Stress and anxiety during pregnancy and length of gestation: a federated study using data from five Canadian and European birth cohorts.

While its etiology is not fully elucidated, preterm birth represents a major public health concern as it is the leading cause of child mortality and morbidity. Stress is one of the most common perinatal conditions and may increase the risk of preterm birth. In this paper we aimed to investigate the association of maternal perceived stress and anxiety with length of gestation. We used harmonized data from five birth cohorts from Canada, France, and Norway. A total of 5297 pregnancies of singletons were included in the analysis of perceived stress and gestational duration, and 55,775 pregnancies for anxiety. Federated analyses were performed through the DataSHIELD platform using Cox regression models within intervals of gestational age. The models were fit for each cohort separately, and the cohort-specific results were combined using random effects study-level meta-analysis. Moderate and high levels of perceived stress during pregnancy were associated with a shorter length of gestation in the very/moderately preterm interval [moderate: hazard ratio (HR) 1.92 (95%CI 0.83, 4.48); high: 2.04 (95%CI 0.77, 5.37)], albeit not statistically significant. No association was found for the other intervals. Anxiety was associated with gestational duration in the very/moderately preterm interval [1.66 (95%CI 1.32, 2.08)], and in the early term interval [1.15 (95%CI 1.08, 1.23)]. Our findings suggest that perceived stress and anxiety are associated with an increased risk of earlier birth, but only in the earliest gestational ages. We also found an association in the early term period for anxiety, but the result was only driven by the largest cohort, which collected information the latest in pregnancy. This raised a potential issue of reverse causality as anxiety later in pregnancy could be due to concerns about early signs of a possible preterm birth.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

BACKGROUND: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. OBJECTIVE: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. METHODS: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. RESULTS: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. CONCLUSIONS: The study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

Paediatric-onset immune-mediated inflammatory disease is associated with an increased mortality risk-A nationwide study.

BACKGROUND: Paediatric-onset immune-mediated inflammatory diseases (pIMID) show more aggressive phenotypes than when diagnosed in adults. However, data on mortality are often extrapolated from adult studies. AIM: To estimate the effect of pIMID on mortality. METHODS: In a population-based cohort study using the nationwide Danish healthcare registers, we included all patients diagnosed with pIMID in Denmark from 1980 to 2018. PIMID were defined as ICD codes indicative of autoimmune hepatitis, primary sclerosing cholangitis, Crohn's disease, ulcerative colitis, juvenile idiopathic arthritis, lupus erythematosus, or vasculitis registered before age 18 years. All-cause mortality was the primary outcome; cause-specific mortality was the secondary outcome. We used Cox survival analysis to estimate hazard ratios (HR), and Aalen survival analysis to estimate rate differences. RESULTS: We included 11,581 individuals diagnosed with pIMID and 99,665 reference individuals, accounting for 1,371,994 person-years of follow-up. Median and interquartile (IQR) age at diagnosis was 12.6 (7.9-15.9) years. During follow-up, 152 patients with pIMID and 316 reference individuals died; adjusted HR (aHR) was 3.8 (95% confidence interval [CI] 3.1-4.7). This corresponded to 6.9 (95% CI: 5.3-8.5) additional deaths per 10,000 person-years. The strongest associations were found for gastrointestinal diseases (aHR 22.8; 95% CI 9.6-64.1), gastrointestinal cancers (aHR 19.2; 95% CI 5.0-74.2) and lymphoproliferative disorders (aHR 6.8; 95% CI 2.8-16.8). CONCLUSION: Patients diagnosed with pIMID have a fourfold higher risk of mortality when followed into early adulthood compared with reference individuals. This underlines the severe disease course of pIMID and highlights the need for multidisciplinary care.

Can social connections become stressful? Exploring the link between social media use and perceived stress in cross-sectional and longitudinal analyses of 25,053 adults.

BACKGROUND: Social media has become a dominant part of daily lives for many, but excessive use may lead to an experience of stress. Only relatively few studies have investigated social media's influence on mental health. AIMS: We aimed to investigate whether social media use is associated with perceived stress and changes in perceived stress over 18 months. METHODS: The study population consisted of 25,053 adults (mean age 42.8; 62% women) from the SmartSleep Study. Self-reported frequency of social media use, of 10 specific social media platforms, and of perceived stress (the Perceived Stress Scale 4 item) was obtained at baseline and 18-months follow-up (N = 1745). The associations were evaluated at baseline and follow-up using multiple linear regression models adjusted for potential confounders. RESULTS: Compared to non-use, high social media use (at least every second hour) was associated with a slightly higher perceived stress level at baseline. No association was found between the frequency of social media use and changes in perceived stress during follow-up. Only small differences in these associations were noted across social media platforms. CONCLUSIONS: Further studies are needed to comprehensively explore the relationship between excessive social media use and mental health, recognizing different characteristics across social media platforms.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers.

