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.

The causal effect of early retirement on medication use across sex and occupation: evidence from Danish administrative data.

We examine the causal effect of early retirement on medication use using Danish registry data. A reform in early retirement schemes in 2006 gradually increased eligibility ages from 60 to 64 differentially across birth cohorts. This enables an instrumental variable design that was applied using novel g-estimation methods that alleviate bias in binary outcome IV models. Our data allow studying patterns in the short run (ages 59½-60½) and in the long run (ages 57-63). For those who were eligible already at age 60, retirement did not change overall medication use. However, when investigating medication and population subgroups, we see that painkiller use decreases and hypertension medication as well as mental health medication use increase after retirement in almost all population subgroups. Moreover, males as well as the blue-collar occupation subgroups do show decreases in overall medication use after early retirement. In conclusion, our analyses reveal that retirement can have important heterogeneous health effects across population groups and are potentially informative about the welfare benefits of social insurance more broadly.

Unlocking the mechanisms of change in the MAMAACT intervention to reduce ethnic disparity in stillbirth and newborns' health: integration of evaluation findings.

Ethnic disparities in stillbirth exist in Europe and suboptimal care due to miscommunication is one contributing cause. The MAMAACT intervention aimed to reduce ethnic disparity in stillbirth and newborns' health through improved management of pregnancy complications. The intervention encompassed training of antenatal care midwives in cultural competencies and intercultural communication combined with health education materials for the expecting parents about symptoms of pregnancy complications. The evaluation consisted of a qualitative in-depth implementation analysis and a process evaluation embedded in a cluster randomized trial including 19 of 20 maternity wards in Denmark. In this article, the findings from the different evaluation perspectives are integrated. The integration follows the principles of realist evaluation by analyzing to what extent the MAMAACT activities were generating mechanisms of change in interaction with the context. The integration analysis shows that the health education materials in the MAMAACT intervention contributed to heightened health literacy concerning pregnancy complications among pregnant women. Additionally, the training of midwives in cultural competency and intercultural communication raised awareness among midwives. Nonetheless, the exclusive emphasis on midwives and the inflexibility in care provision hindered them from changing their communication practices. To enhance the cultural competence in maternity care, it is essential to implement more comprehensive initiatives involving healthcare professionals in maternity care at all levels, from pregraduate to postgraduate. Adequate interpreter services and management support should also be ensured. Currently, the Danish antenatal care system faces challenges including inadequate information transfer between healthcare sectors, insufficient differentiation of care, and inflexibility in midwife scheduling. This results in a lack of responsiveness to the individual needs of women with immigrant backgrounds, potentially reproducing health inequities.

Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements.

Agreement between methods for quantitative measurements are typically assessed by computing limits of agreement between pairs of methods and/or by illustration through Bland-Altman plots. We consider the situation where the observed measurement methods are considered a random sample from a population of possible methods, and discuss how the underlying linear mixed effects model can be extended to this situation. This is relevant when, for example, the methods represent raters/judges that are used to score specific individuals or items. In the case of random methods, we are not interested in estimates pertaining to the specific methods, but are instead interested in quantifying the variation between the methods actually involved making measurements, and accommodating this as an extra source of variation when generalizing to the clinical performance of a method. In the model we allow raters to have individual precision/skill and permit linked replicates (i.e., when the numbering, labeling or ordering of the replicates within items is important). Applications involving estimation of the limits of agreement for two datasets are shown: A dataset of spatial perception among a group of students as well as a dataset on consumer preference of French chocolate. The models are implemented in the MethComp package for R [Carstensen B, Gurrin L, Ekstrøm CT, Figurski M. MethComp: functions for analysis of agreement in method comparison studies; 2013. R package version 1.22, R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012].

Joint regression analysis of multiple traits based on genetic relationships.

Motivation: Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties. Results: Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency. Availability and implementation: GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.

Emergence of the obesity epidemic preceding the presumed obesogenic transformation of the society.

