Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age group–specific disease incidence rates and abridged life tables are examples of binned data. We propose a versatile method for ungrouping histograms that assumes that only the underlying distribution is smooth. Because of this modest assumption, the approach is suitable for most applications. The method is based on the composite link model, with a penalty added to ensure the smoothness of the target distribution. Estimates are obtained by maximizing a penalized likelihood. This maximization is performed efficiently by a version of the iteratively reweighted least-squares algorithm. Optimal values of the smoothing parameter are chosen by minimizing Akaike's Information Criterion. We demonstrate the performance of this method in a simulation study and provide several examples that illustrate the approach. Wide, open-ended intervals can be handled properly. The method can be extended to the estimation of rates when both the event counts and the exposures to risk are grouped.

Diabetes reduces semen quality and increasingly occurs during reproductive years. Whether diabetes medications have glucose-independent effects on the male reproductive system is uncertain. This st... Visual Preconception Antidiabetic Drugs in Men and Birth Defects in Offspring. Diabetes reduces semen quality and increasingly occurs during reproductive years. Whether diabetes medication... Background: Diabetes reduces semen quality and increasingly occurs during reproductive years. Diabetes medications, such as metformin, have glucose-independent effects on the male reproductive syst...

Background: Of all lifestyle behaviours, smoking caused the most deaths in the last century. Because of the time lag between the act of smoking and dying from smoking, and because males generally take up smoking before females do, male and female smoking epidemiology often follows a typical double wave pattern dubbed the ‘smoking epidemic’. How are male and female deaths from this epidemic differentially progressing in high-income regions on a cohort-by-age basis? How have they affected male-female survival differences? Methods: We used data for the period 1950–2015 from the WHO Mortality Database and the Human Mortality Database on three geographic regions that have progressed most into the smoking epidemic: high-income North America, high-income Europe and high-income Oceania. We examined changes in smoking-attributable mortality fractions as estimated by the Preston-Glei-Wilmoth method by age (ages 50–85) across birth cohorts 1870–1965. We used these to trace sex differences with and without smoking-attributable mortality in period life expectancy between ages 50 and 85. Results: In all three high-income regions, smoking explained up to 50% of sex differences in period life expectancy between ages 50 and 85 over the study period. These sex differences have declined since at least 1980, driven by smoking-attributable mortality, which tended to decline in males and increase in females overall. Thus, there was a convergence between sexes across recent cohorts. While smoking-attributable mortality was still increasing for older female cohorts, it was declining for females in the more recent cohorts in the US and Europe, as well as for males in all three regions. Conclusions: The smoking epidemic contributed substantially to the male-female survival gap and to the recent narrowing of that gap in high-income North America, high-income Europe and high-income Oceania. The precipitous decline in smoking-attributable mortality in recent cohorts bodes somewhat hopeful. Yet, smoking-attributable mortality remains high, and therefore cause for concern.

Aims: During the first wave of the COVID-19 pandemic, Sweden registered a high level of excess deaths. Non-pharmaceutical interventions adopted by Sweden have been milder compared to those implemented in Denmark. Moreover, Sweden might have started the pandemic with a large proportion of vulnerable elderly with a high mortality risk. This study aimed to clarify whether excess mortality in Sweden can be explained by a large stock of ‘dry tinder’ instead of being attributed to faulty lockdown policies. Methods: We analysed weekly death counts in Sweden and Denmark from July 2007 to June 2020. We used a novel method for short-term mortality forecasting to estimate expected and excess deaths during the first COVID-19 wave in Sweden and Denmark. Results: In the first part of the epiyear 2019–2020, deaths were low in both Sweden and Denmark. In the absence of COVID-19, a relatively low level of death would be expected for the later part of the epiyear. The registered deaths were, however, way above the upper bound of the prediction interval in Sweden and within the range in Denmark. Conclusions: ‘Dry tinder’ can only account for a modest fraction of excess Swedish mortality. The risk of death during the first COVID-19 wave rose significantly for Swedish women aged >85 but only slightly for Danish women aged >85. The risk discrepancy seems more likely to result from differences between Sweden and Denmark in how care and housing for the elderly are organised, coupled with a less successful Swedish strategy of shielding the elderly.