Nighttime smartphone use is associated with sleep problems, which in turn have a bidirectional association with overweight. We aim to investigate whether nighttime smartphone use and sleep are related to overweight and metabolic dysfunction in adult populations. We used data from three population samples (aged 16-89) from the SmartSleep Study, which included survey data (N = 29,838), high-resolution tracking data (N = 3446), follow-up data (N = 1768), and cardiometabolic risk markers (N = 242). Frequent self-reported nighttime smartphone use was associated with 51% higher odds (95% CI: 1.32; 1.70) of overweight compared with no use. Tracked nighttime smartphone use was also associated with overweight. Similar results were found for obesity as an outcome. No consistent associations were found between nighttime smartphone use and cardiometabolic risk markers in a small subsample of healthy young women. Poor sleep quality (vs. good sleep quality) was associated with overweight (OR = 1.19, 85% CI: 1.10; 1.28). Overall, frequent nighttime smartphone use was consistently associated with overweight and a higher BMI across diverse population samples. The bidirectional interplay between nighttime smartphone use, sleep, and overweight may create a vicious circle of metabolic dysfunction over time. Therefore, nighttime smartphone use may be a potential target point for public health interventions to reduce overweight at the population level.

Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study.

STUDY OBJECTIVES: Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization. METHODS: 4,520 individuals (age 35.6 ± 9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n=3,221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e., the number of weeks in which at least one service from the patient's general practitioner was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics. RESULTS: 319 individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95CI: 1.9,4.1] fold higher odds of reporting poor health and with an average of 1.4 [95CI: 0.7,2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization. CONCLUSION: Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

Application of life course trajectory methods to public health data: A comparison of sequence analysis and group-based multi-trajectory modeling for modelling childhood adversity trajectories.

There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and benefits and drawbacks of both methods.

Cohort profile: The SmartSleep Study, Denmark, combining evidence from survey, clinical and tracking data.

PURPOSE: The SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allows for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment. PARTICIPANTS: The SmartSleep Study consists of three interconnected data samples, which combined include 30 673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5927) collected via a customised app and deep clinical phenotypical data (n=245). A total of 7208 participants are followed in nationwide health registries with full data coverage and long-term follow-up. FINDINGS TO DATE: We highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we use 803 000 data points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterise distinct use patterns. Based on these objective tracking data, we characterise four clusters of night-time smartphone use. FUTURE PLANS: The unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study has been expanded by linkage to nationwide registers, which allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years.

Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship.

STUDY OBJECTIVES: This study investigated the complex relationship between nighttime smartphone use, sleep, and mental health among adult populations in Denmark. METHODS: Data from three interconnected samples (aged 16-89 years) from the SmartSleep Study included 5798 individuals with survey and register data; 4239 individuals also provided high-resolution smartphone tracking data. Logistic regression models and causal discovery algorithms, which suggest possible causal pathways consistent with the underlying data structure, were used to infer the relationship between self-reported and tracked nighttime smartphone use, self-reported sleep quality, mental health indicators, and register-based psychotropic medication use. RESULTS: Frequent self-reported nighttime smartphone use was associated with high perceived stress (OR: 2.24, 95% CI = 1.42 to 3.55) and severe depressive symptoms (OR: 2.96, 95% CI = 2.04 to 4.28). We found no clear associations between tracked nighttime smartphone use and mental health outcomes, except for the cluster that used their smartphones repeatedly during the sleep period, which was associated with severe depressive symptoms (OR = 1.69, 95% CI = 1.24 to 2.31). Poor sleep quality (vs. good sleep quality) was associated with high perceived stress (OR = 5.07, 95% CI = 3.72 to 6.90), severe depressive symptoms (OR = 9.67, 95% CI = 7.09 to 13.19), and psychotropic medication use (OR = 2.13, 95% CI = 1.36 to 3.35). The causal discovery models suggest that nighttime smartphone use affects mental health through both problematic smartphone use and poor sleep quality. CONCLUSION: The complex relationship between nighttime smartphone use, sleep, and poor mental health may create a vicious circle over time, and nighttime smartphone use may constitute a potential leverage point for public health interventions aimed at improving sleep and mental health.

