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BACKGROUND: Urban-rural differences in schizophrenia risk have been widely evidenced across Western countries. However, explanation of these differences is lacking. We aimed to identify contextual risk factors for schizophrenia that explain urban-rural differences in schizophrenia risk. METHODS: Utilizing Danish population-based registers, we partitioned Denmark into 1885 geographic neighborhoods homogeneously sized in terms of population. Information on the entire Danish population from 1981 to 2016 was used to quantify a spectrum of neighborhood-level domains. We subsequently conducted multilevel survival analyses following persons born in Denmark from 1971 to 1982 for the development of schizophrenia allowing for clustering of people within neighborhoods. We used this method to tease apart the effects of individual, specific, and general contextual risk factors for schizophrenia. RESULTS: A significant general contextual effect in schizophrenia risk across neighborhoods was estimated (Medium Incidence Rate Ratio (MRR):1.41; 95% CI:1.35-1.48). Most of the specific contextual factors examined were associated with schizophrenia risk. For instance, neighborhood-level proportion of lone adult households (Incidence Rate Ratios (IRR):1.53; 95% CI:1.44-1.63) had largest risk estimate. Adjustment for all individual-level and specific contextual constructs reduced the IRR for urbanicity from 1.98 (95% CI:1.77-2.22) to 1.30 (95% CI:1.11-1.51). CONCLUSIONS: In the largest prospective multilevel survival analyses of schizophrenia risk conducted to date, multiple neighborhood-level characteristics were associated with raised schizophrenia risk, with these contextual factors explaining most of the elevated risk linked with urbanicity. However, the unexplained heterogeneity that was evident in our multilevel models indicates that our understanding of the role of urbanicity in schizophrenias etiology remains incomplete.

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis‐generation, we produced an open‐source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980–2012, of Denmark‐born parents (1,697,231 births), and their 3‐generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis‐family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex‐specific co‐occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co‐occurrence aHRS. Results are cataloged in interactive heat maps and down‐loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots%5f16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta‐analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity. Lay Summary: We calculated the likelihood that a person will be diagnosed with autism if they had a specific family member (e.g., a parent, sibling, grandparent) with a specific mental, neurologic, cardiometabolic, birth defect, asthma, allergy, or autoimmune condition—over 6000 separate estimates based on 26,840 autistic persons. Results are cataloged in interactive figures and down‐loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots%5f16918786403110/e-Figure5. The study of autism family health history—which varies widely by condition, family member type, sex of the family member, side of the family, sex of the index person, intellectual disability status—may advance understanding of factors underlying neurodiversity. [ABSTRACT FROM AUTHOR]

Background: Despite the recognised importance of mental disorders and social disconnectedness for mortality, few studies have examined their co-occurrence. Aims: To examine the interaction between mental disorders and three distinct aspects of social disconnectedness on mortality, while taking into account sex, age and characteristics of the mental disorder. Method: This cohort study included participants from the Danish National Health Survey in 2013 and 2017 who were followed until 2021. Survey data on social disconnectedness (loneliness, social isolation and low social support) were linked with register data on hospital-diagnosed mental disorders and mortality. Poisson regression was applied to estimate independent and joint associations with mortality, interaction contrasts and attributable proportions. Results: A total of 162 497 individuals were followed for 886 614 person-years, and 9047 individuals (5.6%) died during follow-up. Among men, interaction between mental disorders and loneliness, social isolation and low social support, respectively, accounted for 47% (95% CI: 21–74%), 24% (95% CI: −15 to 63%) and 61% (95% CI: 35–86%) of the excess mortality after adjustment for demographics, country of birth, somatic morbidity, educational level, income and wealth. In contrast, among women, no excess mortality could be attributed to interaction. No clear trends were identified according to age or characteristics of the mental disorder. Conclusions: Mortality among men, but not women, with a co-occurring mental disorder and social disconnectedness was substantially elevated compared with what was expected. Awareness of elevated mortality rates among socially disconnected men with mental disorders could be of importance to qualify and guide prevention efforts in psychiatric services. [ABSTRACT FROM AUTHOR]