The obesity epidemic, evolving in many countries since the 1970s, has been attributed to the widespread contemporary so-called obesogenic transformation of the societies, but what preceded the epidemic? Using quantile regression, we studied the trends by year of birth in the percentile distribution of body mass index (BMI = weight/height2) of 320,962 Danish school children, born from 1930 to 1976, and of 205,153 Danish young conscripts, born from 1939 to 1959. The overall trend of the percentiles of the BMI distributions were found to be linear across the years of birth. While the percentiles below the 75th were almost stable, those above showed a steadily steeper rise the more extreme the percentile among both school children and young men is. These changes, indicating the emergence of the obesity epidemic, preceded the presumed obesogenic transformation of the society by several decades and imply that other, so far unknown, factors have been involved.

Inflammatory human leucocyte antigen genotypes are not a risk factor in chronic subdural hematoma development.

BACKGROUND: Chronic subdural hematoma (CSDH) pathophysiology has undergone a paradigm shift from being regarded as solely traumatic to be driven mainly by inflammation. Human leucocyte antigen (HLA) is a gene complex involved in antigen processing and presentation to T lymphocytes, thereby mediating the adaptive immune responses. As specific HLA profiles are associated with inflammatory diseases, patients with a specific HLA profile may have a lower threshold for subdural inflammation, and therefore are predisposed for CSDH development. We hypothesized that (1) CSDH patients have a specific HLA profile compared to a Danish background population, and (2) patients with recurrent CSDH have a specific HLA profile compared to CSDH patients without recurrent CSDH. METHODS: Three specific HLA class II haplotypes known to drive inflammatory-mediated diseases were determined in 68 patients with CSDH. The distribution of these three haplotypes in our CSDH population was compared to a Danish population of blood donors using Monte Carlo Pearson's chi-square test. Furthermore, the distribution of the haplotypes was compared between CSDH patients with and without recurrent CSDH. RESULTS: We found no significant association between either of the haplotypes and the risk of CSDH, and neither of the haplotypes were associated with increased risk of CSDH recurrence. CONCLUSION: This study did not show an association between selected HLA class II haplotypes and the risk of CSDH or recurrence of CSDH compared with a healthy background population.

Improving health literacy responsiveness to reduce ethnic and social disparity in stillbirth and infant health: A cluster randomized controlled effectiveness trial of the MAMAACT intervention.

OBJECTIVE: The MAMAACT intervention aims to reduce ethnic and social disparities in stillbirth and infant death by improving communication between pregnant women and midwives regarding warning signs of pregnancy complications. This study evaluates the effect of the intervention on pregnant women's health literacy (two domains from the Health Literacy Questionnaire) and complication management - interpreted as improved health literacy responsiveness among midwives. DESIGN: Cluster randomized controlled trial, 2018-2019. SETTING: 19 of 20 Danish maternity wards. PARTICIPANTS: Cross-sectional survey data were collected using telephone interviews (n = 4150 pregnant women including 670 women with a non-Western immigrant background). INTERVENTION: A six-hour training session for midwives in intercultural communication and cultural competence, two follow-up dialog meetings, and health education materials for pregnant women on warning signs of pregnancy complications - in six languages. MAIN OUTCOME MEASURES: Differences in mean scores at post-implementation of the domains Active engagement with healthcare providers (Active engagement) and Navigating the healthcare system from the Health Literacy Questionnaire, and differences in the certainty of how to respond to pregnancy complication signs between women in the intervention and control group. RESULTS: No difference was observed in women's level of Active engagement or Navigating the healthcare system. Women from the intervention group were more certain of how to respond to complication signs: Redness, swelling, and heat in one leg: 69.4 % vs 59.1 %; aOR 1.57 (95 % CI 1.32-1.88), Severe headache: 75.6 % vs 67.3 %; aOR 1.50 (95 % CI 1.24-1.82), and Vaginal bleeding: 97.3 % vs 95.1 %; aOR 1.67 (95 % CI 1.04-2.66). CONCLUSION: The intervention improved women's certainty of how to respond to complication signs, but was unable to improve pregnant women's health literacy levels of Active engagement and Navigating the healthcare system, likely due to barriers related to the organization of antenatal care. A reorganization of antenatal care and a care model sensitive to diversity within the entire healthcare system might help reduce disparities in perinatal health. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03751774.

Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches.

Life-course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigated whether data-driven causal discovery algorithms can help in this process. We focused on a longitudinal data set on a cohort of Danish men (the Metropolit Study, 1953-2017). The theory-driven models were constructed by 2 subject-field experts. The data-driven models were constructed by use of the temporal Peter-Clark (TPC) algorithm. The TPC algorithm utilizes the temporal information embedded in life-course data. We found that the data-driven models recovered some, but not all, causal relationships included in the theory-driven expert models. The data-driven method was especially good at identifying direct causal relationships that the experts had high confidence in. Moreover, in a post hoc assessment, we found that most of the direct causal relationships proposed by the data-driven model but not included in the theory-driven model were plausible. Thus, the data-driven model may propose additional meaningful causal hypotheses that are new or have been overlooked by the experts. In conclusion, data-driven methods can aid causal model construction in life-course epidemiology, and combining both data-driven and theory-driven methods can lead to even stronger models.

Effectiveness of a community-based support programme to reduce social inequality in exclusive breastfeeding: study protocol for a cluster-randomised trial.

BACKGROUND: Breastmilk is the ideal nutrition for infants, and breastfeeding protects infants and mothers from a range of adverse health outcomes. In Denmark, most mothers initiate breastfeeding but many cease within the first months resulting in just 14% reaching the World Health Organization recommendation of six months of exclusive breastfeeding. Furthermore, the low breastfeeding proportion at six months is characterised by a marked social inequality. A previous intervention tested in a hospital setting succeeded in increasing the proportion of mothers breastfeeding exclusively at six months. However, most breastfeeding support is provided within the Danish municipality-based health visiting programme. Therefore, the intervention was adapted to fit the health visiting programme and implemented in 21 Danish municipalities. This article reports the study protocol, which will be used to evaluate the adapted intervention. METHODS: The intervention is tested in a cluster-randomised trial at the municipal level. A comprehensive evaluation approach is taken. The effectiveness of the intervention will be evaluated using survey and register data. Primary outcomes are the proportion of women who breastfeed exclusively at four months postpartum and duration of exclusive breastfeeding measured as a continuous outcome. A process evaluation will be completed to evaluate the implementation of the intervention; a realist evaluation will provide an understanding of the mechanisms of change characterising the intervention. Finally, a health economic evaluation will assess the cost-effectiveness and cost-utility of this complex intervention. DISCUSSION: This study protocol reports on the design and evaluation of the Breastfeeding Trial - a cluster-randomised trial implemented within the Danish Municipal Health Visiting Programme from April 2022 to October 2023. The purpose of the programme is to streamline breastfeeding support provided across healthcare sectors. The evaluation approach is comprehensive using a multitude of data to analyse the effect of the intervention and inform future efforts to improve breastfeeding for all. TRIAL REGISTRATION: Prospectively registered with Clinical Trials NCT05311631 https://clinicaltrials.gov/ct2/show/NCT05311631.

Mortality, risk factors, and causes of death among people with tuberculosis in Denmark, 1990-2018.