Early childhood adversity and body mass index in childhood and adolescence: linking registry data on adversities with school health records of 53,401 children from Copenhagen.

OBJECTIVE: We examined whether childhood adversity experienced in early childhood (0-5 years) is related to body mass index (BMI) in childhood (6-7 years) and adolescence (12-15 years). METHODS: This study combined data from the nationwide register-based DANLIFE study on childhood adversities with data on height and weight of school children in Copenhagen. Data were available for 53,401 children born in Denmark between 1980 and 1996. Children were divided into groups of early childhood adversity by applying group-based multi-trajectory modelling using their yearly count of childhood adversity in three dimensions (i.e., material deprivation, loss or threat of loss, and family dynamics) from 0-5 years. Direct and total associations between the early childhood adversity groups and BMI z-scores in childhood and adolescence were estimated using sex-stratified structural equation models. RESULTS: Five exclusive and exhaustive groups of early childhood adversity were identified, which were characterized by low adversity (51%), moderate material deprivation (30%), high material deprivation (14%), loss or threat of loss (3%) and high adversity (2%). Boys and girls exposed to moderate or high material deprivation and loss or threat of loss had a slightly higher BMI z-score, especially in adolescence, compared with those in the low adversity group, with the strongest association found for girls in the loss or threat of loss group (b (95% CI) = 0.18 (0.10, 0.26)). Additionally, boys in the high adversity group had a slightly lower BMI z-score in childhood than boys in the low adversity group (b (95% CI) = -0.12 (-0.22, -0.02)). CONCLUSIONS: Whereas associations with BMI were found for children and adolescents exposed to material deprivation, loss or threat of loss, and high adversity, the effect sizes were generally small. Contrary to prevailing hypotheses, weight changes in childhood is probably not a major explanatory mechanism linking early childhood adversity with later-life morbidity.

Night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women: A population-based study with high-resolution tracking data.

Study Objectives: To explore the relationship among night-time smartphone use, sleep duration, sleep quality, and menstrual disturbances in young adult women. Methods: Women aged 18-40 years were included in the SmartSleep Study in which they objectively tracked their smartphone use via the SmartSleep app between self-reported sleep onset and offset times (n = 764) and responded to a survey (n = 1068), which included background characteristics, sleep duration, sleep quality (Karolinska Sleep Questionnaire), and menstrual characteristics (International Federation of Gynecology and Obstetrics' definitions). Results: The median tracking time was four nights (interquartile range: 2-8). Higher frequency (p = .05) and longer duration (p = .02) of night-time smartphone use were associated with long sleep duration (≥9 h), but not with poor sleep quality or short sleep duration (<7 h). Short sleep duration was associated with menstrual disturbances (OR = 1.84, 95% confidence interval [CI] = 1.09 to 3.04) and irregular menstruation (OR = 2.17, 95% CI = 1.08 to 4.10), and poor sleep quality was associated with menstrual disturbances (OR = 1.43, 95% CI = 1.19 to 1.71), irregular menstruation (OR = 1.34, 95% CI = 1.04 to 1.72), prolonged bleedings (OR = 2.50, 95% CI = 1.44 to 4.43) and short-cycle duration (OR = 1.40, 95% CI = 1.06 to 1.84). Neither duration nor frequency of night-time smartphone use was associated with menstrual disturbances. Conclusions: Night-time smartphone use was associated with longer sleep duration, but not with menstrual disturbances in adult women. Short sleep duration and sleep quality were associated with menstrual disturbances. Further investigation of the effects of night-time smartphone use on sleep and female reproductive function in large prospective studies is needed.

Workplace Psychosocial Resources and Risk of Sleep Disturbances Among Employees.

