Statistical analysis of the seasonal variation in the twinning rate

Seasonality and multiple maternities: Comparisons between different models

Seasonality of demographic data has been of great interest. The seasonality depends mainly on 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 analyse the number of births (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, 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. 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, non-significant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. The estimated monthly models chosen are approximately orthogonal and they have little influence on the parameter estimates. Exact orthogonality should be obtained if the data are equidistant, that is, if the months are of equal length (e.g. 30 days), corresponding to 30^∘. Exactly equidistant data can be observed when circadian rhythms (24 h) are studied. In this study, we compare seasonal models with models with exact orthogonality.

Demographic Seasonality

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.

Studies of the Seasonal Pattern of Multiple Maternities

The seasonality of population data has been of great interest in demographic studies. When seasonality is analyzed, the population at risk plays a central role. In a study of the monthly number of births and deaths, the population at risk is the product of the size of the population and the length of the month. Usually, the population can be assumed to be constant, and consequently, the population at risk is proportional to the length of the month. Hence, the number of cases per day has to be analyzed. If one studies the seasonal variation in twin or multiple maternities, the population at risk is the total number of monthly confinements, and the study should be based on the rates of the multiple maternities. Consequently, if one considers monthly twinning rates, the monthly number of birth data is eliminated and one obtains an unaffected seasonality measure of the twin maternities. The strength of the seasonality is measured by a chi-squared test or by the standard deviation. When seasonal models are applied, one must pay special attention to how well the model fits the data. If the goodness of fit is poor, it can erroneously result in a statement that the seasonality is slight, although the observed seasonal fluctuations are marked.

The large contribution of twins to neonatal and post-neonatal mortality in The Gambia, a 5-year prospective study

BACKGROUND

A high twinning rate and an increased risk of mortality among twins contribute to the high burden of infant mortality in Africa. This study examined the contribution of twins to neonatal and post-neonatal mortality in The Gambia, and evaluated factors that contribute to the excess mortality among twins.

METHODS

We analysed data from the Basse Health and Demographic Surveillance System (BHDSS) collected from January 2009 to December 2013. Demographic and epidemiological variables were assessed for their association with mortality in different age groups.

RESULTS

We included 32,436 singletons and 1083 twins in the analysis (twining rate 16.7/1000 deliveries). Twins represented 11.8 % of all neonatal deaths and 7.8 % of post-neonatal deaths. Mortality among twins was higher than in singletons [adjusted odds ratio (AOR) 4.33 (95 % CI: 3.09, 6.06) in the neonatal period and 2.61 (95 % CI: 1.85, 3.68) in the post-neonatal period]. Post-neonatal mortality among twins increased in girls (P for interaction = 0.064), being born during the dry season (P for interaction = 0.030) and lacking access to clean water (P for interaction = 0.042).

CONCLUSION

Mortality among twins makes a significant contribution to the high burden of neonatal and post-neonatal mortality in The Gambia and preventive interventions targeting twins should be prioritized.

Secular trends in seasonal variation in birth weight

BACKGROUND

Many environmental factors have been shown to influence birth weight (BW) and one of these are season of birth.

AIM

The aim of the present study was to investigate the seasonal variation in BW in Denmark during 1936-1989, and to see if the variation could be explained by sunshine exposure during pregnancy.

METHODS

The study population was selected from the Copenhagen School Health Records Register and included 276 339 children born between 1936 and 1989. Seasonal variation was modeled using a non-stationary sinusoidal model that allowed the underlying trend in BW and the amplitude and phase of the yearly cycles to change.

RESULTS

There was a clear seasonal pattern in BW which, however, changed gradually across the study period. The highest BWs were seen during fall (September - October) from 1936 to 1963, but a new peak gradually grew from the early 1940s during early summer (May - June) and became the highest from 1964 to 1989. The amplitude of the fall peak started at 25.5 (95%CI 24.6; 25.9) grams and gradually disappeared. The amplitude of the early summer peak gradually arose from nothing to a peak of 18.6 (95%CI 17.7; 19.6) grams in the mid 1980s where it started to decrease again. Sunshine did not explain the seasonal variation in BW.

