12–19 age group, a gradual increase was recorded from January 2021 until it reached +48% in 2022 (+58% among women, +30% among men). An even more remarkable growth in AD usage among non-Italian residents in the same age group was recorded compared to expected. A relevant increase, although smaller, was detected among individuals in the 20–34 age group, with a peak of +9% in 2022. These differences persisted up until the end of the observation period.

The study suggests that the COVID-19 pandemic may have had a lasting negative impact on the mental health of younger individuals. The observed increase in AD use may foreshadow a potential long-term need for enhanced mental healthcare and services directed at this subpopulation.

The associations of prior homelessness with current health are unknown. Using nationally representative data collected in private households in England, this study aimed to examine Common Mental Disorders (CMDs), physical health, alcohol/substance dependence, and multimorbidities in people who formerly experienced homelessness compared to people who never experienced homelessness.

This cross-sectional study utilised data from the 2007 and 2014 Adult Psychiatric Morbidity Surveys. Former homelessness and current physical health problems were self-reported. Current CMDs, alcohol dependence and substance dependence were ascertained using structured validated scales. Survey-weighted logistic regression was used to compare multimorbidities (conditions in combination) for participants who formerly experienced homelessness with those who had never experienced homelessness, adjusting for sociodemographic characteristics, smoking status and adverse experiences. Population attributable fractions (PAFs) were calculated.

Of 13,859 people in the sample, 535 formerly experienced homelessness (3.6%, 95% CI 3.2–4.0). 44.8% of people who formerly experienced homelessness had CMDs (95% CI 40.2–49.5), compared to 15.0% (95% CI 14.3–15.7) for those who had never experienced homelessness. There were substantial associations between prior homelessness and physical multimorbidity (adjusted odds ratio [aOR] 1.98, 95% CI 1.53–2.57), CMD–physical multimorbidity (aOR 3.43, 95% CI 2.77–4.25), CMD–alcohol/substance multimorbidity (aOR 3.53, 95% CI 2.49–5.01) and trimorbidity (CMD–alcohol/substance–physical multimorbidity) (aOR 3.26, 95% CI 2.20–4.83), in models adjusting for sociodemographic characteristics and smoking. After further adjustment for adverse experiences, associations attenuated but persisted for physical multimorbidity (aOR 1.40, 95% CI 1.10–1.79) and CMD–physical multimorbidity (aOR 1.55, 95% CI 1.20–2.00). The largest PAFs were observed for CMD–alcohol/substance multimorbidity (17%) and trimorbidity (16%).

Even in people currently rehoused, marked inequities across multimorbidities remained evident, highlighting the need for longer-term integrated support for people who have previously experienced homelessness.

Psychotic-like experiences (PLEs), especially for persistent PLEs, are highly predictive of subsequent mental health problems. Hence, it is crucial to explore the psychopathological associations underlying the occurrence and persistence of PLEs. This study aimed to explore the above issues through a longitudinal dynamic network approach among PLEs and psychological and psychosocial factors.

A total of 3,358 college students completed two waves of online survey (from Oct 2021 to Oct 2022). Socio-demographic information was collected at baseline, and PLEs, depressive and anxiety symptoms, and adverse life events were assessed in both waves. Cross-lagged panel network analyses were used to establish networks among individuals with baseline PLEs as well as those without.

At baseline, 455(13.5%) students were screened positive for PLEs. Distinct dynamic network structures were revealed among participants with baseline PLEs and those without. While ‘psychomotor disturbance’ had the strongest connection with PLEs in participants with baseline PLEs, ‘suicide/self-harm’ was most associated with PLEs in those without. Among all three subtypes of PLEs, bizarre experiences and persecutory ideation were the most affected nodes by other constructs in participants with baseline PLEs and those without, respectively. Additionally, wide interconnections within the PLEs construct existed only among participants without baseline PLEs.

The study provides time-variant associations between PLEs and depressive symptoms, anxiety symptoms, and adverse life events using network structures. These findings help to reveal the crucial markers of the occurrence and persistence of PLEs, and shed high light on future intervention aimed to prevent and relieve PLEs.

The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the risk of self-harming behaviours warrants further investigation. Here, we hypothesized that people with a history of hospitalization for self-harm may be particularly at risk of readmission in case of SARS-CoV-2 hospitalization.