OBJECTIVES: This study aimed to analyze mortality, risk factors, and causes of death among people with tuberculosis (TB). METHODS: This is a population-based cohort study with patients with TB ≥18 years notified from 1990 to 2018 in Denmark, compared with sex- and age-matched controls. Mortality was assessed in Kaplan-Meier models and risk factors for death were estimated in Cox proportional hazards models. RESULTS: Overall mortality was twofold higher among people with TB compared with controls up to 15 years after TB diagnosis (hazard ratio [HR]: 2.18, 95% confidence interval [CI]: 2.06-2.29, P <0.0001). Danes with TB were three times more likely to die than migrants (adjusted HR: 3.13, 95% CI: 2.84-3.45, P <0.0001). Risk factors for death included living alone, being unemployed, having low income, and comorbidities such as mental illness with substance abuse, lung diseases, hepatitis, and HIV. TB was the most common cause of death (21%), followed by chronic obstructive pulmonary disease (7%), lung cancer (6%), alcoholic liver disease (5%), and mental illness with substance abuse (4%). CONCLUSION: People with TB had substantially inferior survival up to 15 years after TB diagnosis, in particular, socially disadvantaged Danes with TB with specific comorbidities. This may reflect unmet needs for enhanced treatment of other medical/social conditions during TB treatment.

Effectiveness evaluation of an antenatal care intervention addressing disparities to improve perinatal outcomes in Denmark: A nationwide register-based analysis of a cluster randomised controlled trial (MAMAACT).

OBJECTIVE: To evaluate whether MAMAACT, an antenatal care (ANC) intervention, aimed at reducing ethnic and social disparities in perinatal mortality, affected perinatal health outcomes. DESIGN: Cluster randomised controlled trial. SETTING: Nineteen of 20 maternity wards in Denmark. POPULATION: All newborn children within a pre-implementation period (2014-2017) or an implementation period (2018-2019) (n = 188 658). INTERVENTION: A 6-h training session for midwives in intercultural communication and cultural competence, two follow-up dialogue meetings, and health education materials for pregnant women on warning signs of pregnancy complications in six languages. METHODS: Nationwide register-based analysis of the MAMAACT cluster randomised controlled trial. Mixed-effects logistic regression models were used to estimate the change in outcomes from pre- to post-implementation in the intervention group relative to the control group. Results were obtained for the overall study population and for children born to immigrants from low- to middle-income countries, separately. Models were adjusted for confounders selected a priori. MAIN OUTCOME MEASURES: A composite perinatal mortality and morbidity outcome, including stillbirths, neonatal deaths, Apgar score <7, umbilical arterial pH < 7.0, admissions to a neonatal intensive care unit (NICU) >48 h, and NICU admissions for mechanical ventilation. Additional outcomes were the individual measures. RESULTS: The intervention increased the risk of the composite outcome (adjusted odds ratio [aOR] 1.16, 95% confidence interval [CI] 0.99-1.34), mainly driven by differences in NICU admission risk (composite outcome excluding NICU, aOR 0.98, 95% CI 0.84-1.14). The intervention slightly increased the risk of low Apgar score and decreased the risk of low arterial pH, reflecting, however, small differences in absolute numbers. Other outcomes were unchanged. CONCLUSIONS: Overall, the MAMAACT intervention did not improve the composite perinatal mortality and morbidity outcome (when excluding NICU admissions). The lack of effects may be due to contextual factors including organisational barriers in ANC hindering the midwives from changing practices.

Predicting mortality and visualizing health care spending by predicted mortality in Danes over age 65.

Health care expenditure in the last year of life makes up a high proportion of medical spending across the world. This is often framed as waste, but this framing is only meaningful if it is known at the time of treatment who will go on to die. We analyze the distribution of health care spending by predicted mortality for the Danish population over age 65 over the year 2016, with one-year mortality predicted by a machine learning model based on sociodemographics and use of health care services for the two years before entry into follow-up. While a reasonably good model can be built, extremely few individuals have high ex-ante probability of dying, and those with a predicted mortality of more than 50% account for only 2.8% of total health care expenditure. Decedents outspent survivors by a factor of more than ten, but compared to survivors with similar predicted mortality they spent only 2.5 times as much. Our results suggest that while spending in the last year of life is indeed high, this is nearly all spent in situations where there is a reasonable expectation that the patient can survive.

