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The seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as 'non-flatness' throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables. [ABSTRACT FROM AUTHOR]

The Nordic countries have comprehensive, population-based health and medical registries linkable on individually unique personal identity codes, enabling complete long-term follow-up. The aims of this study were to describe the NorTwinCan cohort established in 2010 and assess whether the cancer mortality and incidence rates among Nordic twins are similar to those in the general population. We analyzed approximately 260,000 same-sexed twins in the nationwide twin registers in Denmark, Finland, Norway and Sweden. Cancer incidence was determined using follow-up through the national cancer registries. We estimated standardized incidence (SIR) and mortality (SMR) ratios with 95% confidence intervals (CI) across country, age, period, follow-up time, sex and zygosity. More than 30,000 malignant neoplasms have occurred among the twins through 2010. Mortality rates among twins were slightly lower than in the general population (SMR 0.96; CI 95% [0.95, 0.97]), but this depends on information about zygosity. Twins have slightly lower cancer incidence rates than the general population, with SIRs of 0.97 (95% CI [0.96, 0.99]) in men and 0.96 (95% CI [0.94, 0.97]) in women. Testicular cancer occurs more often among male twins than singletons (SIR 1.15; 95% CI [1.02, 1.30]), while cancers of the kidney (SIR 0.82; 95% CI [0.76, 0.89]), lung (SIR 0.89; 95% CI [0.85, 0.92]) and colon (SIR 0.90; 95% CI [0.87, 0.94]) occur less often in twins than in the background population. Our findings indicate that the risk of cancer among twins is so similar to the general population that cancer risk factors and estimates of heritability derived from the Nordic twin registers are generalizable to the background populations. [ABSTRACT FROM AUTHOR]

We investigated genetic and environmental correlations and gene by environment interactions (GxE) between depressive symptoms measured by the Beck Depression Inventory (BDI) and quantity smoked measured by number of cigarettes smoked per day (CPD) using quantitative genetic modeling. The population-based sample consisted of 12,063 twin individuals from the Finnish Twin Cohort Study. Bivariate Cholesky decomposition revealed that the phenotypic correlation (r = 0.09) between BDI and CPD was explained by shared genetic (r g = 0.18) and environmental (r e = 0.08) factors. GxE models incorporating moderator effects were built by using CPD as trait and BDI as moderator and vice versa. The importance of the genetic variance component increased with increasing moderator value in both models. Thus, the influence of genetic effects on variance of smoking quantity was enhanced in individuals with elevated depression score and vice versa; the genetic effects on depression variance were potentiated among heavy smokers. In conclusion, shared genetic and environmental factors as well as GxE underlie the association of smoking with depression. [ABSTRACT FROM AUTHOR]

Studies have suggested both adverse and protective associations of obesity with depressive symptoms. We examined the contribution of environmental and heritable factors in this association. Participants were same-sex twin pairs from two population-based twin cohort studies, the Older Finnish Twin Cohort (n = 8,215; mean age = 44.1) and the US Midlife Development in the United States (MIDUS; n = 1,105; mean age = 45.1). Body mass index (BMI) was calculated from self-reported height and weight. Depressive symptoms were assessed using Beck's Depression Inventory (BDI; Finnish Twin Cohort), and by negative and positive affect scales (MIDUS). In the Finnish Twin Cohort, higher BMI was associated with higher depressive symptoms in monozygotic (MZ) twins (B = 2.01, 95% CI = 1.0, 3.0) and dizygotic (DZ) twins (B = 1.17, 0.5, 1.9) with BMI >22. This association was observed in within-pair analysis in DZ twins (B = 1.47, CI = 0.4, 2.6) but not in within-pair analysis of MZ twins (B = 0.03, CI = -1.9, 2.0). Consistent with the latter result, a bivariate genetic model indicated that the association between higher BMI and higher depressive symptoms was largely mediated by genetic factors. The results of twin-pair analysis and bivariate genetic model were replicated in the MIDUS sample. These findings suggest an association between obesity and higher depressive symptoms, which is largely explained by shared heritable biological mechanisms. [ABSTRACT FROM AUTHOR]

Pregnancy- and birth-related factors may have an effect on handedness. Compared with singletons, twins have a lower birth weight, shorter gestational age, and are at higher risk for birth complications. We tested whether the prevalence of left-handedness is higher among twins than singletons, and if so, whether that difference is fully explained by pregnancy and birth-related differences between twins and singletons. We analyzed Finnish population-based datasets; included were 8,786 twins and 5,892 singletons with information on birth weight (n = 12,381), Apgar scores (n = 11,129), and gestational age (n = 11,811). Two twin cohorts were involved: FinnTwin12 included twins born during 1983–1987, and FinnTwin16 included twins born during 1974–1979. We had two comparison groups of singletons: 4,101 individuals born during 1986–1988 and enrolled in the Helsinki Ultrasound Trial, and 1,791 individuals who were partners of FinnTwin16 twins. We used logistic regression models with writing hand as the outcome for comparison and evaluating effects of covariates. Left-handedness was more common in twins (9.67%) than in singletons (8.27%; p = .004). However, Apgar scores were associated with handedness, and after controlling for covariates, we found no difference in the prevalence of left-handedness between twins and singletons. Increased left-handedness among twins, often reported by others, was evident in our data, but only among our older twin cohorts, and that association disappeared after removing effects of perinatal covariates. [ABSTRACT FROM PUBLISHER]