The use of regression methods for the investigation of trends in suicide rates in Hungary between 1963 and 2011

PURPOSE: Suicide rates in Hungary have been analyzed from different aspects in recent decades. However, only descriptive rates have been reported. The aim of our epidemiological study was to characterize the pattern of annual rates of suicide in Hungary during the period 1963-2011 by applying advanced statistical methods. METHODS: Annual suicide rates per 100,000 population (>6 years) for gender, age group and suicide method were determined from published frequency tables and reference population data obtained from the Hungarian Central Statistical Office. Trends and relative risks of suicide were investigated using negative binomial regression models overall and in stratified analyses (by gender, age group and suicide method). Joinpoint regression analyses were additionally applied to characterize trends and to find turning points during the period 1963-2011. RESULTS: Overall, 178,323 suicides (50,265 females and 128,058 males) were committed in Hungary during the investigated period. The risk of suicide was higher among males than females overall, in all age groups and for most suicide methods. The annual suicide rate exhibited a significant peak in 1982 and remained basically constant after 2006. Different segmented patterns were observed for the suicide rates in the various age groups. CONCLUSIONS: Suicide rates revealed segmented linear pattern. This is the first detailed trend analysis with risk estimates obtained via joinpoint and negative binomial regression methods simultaneously for age-specific suicide frequencies in Hungary

Heterogeneous effect of gestational weight gain on birth weight: quantile regression analysis from a population-based screening

PURPOSE: Classical regression models might give an incomplete picture of the associations between predictors and outcomes. We investigated associations between gestational weight gain (GWG) and birth weight along the entire birth weight distribution with quantile regression and estimated effects of hypothetical prevention strategies. METHODS: The GWG-birth weight association was analyzed using quantile and classical regression models on data from a population-based gestational diabetes screening (n = 4760) at the Szent Imre Teaching Hospital in Budapest, Hungary (2002-2005). Birth weight distributions were modeled based on hypothetical GWG changes. RESULTS: At a body mass index of 20 kg/m(2), a 1-kg difference in GWG was associated with a 14.2 g (95% confidence interval, 10.0-20.9) higher birth weight at the fifth percentile of the birth weight distribution and a 29.0 g (21.3-35.6) higher birth weight at the 95th percentile. The coefficient from linear regression was 20.7 (17.5-24.0). Estimates differed modestly between the two regressions at a body mass index of 30 kg/m(2). A population-wide 2-kg decrease in GWG would rather affect the risk of macrosomia (-1.8%) than that of low birth weight (+0.4%). In contrast, a 3-kg decrease in GWG among overweight and obese women would lower macrosomia more modestly (-0.8%). CONCLUSIONS: A population-wide lowering of GWG would lead to greater improvements in the right tail of the birth weight distribution