Having a ball: evaluating scoring streaks and game excitement using in-match trend estimation.

Many popular sports involve matches between two teams or players where each team have the possibility of scoring points throughout the match. While the overall match winner and result is interesting, it conveys little information about the underlying scoring trends throughout the match. Modeling approaches that accommodate a finer granularity of the score difference throughout the match is needed to evaluate in-game strategies, discuss scoring streaks, teams strengths, and other aspects of the game. We propose a latent Gaussian process to model the score difference between two teams and introduce the Trend Direction Index as an easily interpretable probabilistic measure of the current trend in the match as well as a measure of post-game trend evaluation. In addition we propose the Excitement Trend Index-the expected number of monotonicity changes in the running score difference-as a measure of overall game excitement. Our proposed methodology is applied to all 1143 matches from the 2019-2020 National Basketball Association season. We show how the trends can be interpreted in individual games and how the excitement score can be used to cluster teams according to how exciting they are to watch. Supplementary Information: The online version contains supplementary material available at 10.1007/s10182-022-00452-w.

Psilocybin-assisted therapy for reducing alcohol intake in patients with alcohol use disorder: protocol for a randomised, double-blinded, placebo-controlled 12-week clinical trial (The QUANTUM Trip Trial).

INTRODUCTION: Alcohol use disorder is a difficult-to-treat psychiatric disorder and a major burden on public health. Existing treatment efficacy is moderate, and relapse rates are high. Preliminary findings suggest that psilocybin, a psychedelic compound, can safely and reliably occasion highly meaningful experiences that may spur a positive change in drinking behaviour when administered in a therapeutic context. However, the efficacy of a single psilocybin administration and its potential neurobiological underpinnings still remain unknown. METHODS AND ANALYSIS: To establish efficacy, we will investigate the effects of psilocybin-assisted therapy versus placebo in a randomised, double-blinded, placebo-controlled 12-week clinical trial. Ninety treatment-seeking patients, aged 20-70 years, diagnosed with alcohol use disorder will be recruited from the community via advertisement and referrals from general practitioners or specialised treatment units. The psilocybin or placebo will be administered in accordance with a protocol for psychological support before, during and after the dosing. Outcome assessments will be carried out 1, 4, 8 and 12 weeks postdosing. The primary outcome is reduction in the percentage of heavy drinking days from baseline to follow-up at 12 weeks. Key secondary outcomes are as follows: (1) total alcohol consumption, (2) phosphatidyl-ethanol, an objective biomarker for alcohol, (3) plasma psilocin, the active metabolite, to establish a possible therapeutic range, (4) the acute subjective drug experience as a possible predictor of treatment outcome and (5) neuronal response to alcohol cues and cognitive flexibility within corticostriatal pathways by use of functional MR brain imaging 1-week postdosing. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Committee on Health Research Ethics of the Capital Region of Denmark (H-20043832). All patients will be provided oral and written information about the trial before screening. The study results will be disseminated by peer-review publications and conference presentations. TRIAL REGISTRATION NUMBER: EudraCT 2020-000829-55 and NCT05416229.

Causal inference for planning randomised critical care trials: Protocol for a scoping review.

BACKGROUND: Randomised clinical trials in critical care are prone to inconclusiveness owing, in part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous treatment effects. Planned predictive enrichment based on secondary critical care data (often very rich with respect to both data types and temporal granularity) and causal inference methods may help overcome these challenges, but no overview exists about their use to this end. METHODS: We will conduct a scoping review to assess the extent and nature of the use of causal inference from secondary data for planned predictive enrichment of randomised clinical trials in critical care. We will systematically search 10 general and specialty journals for reports published on or after 1 January 2018, of randomised clinical trials enrolling adult critically ill patients. We will collect trial metadata (e.g., recruitment period and phase) and, when available, information pertaining to the focus of the review (predictive enrichment based on causal inference estimates from secondary data): causal inference methods, estimation techniques and software used; types of patient populations; data provenance, types and models; and the availability of the data (public or not). The results will be reported in a descriptive manner. DISCUSSION: The outlined scoping review aims to assess the use of causal inference methods and secondary data for planned predictive enrichment in randomised critical care trials. This will help guide methodological improvements to increase the utility, and facilitate the use, of causal inference estimates when planning such trials in the future.