Key Points: Question: Does the association between mental disorders and mortality vary by socioeconomic position (SEP)? Findings: In this systematic review and meta-analysis of 71 observational studies, relative associations between mental disorders and mortality were similar across different SEP levels; however, there were lower relative mortality risks for natural causes but higher for external causes among individuals with mental disorders and of higher rather than lower SEP. Evidence on absolute scales, specific diagnoses, and specific causes of death was scarce. Meaning: The findings in this study related to SEP and a possible differential risk between natural and external causes of death in individuals with mental disorders warrant additional research. This systematic review and meta-analysis explores the potential role of socioeconomic position in the association between mental disorders and mortality as well as the association between socioeconomic position and mortality among individuals with mental disorders. Importance: Studies are lacking summarizing how the association between mental disorders and mortality varies by socioeconomic position (SEP), particularly considering different aspects of SEP, specific types of mental disorders, and causes of death. Objective: To investigate the role of SEP in the association between mental disorders and mortality and the association between SEP and mortality among people with mental disorders. Data Sources: MEDLINE, Embase, PsycINFO, and Web of Science were searched from January 1, 1980, through April 3, 2023, and a snowball search of reference and citation lists was conducted. Study Selection: Inclusion criteria were observational studies estimating the associations between different types of mental disorders and mortality, stratified by SEP and between SEP and mortality in people with mental disorders. Data Extraction and Synthesis: Pairs of reviewers independently extracted data using a predefined data extraction form and assessed the risk of bias using the adapted Newcastle-Ottawa scale. Graphical analyses of the dose-response associations and random-effects meta-analyses were performed. Heterogeneity was explored through meta-regressions and sensitivity analyses. Main Outcomes and Measures: All-cause and cause-specific mortality. Results: Of 28 274 articles screened, 71 including more than 4 million people with mental disorders met the inclusion criteria (most of which were conducted in high-income countries). The relative associations between mental disorders and mortality were similar across SEP levels. Among people with mental disorders, belonging to the highest rather than the lowest SEP group was associated with lower all-cause mortality (pooled relative risk [RR], 0.79; 95% CI, 0.73-0.86) and mortality from natural causes (RR, 0.73; 95% CI, 0.62-0.85) and higher mortality from external causes (RR, 1.18; 95% CI, 0.99-1.41). Heterogeneity was high (I2 = 83% to 99%). Results from subgroup, sensitivity, and meta-regression analyses were consistent with those from the main analyses. Evidence on absolute scales, specific diagnoses, and specific causes of death was scarce. Conclusion and Relevance: This study did not find a sufficient body of evidence that SEP moderated the relative association between mental disorders and mortality, but the underlying mortality rates may differ by SEP group, despite having scarcely been reported. This information gap, together with our findings related to SEP and a possible differential risk between natural and external causes of death in individuals with specific types of mental disorders, warrants further research. [ABSTRACT FROM AUTHOR]

Understanding how social experiences throughout life shape later loneliness levels may help to identify how to alleviate loneliness at later lifestages. This study investigates the association between social relationship adversities throughout the lifecourse and loneliness in later life. Using prospective data from the Medical Research Council National Survey of Health and Development (N = 2,453), we conducted multivariable analyses to investigate independent, cumulative and moderated effects between the number of social relationship adversities experienced in childhood, mid-adulthood and later adulthood and the feeling of loneliness at age 68. We examined interactions between social relationship adversities and current quantity and quality aspects of social relationships. We found evidence of a step-dose response where greater exposure to social relationship adversities experienced at three earlier lifestages predicted higher loneliness levels in later life with more recent social relationship adversities more strongly related to loneliness. The results also demonstrated support for exacerbation and amelioration of earlier adverse social relationship experiences by current social isolation and relationship quality, respectively. This study suggests that social relationship adversities experienced throughout the lifecourse continue to influence loneliness levels much later in life. A key finding is that adverse social relationship experiences in earlier life may explain why otherwise socially similar individuals differ in their levels of loneliness. Implications for policy and research are discussed. [ABSTRACT FROM AUTHOR]

Background Early-life socioeconomic position (SEP) is associated with lifestyle-related diseases in adulthood. However, evidence is lacking on the extent to which adult SEP mediates this association. Methods Time to either chronic obstructive pulmonary disease (COPD), cardiovascular disease or diabetes were assessed in the Danish population born between 1961 and 1971 (n = 793 674) from age 30 until 2015. Early-life position was assessed in 1981 (by parental) and again at age 30 (own) by four markers; income, occupation, education—divided into high, middle, low—and a combined score for all markers. Using a counterfactual approach, we estimated the total effect of early-life position on disease onset and the degree to which adult position mediated this effect. Results Results of the time-to-event analysis showed a gradient of all early-life markers on the risk of developing all lifestyle-related diseases. Notably, comparing those in the lowest to the highest educational position, the hazard of COPD was 130% higher for women [hazard ratio = 2.30(95% confidence interval = 2.20–2.41)] and 114% higher for men [2.14 (2.05–2.25)]. About 67%(63–70%) of the effect of educational position was mediated through adult position for COPD, 55% for cardiovascular disease and 50% for diabetes. For the combined score 44, 29 and 33%, respectively, was mediated. Conclusion About one-tenth to two-thirds of the effect of early-life position is mediated by the position attained in adulthood. The degree mediated depend on the outcome investigated, gender and the social position marker used indicating that alternative pathways may play a key role in developing effective policies targeting early-life behaviours. [ABSTRACT FROM AUTHOR]

