DataSheet_1_Utilization of screening and treatment for osteoporosis among stroke survivors.docx

BackgroundStroke survivors are prone to osteoporosis and fractures. However, bone mineral density (BMD) testing and osteoporosis treatment were underutilized in patients with recent stroke. We aimed to examine whether stroke has an impact on the utilization of BMD testing and osteoporosis treatment as well as the determinants of their utilization in stroke patients using nationwide population-based data in Taiwan.MethodsWe identified patients aged 55 years and older who were hospitalized for hemorrhagic or ischemic stroke as the stroke cohort, and age- and sex-matched patients hospitalized for reasons other than stroke, fracture, or fall as the non-stroke cohort. We used the Fine-Gray sub-distribution hazard competing risk regression model to determine the predictors for BMD testing and osteoporosis treatment.ResultsA total of 32997 stroke patients and 32997 age- and sex-matched controls comprised the stroke and non-stroke cohorts, respectively. BMD testing and osteoporosis treatment were performed in 1.0% and 5.2% of the stroke patients, respectively, within one year after hospitalization while these measures were performed in 0.8% and 4.7% of the controls. Stroke patients were more likely to receive BMD testing (adjusted hazard ratio [HR] 1.33; 95% confidence interval [CI] 1.11–1.58) and osteoporosis treatment (adjusted HR 1.19; 95% CI 1.11–1.29). Female sex, osteoporosis, prior BMD testing, and low-trauma fractures after stroke increased the likelihood of using BMD testing and osteoporosis treatment whereas greater stroke severity reduced the likelihood of receiving both measures.ConclusionsBoth BMD testing and osteoporosis treatment were underutilized among stroke survivors even though they had a higher chance of receiving both measures than non-stroke patients.</p

Summary of the population parameters.

<p><i>β</i>₀ is the logit-transformation prevalence of the outcome disease in people with homozygous major and the moderators in the study population. <i>β</i>₁ is the log-transformation OR of the allele effect in people without moderators. <i>β</i>₂ is the log-transformation OR of moderators on the disease in people with homozygous major, and <i>β</i>₃ is the log-transformation moderator effect. <i>F</i>_st is the frequency difference among various studies, and <i>P</i>₇, <i>P</i>₈, and <i>P</i>₉ are the proportions of moderators status in people with homozygous major [<i>p</i>(<i>x</i>₂ = 1|<i>x</i>₁ = 0)], people with heterozygous genotype [<i>p</i>(<i>x</i>₂ = 1|<i>x</i>₁ = 1)], and people with homozygous minor [<i>p</i>(<i>x</i>₂ = 1|<i>x</i>₁ = 2)], respectively.</p><p>Summary of the population parameters.</p

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

<div><p>Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.</p></div

Confidence interval coverage rate of the allele effect in people without a moderator (<i>β</i>₁) and people with a moderator (<i>β</i>₁ + <i>β</i>₃) in individual patient data regression analysis.

<p>The model names, “Basic,” “Minor rare,” “Serious rare,” “Minor independence,” and “Serious independence” indicate the models, “Basic,” “Minor violation of rare disease assumption,” “Serious violation of rare disease assumption,” “Minor violation of independence assumption,” and “Serious violation of independence assumption,” respectively. <i>F</i>_st is the parameter of frequency difference among various studies. The X-axis represents the confidence interval of the moderator effect (<i>β</i>₃); the Y-axis represents the 95% confidence interval coverage rate. The red bar represents the 95% confidence interval coverage rate of the allele effect in people without a moderator (<i>β</i>₁); the blue bar represents the 95% confidence interval coverage rate of the allele effect in people with a moderator (<i>β</i>₁ + <i>β</i>₃).</p

Confidence interval coverage rate of the allele effect in people without a moderator (<i>β</i>₁) and people with a moderator (<i>β</i>₁ + <i>β</i>₃) using our proposed method.

<p>The model names, “Basic,” “Minor rare,” “Serious rare,” “Minor independence,” and “Serious independence” indicate the models, “Basic,” “Minor violation of rare disease assumption,” “Serious violation of rare disease assumption,” “Minor violation of independence assumption,” and “Serious violation of independence assumption,” respectively. <i>F</i>_st is the parameter of frequency difference among various studies. The X-axis represents the confidence interval of the moderator effect (<i>β</i>₃); the Y-axis represents the 95% confidence interval coverage rate. The red bar represents the 95% confidence interval coverage rate of the allele effect in people without a moderator (<i>β</i>₁); the blue bar represents the 95% confidence interval coverage rate of the allele effect in people with a moderator (<i>β</i>₁ + <i>β</i>₃).</p

Gender-stratified multiple Cox proportional hazards regression analysis of the RLS risk in IBS patients with antidepressant use.

Gender-stratified multiple Cox proportional hazards regression analysis of the RLS risk in IBS patients with antidepressant use.</p

Cumulative incidence of RLS in the IBS and non-IBS groups, compared with a log-rank test.

A statistically significant difference exists between the IBS and non-IBS groups (p<0.001).</p

The cox proportion hazard regression model of RLS in IBS patients.

The cox proportion hazard regression model of RLS in IBS patients.</p

Cox model measured HRs and 95% CIs of the RLS associated with IBS and covariates.

Cox model measured HRs and 95% CIs of the RLS associated with IBS and covariates.</p

Baseline demographic factors and comorbidity of the study participants.

Baseline demographic factors and comorbidity of the study participants.</p