These data suggest a possible need to set lower BMI cutoffs for Asians, but where to draw the line is a complex issue.

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This study assessed the association between second-generation antipsychotic medications and risk of pneumonia requiring hospitalization in patients with schizophrenia because the evidence is limited in the population. We enrolled a nationwide cohort of 33,024 inpatients with schizophrenia ranged in age from 18 to 65 years, who were derived from the National Health Insurance Research Database in Taiwan from 2000 to 2008. Cases (n = 1741) were defined as patients who developed pneumonia after their first psychiatric admissions. Risk set sampling was used to match each case with 4 controls by age, sex, and the year of the first admission based on nested case-control study. Antipsychotic exposure was categorized by type, duration, and daily dose, and the association between exposure and pneumonia was assessed using conditional logistic regression. We found that current use of clozapine (adjusted risk ratio = 3.18, 95% CI: 2.62-3.86, P < .001) was associated with a dose-dependent increase in the risk. Although quetiapine, olanzapine, zotepine, and risperidone were associated with increased risk, there was no clear dose-dependent relationship. Amisulpride was associated with a low risk of pneumonia. The use of clozapine combined with another drug (olanzapine, quetiapine, zotepine, risperidone, or amisulpride), as assessed separately, was associated with increased risk for pneumonia. In addition, with the exception of amisulpride, each drug was associated with increased risk for pneumonia at the beginning of treatment. Clinicians who prescribe clozapine to patients with schizophrenia should closely monitor them for pneumonia, particularly at the start of therapy and when clozapine is combined with other antipsychotics.

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Introduction: For never-smokers (smoked <100 lifetime cigarettes), lung cancer (LC) has emerged as an important issue. We aimed to investigate the effects of prevalence changes in tobacco smoking and particulate matter (PM) 2.5 (PM 2.5) levels on LC in Taiwan, in relation to contrasting PM 2.5 levels, between Northern Taiwan (NT) and Southern Taiwan (ST). Methods: We reviewed 371,084 patients with LC to assess smoking prevalence and correlations between the incidence of adenocarcinoma lung cancer (AdLC) and non-AdLC. Two subsets were selected to assess different AdLC stage trends and the effect of PM 2.5 on survival of patients with AdLC. Results: From 1995 to 2015, the proportion of male adult ever-smokers decreased from 59.4% to 29.9% whereas the female smoking rate remained low (3.2% to 5.3%). AdLC incidence in males and females increased from 9.06 to 23.25 and 7.05 to 24.22 per 100,000 population, respectively. Since 1993, atmospheric visibility in NT improved (from 7.6 to 11.5 km), but deteriorated in ST (from 16.3 to 4.2 km). The annual percent change in AdLC stages IB to IV was 0.3% since 2009 (95% confidence interval [CI]:-1.9%-2.6%) in NT, and 4.6% since 2007 (95% CI: 3.3%-5.8%) in ST; 53% patients with LC had never smoked. Five-year survival rates for never-smokers, those with EGFR wildtype genes, and female patients with AdLC were 12.6% in NT and 4.5% in ST (hazard ratio: 0.79, 95% CI: 0.70-0.90). Conclusions: In Taiwan, greater than 50% of patients with LC had never smoked. PM 2.5 level changes can affect AdLC incidence and patient survival.

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The future of genetic studies of complex human diseases will rely more and more on the epidemiologic association paradigm. The author proposes to scan the genome for disease-susceptibility gene(s) by testing for deviation from Hardy-Weinberg equilibrium in a gene bank of affected individuals. A power formula is presented, which is very accurate as revealed by Monte Carlo simulations. If the disease-susceptibility gene is recessive with an allele frequency of < or = 0.5 or dominant with an allele frequency of > or = 0.5, the number of subjects needed by the present method is smaller than that needed by using a case-parents design (using either the transmission/disequilibrium test or the 2-df likelihood ratio test). However, the method cannot detect genes with a multiplicative mode of inheritance, and the validity of the method relies on the assumption that the source population from which the cases arise is in Hardy-Weinberg equilibrium. Thus, it is prone to produce false positive and false negative results. Nevertheless, the method enables rapid gene hunting in an existing gene bank of affected individuals with no extra effort beyond simple calculations.

A systematic review and meta-analysis is an important step in evidence synthesis. The current paradigm for meta-analyses requires a presentation of the means under a random-effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta-analysis. Prediction intervals show the range of true effects in future studies and have been advocated to be regularly presented. Most commonly, prediction intervals are estimated assuming that the underlying heterogeneity follows a normal distribution, which is not necessarily appropriate. In this article, we provide a simple method with a ready-to-use spreadsheet file to estimate prediction intervals and predictive distributions nonparametrically.Simulation studies show that this new method can provide approximately unbiased estimates compared with the conventional method. We also illustrate the advantage and real-world significance of this approach with a meta-analysis evaluating the protective effect of vaccination against tuberculosis. The nonparametric predictive distribution provides more information about the shape of the underlying distribution than does the conventional method.KEYWORDS meta-analysis, normality assumption, prediction interval, predictive distribution | INTRODUCTIONA systematic review and meta-analysis can integrate the information from many studies and is becoming more important in a diverse range of fields including biomedical, ecological, social, and behavioral sciences. [1][2][3] The fixed-effect model in meta-analyses assumes a common underlying effect, while the random-effects model allows for heterogeneity across the included studies. The random-effects model is usually preferred, because the included studies often differ in various ways. The current standard of meta-analysis therefore includes the quantification of heterogeneity with b τ 2 or I 2 . 4 A test of homogeneity such as the Q statistic 5 had been previously used but was considered not providing a relevant summary of heterogeneity. 4,6-8 If heterogeneity is present, a randomeffects model is recommended. An estimate of the mean of the underlying random-effects distribution is routinely