Too old to have children? Lessons from natural fertility populations.

STUDY QUESTION: Is it possible to construct an age curve denoting the ages above which women are biologically too old to reproduce? SUMMARY ANSWER: We constructed a curve based on the distribution of female age at last birth in natural fertility populations reflecting the ages above which women have become biologically too old to have children. WHAT IS KNOWN ALREADY: The median age at last birth (ALB) for females is ∼40-41 years of age across a range of natural fertility populations. This suggests that there is a fairly universal pattern of age-related fertility decline. However, little is known about the distribution of female ALB and in the present era of modern birth control, it is impossible to assess the age-specific distribution of ALB. Reliable information is lacking that could benefit couples who envisage delaying childbearing. STUDY DESIGN, SIZE, DURATION: This study is a review of high-quality historical data sets of natural fertility populations in which the distributions of female age at last birth were analysed. The studies selected used a retrospective cohort design where women were followed as they age through their reproductive years. PARTICIPANTS/MATERIALS, SETTING, METHODS: Using a common set of eligibility criteria, large data files of natural fertility populations were prepared such that the analysis could be performed in parallel across all populations. Data on the ALB and confounding variables are presented as box and whisker plots denoting the 5th, 25th, 50th, 75th and 95th percentile distribution of the age at last birth for each population. The analysis includes the estimation of Kaplan-Meier curves for age at last birth of each population. The hazard curve for ALB was obtained by plotting the smoothed hazard curve of each population and taking the lowest hazard within a time period of at least 5 years. This lowest hazard curve was then transformed into a cumulative distribution function representing the composite curve of the end of biological fertility. This curve was based on the data from three of the six populations, having the lowest hazards of end of fertility. MAIN RESULTS AND THE ROLE OF CHANCE: We selected six natural fertility populations comprising 58 051 eligible women. While these populations represent different historical time periods, the distribution of the ages at last birth is remarkably similar. The curve denoting the end of fertility indicates that <3% of women had their last birth at age 20 years meaning that almost 98% were able to have at least one child thereafter. The cumulative curve for the end of fertility slowly increases from 4.5% at age 25 years, 7% at age 30 years, 12% at age 35 years and 20% at age 38 years. Thereafter, it rises rapidly to about 50% at age 41, almost 90% at age 45 years and approaching 100% at age 50 years. LIMITATIONS, REASONS FOR CAUTION: It may be argued that these historical fertility data do not apply to the present time; however, the age-dependent decline in fertility is similar to current populations and is consistent with the pattern seen in women treated by donor insemination. Furthermore, for reproductive ageing, we note that it is unlikely that such a conserved biological process with a high degree of heritability would have changed significantly within a century or two. WIDER IMPLICATIONS OF THE FINDINGS: We argue that the age-specific ALB curve can be used to counsel couples who envisage having children in the future. Our findings challenge the unsubstantiated pessimism regarding the possibility of natural conception after age 35 years. STUDY FUNDING/COMPETING INTEREST(S): No external funding was either sought or obtained for this study. There are no conflicts of interest to be declared.

Feasibility study of adjustment for contamination and non-compliance in a prostate cancer screening trial.

BACKGROUND: The use of PSA as a screening test has become increasingly prevalent in the general population and therefore also in the control arm of the European Randomized study of Screening for Prostate Cancer (ERSPC). We present a feasibility study and impact simulation of a secondary analysis, which imitates a situation where all participants in the study are managed according to their random assignment. METHODS: The results of the Rotterdam section of the ERSPC were adjusted for contamination and non-compliance according to Cuzick et al. [Stat Med 1997; 16:1017-1029]. Endpoints of this analysis were simulated reductions in prostate cancer mortality. RESULTS: Of the men allocated to the screen arm, 27.1% were non-compliant. In the control arm 30.7% had their PSA-level measured by a general practitioner (GP) (i.e., contamination). For a scenario in which the intention-to-screen analysis was assumed to give a decrease in the mortality in the men randomized to screening of 6.7%, the secondary analysis resulted in a decrease of 16.1% for those actually screened. CONCLUSION: Although the definition of contamination as "PSA ever tested" gives an indication of the proportion of contamination, it will be important to differentiate the screening use of PSA from its diagnostic use. For the rest, adjustment for non-compliance and contamination was shown to be feasible in this prostate cancer screening trial. It can therefore be used to carry out a secondary analysis on the definitive outcome of the ERSPC and will provide accurate information for those men who are in fact screened.

Quality of life after hip fracture: a comparison of four health status measures in 208 patients.

OBJECTIVES: We compared four health status measures for the evaluation of quality of life after hip fracture. METHODS: Two hundred and eight elderly hip fracture patients were followed up to 4 months after hospital admission. We used two interviewer-administered instruments (the Rehabilitation Activities Profile (RAP) and the Barthel Index (BI)) that focus on functional status, and two self-assessment instruments (the Nottingham Health Profile (NHP) and the COOP/WONCA charts) that additionally include psychological and social health domains. The score distribution, internal consistency, construct validity, and sensitivity to change were investigated. RESULTS: At 4 months only 18% of surviving patients had reached the same level of functioning as before the fracture and, compared with reference values, lower scores of health status were found in the areas of physical mobility and emotional reactions. The number of comorbidities at hospital admission was the most important prognostic factor for recovery of health status at 4 months. The RAP and the BI both performed well in the assessment of functional status in regard to score distribution, internal consistency and construct validity. In contrast to the BI, the RAP also assessed instrumental activities of daily living and perceived problems with existing disabilities. The generic health status measures produced no added value in the assessment of functional status. The NHP covered a wider range of psychological health dimensions (emotion, pain, energy, and sleep) and had better psychometric properties than COOP/WONCA. None of the four instruments performed well in assessing social functioning. CONCLUSIONS: To assess health status after hip fracture, we recommend the RAP for functional status and the NHP for changes in emotion, pain, and energy. These instruments detected poor recovery in functional and emotional status at 4 months after fracture.