CONCLUSION

There was a clear seasonal pattern in BW in Denmark 1936-1989, which however changed across the study period. Throughout the study period we observed a peak in BW during the fall, but gradually, starting in the early 1940s, an additional early summer peak emerged and became the highest from 1964 and onwards.

Twinning and Birth Weight in the Israeli Jewish Versus Muslim Maternities

Ethnicity differences account for genetic, environmental, lifestyle, and reproductive variables, influencing the rate of twinning (Nylander, 1981). Frequently, ethnic differences correlate with variable perinatal care leading to differences in outcome. Free access to antenatal care, and to facilities for delivery and neonatal care is available for the entire population in Israel, and therefor differences attributed to levels of medical care are practically negligible. We previously evaluated the overall relationship between ethnicity and outcome in a popula-tion-based cohort of mothers of twins (Goldman et al., 2001). However, the overall comparison may have masked some differences that could be present. The purpose of this study was to evaluate whether ethnicity is associated with differences in perinatal outcome in randomly selected, matched-controlled Israeli Jewish and Muslim mothers of twins.

Dizygotic twinning

The tendency to conceive spontaneous dizygotic (DZ) twins is a complex trait with important contributions from both environmental factors and genetic disposition. Twins are relatively common and occur on average 13 times per 1000 maternities, though the twinning frequency varies over time and geographic location. This variation is mostly attributed to the differences in DZ twinning rate, since the monozygotic twinning rate is relatively constant. DZ twinning is in part under genetic control, with mothers of DZ twins reporting significantly more female family members with DZ twins than mothers of monozygotic twins. Maternal factors such as genetic history, advanced age and increased parity are known to increase the risk of DZ twins. Recent research confirmed that taller mothers and mothers with a high body mass index (30>) are at greater risk of DZ twinning. Seasonality, smoking, oral contraceptive use and folic acid show less convincing associations with twinning. Genetic analysis is beginning to identify genes contributing to the variation in twinning. Mutations in one of these genes (growth differentiation factor 9) are significantly more frequent in mothers of DZ twins. However, the mutations are rare and only account for a small part of the genetic contribution for twinning.

Variation in levels of serum inhibin B, testosterone, estradiol, luteinizing hormone, follicle-stimulating hormone, and sex hormone-binding globulin in monthly samples from healthy men during a 17-month period: possible effects of seasons

To obtain information on the scale of the intraindividual variation in testicular hormone, blood samples for inhibin B determination were collected monthly in 27 healthy male volunteers during a 17-month period. In addition, the traditional reproductive hormones FSH, LH, testosterone, estradiol, and SHBG were measured. The intraindividual variation in inhibin B over the study period was, on the average, 10%, corresponding to the assay variation of the inhibin B assay, indicating that most of the observed day to day variation in inhibin B levels in men could be explained by assay variation. A seasonal variation was observed in LH and testosterone levels, but not in the levels of the other hormones. The seasonal variation in testosterone levels could be explained by the variation in LH levels. The seasonal variation in LH levels seemed to be related to the mean air temperature during the month before blood sampling, but not to the length of daylight or the hours of sunshine. In conclusion, our data showed that day to day levels of inhibin B are relatively constant in men and do not seem to be influenced by seasonal factors. In contrast, we found a seasonal variation in LH and testosterone levels in men. The peak levels of both LH and testosterone were observed during June-July, with minimum levels present during winter-early spring. Air temperature, rather than light exposure, seems to be a possible climatic variable explaining the seasonal variation in LH levels.

Regional, temporal, and seasonal variations in births and deaths: the effects of famines

A knowledge of the seasonal variation in births and deaths during normal years is important for analyses of the effects of wars, famines, epidemics or similar privations on these two variables. In studies of seasonality, multiple trigonometric regression models are more flexible than the simple sine curve. The seasonal variation in mortality in Iceland, 1856-1990, shows a strong secular decrease, and a connection between this and the epidemiological transition is considered. As a consequence of the severe famine in Finland in 1867-68, the mortality for the whole year 1868 was almost four times as high as during normal years, and the seasonality of the mortality was even more accentuated. The birth rate in Finland during 1868 was about 70 percent of that during normal years and showed an aberrant seasonality, with a strong trough from October 1868 to February 1869 (fewer conceptions between January and May 1868, when the food shortage was severe).