Exenatide once weekly for alcohol use disorder investigated in a randomized, placebo-controlled clinical trial.

BackgroundAlcohol use disorder (AUD) is a chronic, relapsing brain disorder that accounts for 5% of deaths annually, and there is an urgent need to develop new targets for therapeutic intervention. The glucagon-like peptide-1 (GLP-1) receptor agonist exenatide reduces alcohol consumption in rodents and nonhuman primates, but its efficacy in patients with AUD is unknown.MethodsIn a randomized, double-blinded, placebo-controlled clinical trial, treatment-seeking AUD patients were assigned to receive exenatide (2 mg subcutaneously) or placebo once weekly for 26 weeks, in addition to standard cognitive-behavioral therapy. The primary outcome was reduction in number of heavy drinking days. A subgroup also completed functional MRI (fMRI) and single-photon emission CT (SPECT) brain scans.ResultsA total of 127 patients were enrolled. Our data revealed that although exenatide did not significantly reduce the number of heavy drinking days compared with placebo, it significantly attenuated fMRI alcohol cue reactivity in the ventral striatum and septal area, which are crucial brain areas for drug reward and addiction. In addition, dopamine transporter availability was lower in the exenatide group compared with the placebo group. Exploratory analyses revealed that exenatide significantly reduced heavy drinking days and total alcohol intake in a subgroup of obese patients (BMI > 30 kg/m2). Adverse events were mainly gastrointestinal.ConclusionThis randomized controlled trial on the effects of a GLP-1 receptor agonist in AUD patients provides new important knowledge on the effects of GLP-1 receptor agonists as a novel treatment target in addiction.Trial registrationEudraCT: 2016-003343-11. ClinicalTrials.gov (NCT03232112).FundingNovavi Foundation; Research Foundation, Mental Health Services, Capital Region of Denmark; Research Foundation, Capital Region of Denmark; Ivan Nielsen Foundation; A.P. Moeller Foundation; Augustinus Foundation; Woerzner Foundation; Grosserer L.F. Foghts Foundation; Hartmann Foundation; Aase and Ejnar Danielsen Foundation; P.A. Messerschmidt and Wife Foundation; and Lundbeck Foundation.

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome.

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.

Social determinants of tuberculosis: a nationwide case-control study, Denmark, 1990-2018.

BACKGROUND: The global vision is a world free of tuberculosis (TB). Even in resource-rich TB low-incidence settings, we need more focus on the role of social risk factors to end the TB epidemic. METHODS: Nationwide, retrospective register-based, case-control study from 1990 to 2018, including all TB patients in Denmark ≥18 years old (n = 9581) matched 1:3 on sex and age with population controls. TB risk factors were assessed in logistic regression models and estimated by odds ratio (OR). RESULTS: All TB patients had considerably lower socio-economic status compared with controls (P < 0.0001). Among ethnic Danes, TB was mostly found among males, persons between 35 and 65 years, those living alone, those with low educational level, persons on social welfare benefits and those with low income. Conversely, for migrants, being younger, sex and living alone were less important, whereas having children was protective. In an adjusted multivariable regression model among Danes, key risk factors for TB were being on disability pension (OR = 2.7) and cash benefits (OR = 4.7). For migrants, fewer social risk factors increased TB risk, although low income and cash benefits did (OR = 3.1). CONCLUSION: Even today in a resourceful setting, socio-economic status drives disparities in health. In our study, multifactorial social deprivation was highly associated with TB. Especially household structure, education, employment and income were important risk factors that should be addressed in the future to accelerate TB control and end the TB epidemic.