Children with special health care needs (SHCN) due to a chronic health condition perform more poorly at school compared to their classmates. There is still little knowledge on the causal pathways and ...which factors could be targeted by interventions. We, therefore, investigated school absenteeism in children with SHCN compared to their peers.
This study was based on data from the German population-based prospective cohort study ikidS (German for: I will start school). Children with SHCN were identified by the Children with Special Health Care Needs screener that captures five consequences of physical or mental chronic health conditions: (1) use or need of prescription medication, (2) above average use or need of medical, mental health, or educational services, (3) functional limitations compared with others of the same age, (4) use or need of specialized therapies, and (5) treatment or counseling for emotional, behavioral, or developmental problems. School absenteeism was defined as days absent from school due to illness during first grade and was reported by classroom teachers. Associations between SHCN consequences and school absenteeism were investigated by negative binomial regression models. Effect estimates were adjusted for confounding variables identified by a causal framework and directed acyclic graphs.
1,921 children (mean age at follow-up 7.3 years, standard deviation 0.3; 49% females) were included; of these, 14% had SHCN. Compared to their classmates, children with SHCN had more days absent (adjusted rate ratio: 1.37; 95% confidence interval 1.16, 1.62). The effect was strongest among children with i) functional limitations, ii) treatment or counseling for emotional, behavioral, or developmental problems, and iii) those who experienced two or more SHCN consequences.
Children with SHCN have higher school absenteeism, which could-at least partly-explain their poorer school performance and lower educational attainment. SHCN-specific targeted interventions may reduce the adverse effects of SHCN on educational outcomes in children.
Depression has been associated with increased inflammation. However, only few large-scale, prospective studies have evaluated whether inflammation leads to new cases of depression and whether this ...association can be found in men and women. Longitudinal data of N = 10,357 adult participants with no evidence of depression at baseline (based on Patient Health Questionnaire (PHQ-9), lifetime diagnoses, and current antidepressant medication) were evaluated for depression 5 years later. Multivariate logistic regression models were used to predict the onset of depression based on C-reactive protein (CRP) and white blood cell count (WBC). We used interaction terms and separate analyses in men and women to investigate gender-dependent associations. Based on both markers, inflammation was predictive of new cases of depression 5 years later, even when adjusting for sociodemographic, physical health, health behavior variables, and baseline depression symptoms. As established by interaction terms and separate analyses, inflammatory markers were predictive of depression in men, but not in women. Additional predictors of new onset of depression were younger age, loneliness, smoking (only in men), cancer and less alcohol consumption (only in women). The study indicates gender differences in the etiology of depressive disorders within the community, with a greater role of physical factors in men.
Abstract
Context
The structure of the human leucocyte antigen (HLA) peptide-binding clefts strongly contributes to monoglandular and polyglandular autoimmunity (AP).
Objective
To investigate the ...impact of amino acid polymorphisms on the peptide-binding interactions within HLA class II and its association with AP.
Design
Immunogenetic study.
Setting
Tertiary referral center for autoimmune endocrine diseases.
Subjects
587 subjects with AP, autoimmune thyroid disease (AITD), type 1 diabetes (T1D), and healthy unrelated controls were typed for HLA class II.
Methods
Amino acids within the peptide binding cleft that are encoded by HLA class II exon 2 were listed for all codon positions in all subjects. Overall comparisons between disease and control groups with respect to allele distribution at a given locus were performed by assembling rare alleles applying an exact Freeman Halton contingency table test with Monte-Carlo P values based on 150 000 samples.
Results
The Monte Carlo exact Fisher test demonstrated marked differences in all 3 loci, DQA1, DQB1, and DRB1 (P < .0001) between AP and both AITD and controls, as well as between AP type II (Addison’s disease as a major endocrine component) and AP type III (T1D + AITD). Differences were also noted between AP and T1D pertaining to the DRB1 allele (P < .041). Seven amino acid positions, DRB1-13, DRB1-26, DRB1-71, DRB1-74, DQA1-47, DQA1-56, and DQB1-57, significantly contributed to AP. Five positions in DQA1 (11, 47, 50, 56, and 69) completely correlated (P < .0001).
Conclusion
Amino acid polymorphisms within HLA class II exon 2 mediate the AP risk and differentiate between thyroid and polyglandular autoimmunity.
The glycoprotein chromogranin A (CgA) is expressed by endocrine and neuroendocrine cells. High levels of serum CgA serve as markers of neuroendocrine tumors (NET), but its role in autoimmunity has ...not been assessed.
To investigate CgA utility as a marker of endocrine autoimmunity.