We conducted a retrospective analysis based on the French national hospitalization database. We identified all patients hospitalized for deliberate self-harm (10th edition of the International Classification of Diseases codes X60–X84) between March 2020 and March 2021. To study the effect of SARS-CoV-2 hospitalization on the risk of readmission for self-harm at 1-year of the inclusion, we performed a multivariable Fine and Gray model considering hospital death as a competing event.

A total of 61,782 individuals were hospitalized for self-harm. During the 1-year follow-up, 9,403 (15.22%) were readmitted for self-harm. Between inclusion and self-harm readmission or the end of follow-up, 1,214 (1.96% of the study cohort) were hospitalized with SARS-CoV-2 (mean age 60 years, 52.9% women) while 60,568 were not (mean age 45 years, 57% women). Multivariate models revealed that the factors independently associated with self-harm readmission were: hospitalization with SARS-CoV-2 (adjusted hazard ratio (aHR) = 3.04 [2.73–3.37]), psychiatric disorders (aHR = 1.61 [1.53–1.69]), self-harm history (aHR = 2.00 [1.88–2.04]), intensive care and age above 80.

In hospitalized people with a personal history of self-harm, infection with SARS-CoV-2 increased the risk of readmission of self-harm, with an effect that seemed to add to the effect of a history of mental disorders, with an equally significant magnitude. Infection may be a significantly stressful condition that precipitates self-harming acts in vulnerable individuals. Clinicians should pay particular attention to the emergence of suicidal ideation in these patients in the aftermath of SARS-CoV-2 infection.

Epidemiological studies show that despite the episodic nature, the long-term trajectory of depression can be variable. This study evaluated the heterogeneity of 10-year trajectory of major depressive disorder (MDD) related service utilization and associated clinical characteristics among US Veterans with a first diagnosis after 9/11.

Using a cohort design, electronic health record data for 293,265 Operation Enduring Freedom and Iraqi Freedom (OEF/OIF) Veterans were extracted to identify those with MDD between 2001 and 2021 with a full preceding year of clinical data and 10 years following the diagnosis. Latent class growth analysis compared clinical characteristics associated with four depression trajectories. Across all Veterans Affairs (VA)hospitals, 25,307 Veterans met our inclusion criteria. Demographic and clinical information from medical records was extracted and used as predictors of depression 10-year trajectories.

Among the study cohort (N = 25,307), 27.7% were characterized by brief contact, 41.7% were later re-entry, 17.6% were persistent contact and 12.9% were prolonged initial contact for depression related services. Compared to Veterans with trajectories showing brief contact, those with protracted treatment (persistent or prolonged initial contact) were more likely to be diagnosed with comorbid posttraumatic stress disorder (PTSD) and with MDD that was moderate to severe or recurrent.

Depression is associated with a range of treatment trajectories. The persistent and prolonged initial contact trajectories may have distinct characteristics and uniquely high resource utilization and disability income. We can anticipate that patients with comorbid PTSD may need longer-term care which has implications for brief models of care.

Most information about the association between childhood maltreatment (CM) and subsequent psychiatric morbidity is based on retrospective self-reports. Findings from longitudinal studies using prospective reports to statutory agencies may be subject to attrition. We therefore compared the prevalence to age 30 of inpatient psychiatric diagnoses in those who experienced agency-reported CM with those of the rest of the cohort using administrative data to minimise loss to follow-up.

We used linked administrative data for two birth cohorts of all individuals born in Queensland, Australia in 1983 and 1984 (N = 83,050) and followed to age 30 years. This was the entire cohort aside from 312 people who died. Information on CM came from statewide child protection data and psychiatric diagnoses from all public and private hospital admissions in Queensland.

On adjusted analyses, the 4,703 participants (5.7%) who had been notified to the statewide child protection authority had three to eight times the odds of being admitted for any of the following psychiatric diagnoses by age 30 years old: schizophrenia-spectrum disorders, bipolar affective disorders, depression, anxiety and post-traumatic stress disorders (PTSD). There were similar findings for all the CM subtypes. Associations were especially strong for PTSD with between a seven – and nine-fold increase in the odds of admission.

This is one of the largest studies of the long-term effects of CM, covering an entire jurisdiction. All types of maltreatment are significantly related to a range of psychiatric disorders requiring hospitalisation. Early identification, intervention and providing appropriate support to individuals who have experienced CM may help mitigate the long-term consequences and reduce the risk of subsequent mental health problems.