Importance: Workplace psychosocial resources naturally tend to cluster in some work teams. To inform work-related sleep health promotion interventions, it is important to determine the associations between clustering of workplace resources and sleep disturbances when some resources are high while others are low and to mimic an actual intervention using observational data. Objective: To examine whether clustering of and changes in workplace psychosocial resources are associated with sleep disturbances among workers. Design, Setting, and Participants: This population-based cohort study used data from the Swedish Longitudinal Occupational Survey of Health (2012-2018), the Work Environment and Health in Denmark study (2012-2018), and the Finnish Public Sector Study (2008-2014), collected biennially. Statistical analysis was conducted from November 2020 to June 2022. Exposure: Questionnaires were distributed measuring leadership quality and procedural justice (ie, vertical resources) as well as collaboration culture and coworker support (ie, horizontal resources). Resources were divided into clusters of general low, intermediate vertical and low horizontal, low vertical and high horizontal, intermediate vertical and high horizontal, and general high. Main Outcomes and Measures: Odds ratios (ORs) and 95% CIs were reported from logistic regression models for the associations between the clustering of resources and concurrent and long-term sleep disturbances. Sleep disturbances were measured by self-administered questionnaires. Results: The study identified 114 971 participants with 219 982 participant-observations (151 021 [69%] women; mean [SD] age, 48 [10] years). Compared with participants with general low resources, other groups showed a lower prevalence of sleep disturbances, with the lowest observed in the general high group concurrently (OR, 0.38; 95% CI, 0.37-0.40) and longitudinally after 6 years (OR, 0.52; 95% CI, 0.48-0.57). Approximately half of the participants (27 167 participants [53%]) experienced changes in resource clusters within 2 years. Improvements in vertical or horizontal dimensions were associated with reduced odds of persistent sleep disturbances, and the lowest odds of sleep disturbances was found in the group with improvements in both vertical and horizontal dimensions (OR, 0.53; 95% CI, 0.46-0.62). A corresponding dose-response association with sleep disturbances was observed for decline in resources (eg, decline in both dimensions: OR, 1.74; 95% CI, 1.54-1.97). Conclusions and Relevance: In this cohort study of workplace psychosocial resources and sleep disturbances, clustering of favorable resources was associated with a lower risk of sleep disturbances.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data.

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Implications of Non-Specific Effects for Testing, Approving, and Regulating Vaccines.

The current framework for testing and regulating vaccines was established before the realization that vaccines, in addition to their effect against the vaccine-specific disease, may also have "non-specific effects" affecting the risk of unrelated diseases. Accumulating evidence from epidemiological studies shows that vaccines in some situations can affect all-cause mortality and morbidity in ways that are not explained by the prevention of the vaccine-targeted disease. Live attenuated vaccines have sometimes been associated with decreases in mortality and morbidity that are greater than anticipated. In contrast, some non-live vaccines have in certain contexts been associated with increases in all-cause mortality and morbidity. The non-specific effects are often greater for female than male individuals. Immunological studies have provided several mechanisms that explain how vaccines might modulate the immune response to unrelated pathogens, such as through trained innate immunity, emergency granulopoiesis, and heterologous T-cell immunity. These insights suggest that the framework for the testing, approving, and regulating vaccines needs to be updated to accommodate non-specific effects. Currently, non-specific effects are not routinely captured in phase I-III clinical trials or in the post-licensure safety surveillance. For instance, an infection with Streptococcus pneumoniae occurring months after a diphtheria-tetanus-pertussis vaccination would not be considered an effect of the vaccination, although evidence indicates it might well be for female individuals. Here, as a starting point for discussion, we propose a new framework that considers the non-specific effects of vaccines in both phase III trials and post-licensure.

Pre- and perinatal exposures associated with developing pediatric-onset immune-mediated inflammatory disease: A Danish nation-wide cohort study.

OBJECTIVES: We aimed to identify pre- and perinatal risk factors for developing pediatric-onset immune-mediated inflammatory (pIMID). METHODS: This nation-wide, cohort study included all children born in Denmark from 1994 to 2014 identified from the Danish Medical Birth registry. Individuals were followed through 2014 and cross-linked to the continuously updated national socioeconomic and healthcare registers to obtain data on pre- and perinatal exposures (maternal age, educational level, smoking, maternal IMID, parity, mode of conception and delivery, plurality, child's sex, and birth season). The primary outcome was a pIMID diagnosis (inflammatory bowel disease, autoimmune hepatitis, primary sclerosing cholangitis, juvenile idiopathic arthritis, or systemic lupus erythematosus) before 18 years of age. Risk estimates were calculated using Cox proportional hazards model and presented by hazard ratios (HR) with 95% confidence intervals (95%CI). RESULTS: We included 1,350,353 children with a follow-up time of 14,158,433 person-years. Among these, 2,728 were diagnosed with a pIMID. We found a higher risk of pIMID in children born to women with a preconception IMID diagnosis (HR: 3.5 [95%CI: 2.7-4.6]), children born by Caesarean section (HR: 1.2 [95%CI: 1.0-1.3]), and among females (1.5 [95%CI: 1.4-1.6]) than among children without these characteristics. Plural pregnancies were associated with a lower risk of pIMID than single pregnancies (HR: 0.7 [95%CI: 0.6-0.9]). CONCLUSIONS: Our results indicate a high genetic burden in pIMID but also identifies intervenable risk factors, such as Cesarean section. Physicians should, keep this in mind when caring for high-risk populations and pregnant women previously diagnosed with an IMID.