Objectives: Improving the design and targeting of interventions is important for alleviating loneliness among older adults. This requires identifying which correlates are the most important predictors of loneliness. This study demonstrates the use of recursive partitioning in exploring the characteristics and assessing the relative importance of correlates of loneliness in older adults. Method: Using exploratory regression trees and random forests, we examined combinations and the relative importance of 42 correlates in relation to loneliness at age 68 among 2453 participants from the birth cohort study the MRC National Survey of Health and Development. Results: Positive mental well-being, personal mastery, identifying the spouse as the closest confidant, being extrovert and informal social contact were the most important correlates of lower loneliness levels. Participation in organised groups and demographic correlates were poor identifiers of loneliness. The regression tree suggested that loneliness was not raised among those with poor mental wellbeing if they identified their partner as closest confidante and had frequent social contact. Conclusion: Recursive partitioning can identify which combinations of experiences and circumstances characterise high-risk groups. Poor mental wellbeing and sparse social contact emerged as especially important and classical demographic factors as insufficient in identifying high loneliness levels among older adults. [ABSTRACT FROM AUTHOR]

Multiple imputation by chained equations (MICE) is an effective tool to handle missing data - an almost unavoidable problem in quantitative data analysis. However, despite the empirical and theoretical evidence supporting the use of MICE, researchers in the social sciences often resort to inferior approaches unnecessarily risking erroneous results. The complexity of the decision process when encountering missing data may be what is discouraging potential users from adopting the appropriate technique. In this article, we develop straightforward step-by-step graphical guidelines on how to handle missing data based on a comprehensive literature review. It is our hope that these guidelines can help improve current standards of handling missing data. The guidelines incorporate recent innovations on how to handle missing data such as random forests and predictive mean matching. Thus, the data analysts who already actively apply MICE may use it to review some of the newest developments. We demonstrate how the guidelines can be used in praxis using the statistical program R and data from the European Social Survey. We demonstrate central decisions such as variable selection and number of imputations as well as how to handle typical challenges such as skewed distributions and data transformations. These guidelines will enable a social science researcher to go through the process of handling missing data while adhering to the newest developments in the field. [ABSTRACT FROM AUTHOR]

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis-generation, we produced an open-source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are cataloged in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity., (© 2024 The Author(s). Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)

Psychiatric family history or a high autism polygenic risk score (PRS) have been separately linked to autism spectrum disorder (ASD) risk. The study aimed to simultaneously consider psychiatric family history and individual autism genetic liability (PRS) in autism risk. We performed a case-control study of all Denmark singleton births, May 1981-December 2005, in Denmark at their first birthday and a known mother. Cases were diagnosed with ASD before 2013 and controls comprised a random sample of 30,000 births without ASD, excluding persons with non-Denmark-born parents, missing ASD PRS, non-European ancestry. Adjusted odds ratios (aOR) were estimated for ASD by PRS decile and by psychiatric history in parents or full siblings (8 mutually-exclusive categories) using logistic regression. Adjusted ASD PRS z-score least-squares means were estimated by psychiatric family history category. ASD risk (11,339 ASD cases; 20,175 controls) from ASD PRS was not substantially altered after accounting for psychiatric family history (e.g., ASD PRS 10th decile aOR: 2.35 (95% CI 2.11-2.63) before vs 2.11 (95% CI 1.91-2.40) after adjustment) nor from psychiatric family history after accounting for ASD PRS (e.g., ASD family history aOR: 6.73 (95% CI 5.89-7.68) before vs 6.32 (95% CI 5.53-7.22) after adjustment). ASD risk from ASD PRS varied slightly by psychiatric family history. While ASD risk from psychiatric family history was not accounted for by ASD PRS and vice versa, risk overlap between the two factors will likely increase as measures of genetic risk improve. The two factors are best viewed as complementary measures of family-based autism risk. LAY SUMMARY: Autism risk from a history of mental disorders in the immediate family was not explained by a measure of individual genetic risk (autism polygenic risk score) and vice versa. That is, genetic risk did not appear to overlap family history risk. As genetic measures for autism improve then the overlap in autism risk from family history versus genetic factors will likely increase, but further study may be needed to fully determine the components of risk and how they are inter-related between these key family factors. Meanwhile, the two factors may be best viewed as complementary measures of autism family-based risk., (© 2021 International Society for Autism Research and Wiley Periodicals LLC.)