Is age at menopause decreasing? - The consequences of not completing the generational cohort.

BACKGROUND: Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to be related to an increased risk of breast cancer. Using data from breast cancer screening programs to study the temporal trend of age at menopause is difficult since especially younger women in the same generational cohort have often not yet reached menopause. Deleting these younger women in a breast cancer risk analyses may bias the results. The aim of this study is therefore to recover missing menopause ages as a covariate by comparing methods for handling missing data. Additionally, the study makes a contribution to understanding the evolution of age at menopause for several generations born in Portugal between 1920 and 1970. METHODS: Data from a breast cancer screening program in Portugal including 278,282 women aged 45-69 and collected between 1990 and 2010 are used to compare two approaches of imputing age at menopause: (i) a multiple imputation methodology based on a truncated distribution but ignoring the mechanism of missingness; (ii) a copula-based multiple imputation method that simultaneously handles the age at menopause and the missing mechanism. The linear predictors considered in both cases have a semiparametric additive structure accommodating linear and non-linear effects defined via splines or Markov random fields smoothers in the case of spatial variables. RESULTS: Both imputation methods unveiled an increasing trend of age at menopause when viewed as a function of the birth year for the youngest generation. This trend is hidden if we model only women with an observed age at menopause. CONCLUSION: When studying age at menopause, missing ages must be recovered with an adequate procedure for incomplete data. Imputing these missing ages avoids excluding the younger generation cohort of the screening program in breast cancer risk analyses and hence reduces the bias stemming from this exclusion. In addition, imputing the not yet observed ages of menopause for mostly younger women is also crucial when studying the time trend of age at menopause otherwise the analysis will be biased.

Treatment effects beyond the mean using distributional regression: Methods and guidance.

This paper introduces distributional regression also known as generalized additive models for location, scale and shape (GAMLSS) as a modeling framework for analyzing treatment effects beyond the mean. In contrast to mean regression models, GAMLSS relate each distributional parameter to covariates. Therefore, they can be used to model the treatment effect not only on the mean but on the whole conditional distribution. Since they encompass a wide range of different distributions, GAMLSS provide a flexible framework for modeling non-normal outcomes in which additionally nonlinear and spatial effects can easily be incorporated. We elaborate on the combination of GAMLSS with program evaluation methods including randomized controlled trials, panel data techniques, difference in differences, instrumental variables, and regression discontinuity design. We provide practical guidance on the usage of GAMLSS by reanalyzing data from the Mexican Progresa program. Contrary to expectations, no significant effects of a cash transfer on the conditional consumption inequality level between treatment and control group are found.

Assessing the risk for major injuries in equestrian sports.

BACKGROUND/AIM: Horse riding is a popular sport, which bears the risk of serious injuries. This study aims to assess whether individual factors influence the risk to sustain major injuries. METHODS: Retrospective data were collected from all equine-related accidents at a German Level I Trauma Centre between 2004 and 2014. Logistic regression was used to identify the risk factors for major injures. RESULTS: 770 patients were included (87.9% females). Falling off the horse (67.7%) and being kicked by the horse (16.5%) were the two main injury mechanisms. Men and individuals of higher age showed higher odds for all tested parameters of serious injury. Patients falling off a horse had higher odds for being treated as inpatients, whereas patients who were kicked had higher odds for a surgical therapy (OR 1.7) and intensive care unit/intermediate care unit (ICU/IMC) treatment (OR 1.2). The head was the body region most often injured (32.6%) and operated (32.9%). Patients with head injuries had the highest odds for being hospitalised (OR 6.13). Head or trunk injuries lead to the highest odds for an ICU/IMC treatment (head: OR 4.37; trunk: OR 2.47). Upper and lower limb injuries showed the highest odds for a surgical therapy (upper limb: OR 2.61; lower limb: OR 1.7). CONCLUSION: Risk prevention programmes should include older individuals and males as target groups. Thus a rethinking of the overall risk assessment is necessary. Not only horseback riding itself, but also handling a horse bears a relevant risk for major injuries. Serious head injures remain frequent, serious and an important issue to be handled in equestrians sports.