Cancer burden among adolescents and young adults in relation to childhood adversity: a nationwide life-course cohort study of 1.2 million individuals.

Background: Childhood adversity such as poverty, loss of a parent, and dysfunctional family dynamics may be associated with exposure to environmental and behavioral hazards, interfere with normal biological functions, and affect cancer care and outcomes. To explore this hypothesis, we assessed the cancer burden among young men and women exposed to adversity during childhood. Methods: We undertook a population-based study using Danish nationwide register data on childhood adversity and cancer outcomes. Children who were alive and resident in Denmark until their 16th birthday were followed into young adulthood (16-38 years). Group-based multi-trajectory modelling was used to categorize individuals into five distinct groups: low adversity, early material deprivation, persistent material deprivation, loss/threat of loss, and high adversity. We assessed the association with overall cancer incidence, mortality, and five-year case fatality; and cancer specific outcomes for the four most common cancers in this age group in sex-stratified survival analyses. Findings: 1,281,334 individuals born between Jan 1, 1980, and Dec 31, 2001, were followed up until Dec 31, 2018, capturing 8229 incident cancer cases and 662 cancer deaths. Compared to low adversity, women who experienced persistent material deprivation carried a slightly lower risk of overall cancer (hazard ratio (HR) 0.90; 95% CI 0.82; 0.99), particularly due to malignant melanoma and brain and central nervous system cancers, while women who experienced high adversity carried a higher risk of breast cancer (HR 1.71; 95% CI 1.09; 2.70) and cervical cancer incidence (HR 1.82; 95% CI 1.18; 2.83). While there was no clear association between childhood adversity and cancer incidence in men, those men who had experienced persistent material deprivation (HR 1.72; 95% CI 1.29; 2.31) or high adversity (HR 2.27; 95% CI 1.38; 3.72) carried a disproportionate burden of cancer mortality during adolescence or young adulthood compared to men in the low adversity group. Interpretation: Childhood adversity is associated with a lower risk of some subtypes of cancer and a higher risk of others, particular in women. Persistent deprivation and adversity are also associated with a higher risk of adverse cancer outcomes for men. These findings may relate to a combination of biological susceptibility, health behaviors and treatment-related factors. Funding: None.

Promoting the health of vulnerable populations: Three steps towards a systems-based re-orientation of public health intervention research.

This paper proposes a novel framework for the development of interventions in vulnerable populations. The framework combines a complex systems lens with syndemic theory. Whereas funding bodies, research organizations and reporting guidelines tend to encourage intervention research that (i) focuses on singular and predefined health outcomes, (ii) searches for generalizable cause-effect relationships, and (iii) aims to identify universally effective interventions, the paper suggests that a different direction is needed for addressing health inequities: We need to (i) start with exploratory analysis of population-level data, and (ii) invest in contextualized in-depth knowledge of the complex dynamics that produce health inequities in specific populations and settings, while we (iii) work with stakeholders at multiple levels to create change within systems.

Preventing Stress among High School Students in Denmark through the Multicomponent Healthy High School Intervention-The Effectiveness at First Follow-Up.

Stress is a widespread phenomenon and young people especially are experiencing high levels of stress. School-related factors are the most frequently self-reported stressors among adolescents, but few interventions have targeted the school environment. This study evaluated the effectiveness of the Healthy High School (HHS) intervention on stress at a 9-month follow-up. The study included 5201 first-year high school students (~16 years) in Denmark. Participating schools were randomized into the HHS intervention (N = 15) or control group (N = 15). Baseline measurements were conducted in August 2016 and the follow-up was conducted in May 2017. The intervention was designed to promote well-being (primary outcome) by focusing on physical activity, meals, sleep, sense of security, and stress (secondary outcomes). The intervention comprised: structural initiatives at the school level; a teaching material; peer-led innovation workshops; and a smartphone app. The 10-item Perceived Stress Scale was used to measure stress. Intervention effects on perceived stress were estimated using an intention-to-treat approach with multiple imputations of missing data and multilevel general linear regression modelling. A total of 4577 students answered the baseline questionnaire. No statistically significant difference was found in stress between students at intervention and control schools at the follow-up (mean score: 16.7 versus 16.7, adjusted b = 0.42, 95% CI: -0.16;1.00). The HHS Study is one of the first large randomized controlled trials targeting school environmental stressors. Potential implementation failures and the failures of the program theory are discussed.