CgA serum levels were evaluated in 807 consecutive unselected participants (cross-sectional study) with the time-resolved amplified cryptate emission technology.
Serum CgA concentrations were increased in 66%, 39%, 38%, and 24% of patients with NET, type 1 diabetes (T1D), autoimmune gastritis (AG) and autoimmune polyendocrinopathy (AP), respectively. Compared with healthy participant controls (C), the odds of positive CgA measurement were up to 28 times higher in the disease groups. In detail, the odds ratios (ORs) for positive CgA levels were 27.98, 15.22, 7.32 (all P < 0.0001) and 3.89 (P = 0.0073) in patients with NET, T1D, AG, and AP, respectively. In AG, CgA and serum gastrin correlated positively (r = 0.55; P < 0.0001). The area under the receiver operating characteristic curve to predict AG was higher for parietal cell antibody (PCA) positivity than for CgA (0.84 vs 0.67; P < 0.0001). However, in combination with PCA and intrinsic factor autoantibodies, CgA independently improved prediction of AG (OR 6.5; P = 0.031). An impact of age on CgA positivity and on CgA value was detected (P < 0.0001) while current smoking significantly increased CgA serum levels by 25% (P = 0.0080).
CgA qualifies as a novel biomarker for T1D, AP, and AG.
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results ...can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all‐cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all‐cause hazard within each group is given by the sum of the cause‐specific hazards corresponding to the individual components. A natural extension of the standard all‐cause hazard ratio can be defined by a “weighted all‐cause hazard ratio” where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all‐cause hazard ratio then corresponds to the standard all‐cause hazard ratio. To identify the cause‐specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all‐cause hazard ratio by theoretical considerations, Monte‐Carlo simulations, and by means of a real clinical trial example.
Background
The assessment of pressure pain has become an integral part in pain research. The distribution of pressure under a plunger can be uneven. However, measurements based on conventional ...devices show the applied force or mean pressure, failing to take local pressure peaks into account. Our main question was whether peak pressures under the probe are responsible for pain onset.
Methods
A force‐controlled algometer was fitted with a newly developed pressure‐indicating film. Pressure pain thresholds (PPTs) of 100 healthy subjects (57 men, age 18–66 years) were assessed at 29 sites across the body. Each site was measured three times, nonconsecutively and presented in randomized order. Forty subjects were manual labourers.
Results
Pressure distributions on hard tissue (bone) were more heterogeneous and showed more prominent peaks beneath the probe when reaching the PPT. Soft tissue (e.g. muscle) created a distinct distribution, with higher pressure especially around the corners of the probe. A high variability of PPTs between subjects and different measurement sites was observed. Men as well as manual labourers had comparatively higher adjusted pressure pain thresholds (force and pressure).
Conclusions
Peak pressures could be relevant for pain onset and should be accounted for in mechanical pain studies. The probe, indentation depth and tissue properties have a major impact on pressure distributions and may therefore affect the perception of pressure pain. Due to higher intra‐individual differences regarding peak pressures at the spinous processes, breastbone, forehead and abdomen caution are needed when interpreting those sites.
Significance
This study adds some important considerations for the use of pressure algometers. It was found that during pressure pain thresholds readings distinct peak pressure profiles could arise, which may influence the perception of pain. Peak pressure could be another contributing factor, which may explain some of the high variability in pressure pain readings.
In network meta-analysis, several alternative treatments can be compared by pooling the evidence of all randomised comparisons made in different studies. Incorporated indirect conclusions require a ...consistent network of treatment effects. An assessment of this assumption and of the influence of deviations is fundamental for the validity evaluation.
We show that network estimates for single pairwise treatment comparisons can be approximated by the evidence of a subnet that is decomposable into independent paths. Path-based estimates and the estimate of the residual evidence can be used with their contribution to the network estimate to set up a forest plot for the consistency assessment. Using a network meta-analysis of twelve antidepressants and controlled perturbations in the real and constructed consistent data, we discuss the consistency assessment by the independent path decomposition in contrast to an approach using a recently presented graphical tool, the net heat plot. In addition, we define influence functions that describe how changes in study effects are translated into network estimates.
While the consistency assessment by the net heat plot comprises all network estimates, an independent path decomposition and visualisation in a forest plot is tailored to one specific treatment comparison. It allows for the recognition as to whether inconsistencies between different paths of evidence and outlier effects do affect the considered treatment comparison.
The approximation of the network estimate for a single comparison by the evidence of a subnet and the visualisation of the decomposition into independent paths provide the applicability of a graphical validation instrument that is known from classical meta-analysis.
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